Behavioral research for pandemic policymaking: Focusing
on activity-travel behavior and social contact decisions
under the impacts of COVID-19

D200978
HONGXIANG DINGA Dissertation Submitted to
the Graduate School of Advanced Science and Engineeringof Hiroshima University in Partial Fulfillmentof the Requirement for the Degree ofDoctor of Philosophy in Engineering
March 2023

ABSTRACT
Research background and motivation
The Coronavirus disease (COVID-19) is an infectious disease, predominantly transmitted from person
to person. As of January 2023, globally, more than 670 million infections and more than 6.7 million
deaths were observed, without any clear sign of stopping in the growth trend. The risk of COVID-19 isstill a threat to human beings. To fight against COVID-19, various pharmaceutical and nonpharmaceutical measures have been taken, such as lockdown, restrictions of activity participation andtrip making, physical distancing, mask wearing, and hand disinfection. These measures played theirrightful roles in pandemic control on one hand, while they also brought about various negative impacts
on the other. After suffering from COVID-19 for nearly three years, it becomes more and more
important how to balance the control of the COVID-19 pandemic and the maintenance of economic
activities and daily life activities. However, there is a serious lack of scientifically sound evidence forsupporting pandemic policymaking, especially based on understanding of people’s decision-making
mechanisms related to those behaviors that are closely related to the virus transmission. For addressing
the above research gap, this study focuses on people’s daily activity participation and trip making aswell as social contact (referring to offline social contact) before and during the COVID-19 pandemic.

Research questions and objectives
This dissertation aims to provide a comprehensive investigation of people’s behavioral decisions on
daily activity participation and trip making as well as social contact under the impacts of COVID-19,
for providing behavioral insights into pandemic policymaking. The investigation is done from various
angles by answering the following different but connected research questions related to individual-level
decision-making mechanisms.
First, COVID-19 has brought about various changes in people’s behaviors; however, the following
question has remained under-explored.
Q-1 How do people’s daily activity-travel and social contact behaviors change over time byconsidering the risk of COVID-19 and the influences of psychological factors?
Second, trip making is one of the virus transmission channels; however, the following questions
have not been investigated by existing studies in a satisfactory way.
Q-2 How are travel mode choices associated with risk perception in different spatial scales,
trust in stakeholders’ policymaking capabilities, and cultural risk factors?
iQ-3 How did people prepare for such a pandemic like COVID-19 and how the preparedness
affects travel mode choices under the impacts of COVID-19?
Q-4 How the performing factors of travel behavior (e.g., travel time, travel companion, travel
habit) affect people’s travel mode choice during the COVID-19 pandemic with individuals’
taste variations?
Third, individuals’ social contacts with others during activity participation and travelling are oneof the main channels of virus transmission; however, the following questions have not been well
examined.
Q-5 How are changes in the number of social contacts heterogeneous depending on
demographic characteristics and attributes of activity-travel behavior?
Q-6 How do people behave social contact in different contact modes, including not only
physical or direct contact but also non-physical or indirect contact?
Q-7 Whether and how do people make joint decisions on choices of social contact modes andthe number of contacted persons during the COVID-19 pandemic, and what are theroles/influences of various factors, including demographic attributes, psychological factors,
and attributes of activity-travel behavior?
Fourth, decisions on trip making behavior and social contact may vary across activity settings;
however, such setting-sensitivity analyses have been neglected in existing studies, leading to thefollowing unresolved research question.
Q-8 How do travel mode choice and social contact decisions differ across activity settings,
especially related to leisure?

Methodology
Large-scale nationwide questionnaire surveys with a total of 9,888 respondents were implemented insix developed countries (Australia, Canada, Japan, New Zealand, United States, and United Kingdom),followed by extensive descriptive analyses. And models with temporal dynamics and choice decisionmaking mechanisms were built, where respondents were recruited from the whole territory of eachcountry by reflecting population distributions in terms of age, gender and region.
Two types of questionnaire surveys were conducted online. The first type is a life-oriented panel
survey, where respondents in Japan reported their various daily life behaviors between April and
September 2020, month by month, in a retrospective way, where several ten behaviors were jointly
investigated. In this survey, 2,643 respondents provided valid data about both behaviors and potential
influential factors (both objective and subjective factors), in comparison with the behaviors before the
COVID-19 pandemic. The second type is a comparative questionnaire survey about both activity-traveland social contact by comparing the current pandemic period and influenza seasons before the pandemic,iiwhere social contact was investigated with respect to both activity participation and trip making. It was
carried out in all the six developed countries (Australia, Canada, Japan, New Zealand, United States,
and United Kingdom) form March to May 2021 and collected valid data from 7,245 respondents(Australia: 1125, Canada: 1176, Japan: 1169, New Zealand: 1193, United Kingdom: 1305, United States:
1297). Question items include social contact information (e.g., contact modes, number of contacted
persons), activity participation attributes (e.g., frequency, duration, travel modes), and various
psychological factors when doing different daily activities and using different transport vehicles before(influenza season in 2019) and during (March 2020 to May 2021) the pandemic.
Models include, (1) a dynamic structural equation model (DSEM) that is developed to investigate
the relationship between the latent psychological/cultural variables and individuals’ life behavior
changes over time, (2) a mixed hybrid choice model (XHCM) with correlated latent variables isdeveloped to reveal individuals’ travel mode choice behaviors with unobserved heterogeneities, and (3)a copula-based model is applied to estimate the joint distribution and dependency between individuals’
contact modes (based on multinomial logit model) and the number of contacted persons (based on anordered logit model).

Main findings
First (answer Q-1), people’s accumulated behavior changes in the past can affect their psychologicalfactors (e.g., the reliability of information sources, the risk perceptions, the attitudes toward COVID19 policymaking capability, the attitudes toward PASS-LASTING based policies) and then further
influence the most recent behavior changes. People’s behavior changes are obviously represented by
avoiding social contacts with others. And there is a strong relationship between different psychologicalfactors during the process that accumulated past behavior changes affect the recent behavior changes
dynamically.
Second (answer Q-2, Q-3, Q-4, Q-8), people are less likely to change travel mode choices forcommuting than other travel purposes. The impacts of latent psychological/cultural factors on travel
mode choice show an obvious diversity across different travel purposes in direct and indirect way
simultaneously. Risk perception is found to be more remarkable at larger spatial scales than inside
crowded public transport vehicles. The effect of the cultural orientations on travel mode choices can bedivided into two dimensions: hierarchism-egalitarianism and individualism-fatalism because of thehigher similarities in each pair. The preparedness focusing on risk-concerned travel habits formed
before the COVID-19 pandemic shows a significant impact on travel mode choices, but the influences
are not consistent across travel purposes.
Third (answer Q-5, Q-6, Q-8), there is an obvious heterogeneity of the number of social contacts
and its changes across the targeted attributes and contact settings, the pandemic policymaking could
focus on the protection of specific group with higher danger level of social contacts. People’s contact
modes and the taken protecting measures are associated with the number of social contacts during theiii

pandemic and show different features across the contact settings. On one hand, most both physical andnon-physical contacts occurred in work/study setting and party setting, while the most non-physicalcontacts contact (with the physical presence of others) occurred in shopping setting and public transportsetting. On the other hand, protecting measures are well implemented for work/study setting andshopping settings during the pandemic.
Fourth (answer Q-6, Q-7, Q-8), the joint estimation of people’s different indexes of social contact
behaviors is possible via the copula approach. A significant joint correlation between the non-physicalcontact mode and the number of social contacts is found indicating that people trend to simultaneously
increase the number of contacted persons and the probability of having non-physical contacts withothers under the influence of common unobserved factors during the COVID-19 pandemic. Not only
individuals’ demographic attributes but also the psychological factors and activity participation
attributes have a diversified influence on people’s joint choice of social contact behaviors. Having
contacts with others doesn’t means people would consequentially contact more people. The additional
psychological variables in copula analysis highlight that people’ risk-related psychological factors
surely have the significant impact on the a certain behavior change too. And it is reasonable to include
risk perception with different spatial scales in a certain behavior analysis like social contact because of
the obvious difference among them. The joint analysis of social contact also indicates that the differenceof behavioral mechanisms for different contact settings can be reflected not only in the difference of
positive-negative effects but also in different sensitivity to influencing factors.

Contributions
The contributions of this study are summarized in academic level and practical level respectively.
(1) Academic contributions
Academic contribution on research framework:
1) A seamless framework of individuals’ pandemic activity-travel behaviors mechanisms withsocial contacts is built.
Academic contributions on research contents based on data:
1) The cultural risk factors basing on the cultural theory of risk are the first time to be applied forthe analysis of individuals’ travel mode choices.
2) The leisure related travel purposes/contact settings for choice analysis are specifically classified
into eating out, physical exercise, party, and cultural leisure in the comparative survey data of activitytravel and social contact before-during COVID-19 of this study.

iv3) The heterogeneities of the changes in the number of social contacts are revealed not only acrosspersonal attributes but also across individuals’ participation attributes of activity-travel behaviors fordifferent contact settings.
4) In addition to the close contact (direct physical contacts or close conversation) behavior,individuals’ behavioral mechanisms for other contact modes (e.g., only non-physical contacts without
direct physical contacts or close conversation) are also investigated in a comprehensive perspective.
Academic contributions on modeling approach:1) The dynamic associations between individuals’ behavior changes over time and psychologicalfactors is the first time to be quantified by a developed dynamic structural equation model (DSEM).
2) Individuals’ taste variations related to preparedness (travel mode choices corresponding todifferent risk levels) and key travel attributes are incorporated into the mixed hybrid choice model withcorrelated latent variables for travel mode choice analysis.
3) The copula-based approach is first time to be used for the exploration of the joint behavioral
mechanisms of different indexes for social contacts under the effect of personal attributes, activity
participation attributes and psychological factors.
(2) Practical contributions
1) The complementary relationship between policymaking for different kinds of activity-travel andsocial contact is revealed.
2) A comprehensive perspective on the dynamic effect between people’s recent behaviors and the
behaviors in the past across the psychological factors is provided indicating a necessity of the timely
adjustment for pandemic policymaking.
3) The important impact of the risk perception in different spatial scales and the attitudes topolicymaking capacity on the change of the activity-travel behaviors with social contacts is highlighted
for the pandemic policymaking.
4) The logical relations between different psychological factors related to the pandemic
policymaking across multiple daily behavior changes and across travel mode choice behaviors are
confirmed.
5) The pandemic policymaking approach that could influence different aspects of social contact
behaviors simultaneously has been proven to be necessary and possible in this study. And thepolicymaking for activity-travel behavior with social contact which is tailored to specific situations is
recommended.

v

ACKNOWLEDGEMENTS
3 years in Japan passed in the blink of an eye, I'm about to finish my PhD and feel like a dream. Here,I would like to express my heartfelt thanks to all those who have helped me.
My sincere and hearty thanks and appreciations go firstly to my main supervisor, Prof. Junyi Zhang
and his wife Ziying Jiang. Getting PhD is a road full of thorns, it would have been hard for me to stick
it out without their support in life and research. It is my great fortune to study under the supervision ofsuch kinds of Prof. All things are difficult before they are easy, I'm going to take the next step in mylife, but I believe that the Prof. Zhang's serious academic attitude and life attitude will continue toencourage me to face the challenges in my future life.
Excellent scholars are the face of excellent university. I would like to extremely grateful to eachofthemembersofmydissertationcommittee.

Profs. Akimasa

Fujiwara, Tao

Feng, Masaaki Fuse, Makoto Chikaraishi, Saori Kashima have provided me extensive professional
guidance on the revision of my dissertation via their rich research experience. Their kind help andguidance opened my eyes to see the endless possibilities of learning in life.
A friend in need is a friend indeed, I am so lucky to make lots of true friends in these short three
years. Hence, I would like to show my sincere gratitude to Rui Liu, Wenhao Li, Wenhui Huang,
Hongyang Du, Lin Liao, Baoxin Zhai, Hyewon Namgung, Fuyo (Jenny) Yamamoto, Baraa Alhakim fortheir generous tolerance and help to me during the worst period of my life. Life without a friend is death
without a witness, I am also extremely grateful to the new friend I met in 2021-2022 in Mobilities and
Urban Policy Lab (Yueou Li et al.), Urban and Data Science Lab (Mengxia Li et al.), Transport
Engineering Lab (Iqbal Muhammad Yousaf et al.), Infrastructure Planning and Urban Risk Management
Lab (Maya Safira et al.). And I must show my apologies for can’t writing all the names at here due tothe word limitation.
Although I didn't get much support from my family during my PhD, I am still grateful to them formaking me become a mature man. As the saying goes, “He who makes constant complaint gets littlecompassion”. Thank you for making me comprehend what are “The Unbearable Lightness of Being” inlife.
Finally, adversity reveals genius, prosperity conceals it. I am not the genius, but you are the blessed
person. I hope the people I'm grateful for can live life without regrets, with my best wishes.
HONGXIANG DING (丁 泓翔)
March 2023
Hiroshima, Japan

vi

CONTENTS

ABSTRACT ............................................................................................................................................. i
ACKNOWLEDGEMENTS ................................................................................................................... vi
CONTENTS.......................................................................................................................................... vii
LIST OF TABLES ................................................................................................................................. xi
LIST OF FIGURES .............................................................................................................................. xii
CHAPTER 1 Introduction....................................................................................................................... 1
1.1 Background and motivation .......................................................................................................... 1
1.2 Research questions and objectives ................................................................................................ 2
1.3 Research framework ..................................................................................................................... 4
1.4 Contributions of dissertation ......................................................................................................... 6
1.5 Outline of dissertation ................................................................................................................... 7
CHAPTER 2 Literature review ............................................................................................................. 10
2.1 Behavioral and psychological adaptations to COVID-19 for multiple behaviors ...................... 10
2.1.1 Activity-travel behavior changes during the COVID-19 pandemic..................................... 10
2.1.2 The roles of information reliability in affecting activity-travel behavior changes............... 11
2.1.3 The role of risk perceptions in affecting activity-travel behavior changes .......................... 12
2.1.4 The role of attitudes toward policymaking in affecting activity-travel behavior changes ... 13
2.1.5 PASS-LASTING based policymaking................................................................................. 13
2.2 Travel mode choice behavior during the COVID-19 pandemic ................................................. 14
2.2.1 Travel mode choices under the impacts of COVID-19 ........................................................ 14
2.2.2 Influences of risk perception and trust in policymakers on travel mode choice .................. 16
2.2.3 Influences of cultural risk factors on transportation field .................................................... 17
2.2.4 Influences of preparedness on under-pandemic travel mode choices .................................. 18
2.2.5 Applications of hybrid choice models in the COVID-19 context ........................................ 19vii

2.3 Social contact behaviors during the COVID-19 pandemic ......................................................... 20
2.3.1 Ignorance of indirect social contact ..................................................................................... 20
2.3.2 Lack of the representation of joint social contact decisions................................................. 21
2.3.3 Lack of modeling approaches with the dependence of social contact behaviors ................. 22
2.4 Summary ..................................................................................................................................... 23
CHAPTER 3 Pandemic-responsive methodologies .............................................................................. 25
3.1 Nationwide behavioral surveys ................................................................................................... 26
3.1.1 Life-oriented panel survey of behavioral and psychological adaptations to COVID-19 ..... 26
3.1.2 Comparative survey of activity-travel and social contact before and during the COVID-19
pandemic ....................................................................................................................................... 28
3.2 Modeling application .................................................................................................................. 32
3.2.1 Modeling of temporal dynamics of behavioral and psychological adaptations to COVID-19:
Dynamic structural equation model (DSEM) ............................................................................... 32
3.2.2 Hybrid choice modeling of heterogeneous preparedness and correlated cultural andpsychological factors in response to COVID-19: Mixed hybrid choice model (XHCM)............. 32
3.2.3 Dependence modeling of multi-faceted social contact decisions under the impacts of
COVID-19: Copula model ............................................................................................................ 33
CHAPTER 4 Capturing temporal dynamics of behavioral and psychological adaptations to COVID19 .......................................................................................................................................................... 34
4.1 Introduction ................................................................................................................................. 34
4.2 Data and descriptive analysis ...................................................................................................... 35
4.3 Methodology and framework ...................................................................................................... 43
4.4 Result and discussion .................................................................................................................. 44
4.5 Conclusion .................................................................................................................................. 50
CHAPTER 5 Influences of preparedness and correlated cultural and psychological factors on choice
behaviors in response to COVID-19: A case study on travel mode choices......................................... 52
5.1 Introduction ................................................................................................................................. 52
5.2 Data and descriptive analysis ...................................................................................................... 53
5.3 Methodology and framework ...................................................................................................... 64
5.4 Result and discussion .................................................................................................................. 68
viii

5.5 Conclusion .................................................................................................................................. 79
CHAPTER 6 Differences of social contacts by activity settings before and during the COVID-19
pandemic and associational analysis ..................................................................................................... 81
6.1 Introduction ................................................................................................................................. 81
6.2 Data ............................................................................................................................................. 81
6.3 Result and discussion .................................................................................................................. 82
6.3.1 Social contacts across individual and household attributes and its changes before andduring the COVID-19 pandemic ................................................................................................... 84
6.3.2 Social contacts across activity participation attributes and its changes before and during the
COVID-19 pandemic .................................................................................................................... 88
6.3.3 Social contacts across contact modes and its changes before and during the COVID-19
pandemic ....................................................................................................................................... 90
6.4 Conclusion .................................................................................................................................. 94
CHAPTER 7 Investigation of multi-faceted social contact decisions under the impacts of COVID-19:
Joint analysis of contact mode choices and the number of contacted persons ...................................... 96
7.1 Introduction ................................................................................................................................. 96
7.2 Survey and Data .......................................................................................................................... 98
7.3 Features of Key Variables ......................................................................................................... 103
7.4 Integrated copula-based social contact decision modeling ....................................................... 113
7.5 Results and discussion .............................................................................................................. 120
7.6 Conclusion ................................................................................................................................ 127
CHAPTER 8 Conclusions................................................................................................................... 130
8.1 Introduction ............................................................................................................................... 130
8.2 Conclusion ................................................................................................................................ 130
8.3 Policy implication ..................................................................................................................... 133
8.4 Future research challenges ........................................................................................................ 138
REFERENCES ................................................................................................................................... 140
PUBLICATIONS ................................................................................................................................ 157
APPENDICES .................................................................................................................................... 158

ix

Appendix 1: Questionnaire of life-oriented panel survey of behavioral and psychological
adaptations to COVID-19 in Japanese ............................................................................................ 158
Appendix 2: Questionnaire of comparative survey of activity-travel and social contact beforeduring COVID-19 in English .......................................................................................................... 177
Appendix 3: Questionnaire of comparative survey of activity-travel and social contact beforeduring COVID-19 in Japanese ........................................................................................................ 204
Appendix 4: Statistics of social contacts for Work/Study, Shopping, Eating out, Physical exercise
setting before and during the COVID-19 pandemic ....................................................................... 234
Appendix 5: Statistics of social contacts for Party, Cultural leisure, Medical activities, Public
transport setting before and during the COVID-19 pandemic ........................................................ 238

x

LIST OF TABLES
Table 3. 1. Share differences of respondents in the survey and the population by regions ................................... 27
Table 3. 2. Socio-demographic attributes of the survey respondents .................................................................... 28
Table 3. 3. The shares of respondents and population by regions ......................................................................... 30
Table 3. 4. Distribution of attributes for all respondents in six developed countries ............................................ 31
Table 4. 1. Evaluation of reliability of COVID-19 information sources............................................................... 37
Table 4. 2. Risk perceptions about the virus infection .......................................................................................... 37
Table 4. 3. Attitudes toward COVID-19 policymaking capability ........................................................................ 38
Table 4. 4. Attitudes toward PASS and LASTING based policy measures .......................................................... 39
Table 4. 5. Standardized direct effects between latent variables ........................................................................... 45
Table 4. 6. Standardized total effects between latent variables ............................................................................. 46
Table 4. 7. Standardized effects between latent variables and observed variables ............................................... 47
Table 5. 1. The sample distributions of gender, age, and number of household members by travel purposes ...... 54
Table 5. 2. Changes in travel mode choices and trip reduction before and during the COVID-19 pandemic ...... 56
Table 5. 3. The distribution of travel time during the COVID-19 pandemic ........................................................ 63
Table 5. 4. Estimation results of mixed hybrid choice model: Utility equation .................................................... 68
Table 5. 5. Estimation results of mixed hybrid choice model: Measurement equations ....................................... 70
Table 5. 6. Estimation results of mixed hybrid choice model: Structural equations ............................................. 70
Table 5. 7. Total effects of trust and cultural risk factors on the utility of travel mode choice ............................. 74
Table 5. 8. Interval estimates (95% confidence level) of the parameters of travel attributes and pre-pandemic
preparedness ................................................................................................................................................. 78
Table 7. 1. Differences of regional distributions between samples and whole populations ................................ 100
Table 7. 2. Distribution of individual attributes by country ................................................................................ 101
Table 7. 3. Individual attributes by social contact setting ................................................................................... 102
Table 7. 4. The model fit for six copula-based models ....................................................................................... 121
Table 7. 5. Estimation results of copula-based social contact behavior model ................................................... 121

xi

LIST OF FIGURES
Figure 1. 1. Newly reported cases of the COVID-19 across the world .................................................................. 1
Figure 1. 2. Background and motivation of this study ............................................................................................ 2
Figure 1. 3. Research framework ............................................................................................................................ 4
Figure 1. 4. Outline of dissertation ......................................................................................................................... 9
Figure 3. 1. Targeted survey period of behaviors in two surveys ......................................................................... 25
Figure 4. 1. Changes in behaviors from April to September 2020 ........................................................................ 42
Figure 4. 2. Conceptual framework about cause-effect relationships ................................................................... 43
Figure 5. 1. Choices of travel modes by travel purpose during the COVID-19 pandemic ................................... 55
Figure 5. 2. The distributions of risk perception scores ........................................................................................ 57
Figure 5. 3. The distributions of trust scores ........................................................................................................ 59
Figure 5. 4. The distributions of cultural risk factors by travel purpose ............................................................... 61
Figure 5. 5. The distributions of risk-concerned travel habits (or pre-pandemic preparedness) by travel purpose...................................................................................................................................................................... 62
Figure 5. 6. The distributions of travel companions by travel purpose during the COVID-19 pandemic ............ 64
Figure 5. 7. A mixed hierarchical hybrid choice model (XHCM) ........................................................................ 65
Figure 6. 1. The changes of total number of social contacts between pre-pandemic and during-pandemic ......... 92
Figure 6. 2. The changes of the mean number of social contacts between pre-pandemic and during-pandemic.. 93
Figure 7. 1. Distributions of social contact modes by activity ............................................................................ 104
Figure 7. 2. Numbers of persons contacted by activity ....................................................................................... 104
Figure 7. 3. Distributions of numbers of persons contacted under different modes by activity.......................... 104
Figure 7. 4. Perceptions of 3Cs at activity locations ........................................................................................... 105
Figure 7. 5. Perceived safety during activity participation ................................................................................. 106
Figure 7. 6 Protective measures taken during activity participation ................................................................... 106
Figure 7. 7. Self-efficiency and others’ efficiency .............................................................................................. 107
Figure 7. 8. Perceptions of activity-related necessity and punishment ............................................................... 108
Figure 7. 9. Activity frequencies ......................................................................................................................... 108
Figure 7. 10. Travel distance from home to activity locations ............................................................................ 109
Figure 7. 11. Shares of activity participation with acquaintances ....................................................................... 110
Figure 7. 12. Risk perception and reliability of risk information ........................................................................ 111
Figure 7. 13. Triggers for behavioral changes .................................................................................................... 112
Figure 7. 14. Acceptance to change the duration/timing for activity participation ............................................. 113

xii

Chapter 1 Introduction

CHAPTER 1 Introduction

1.1 Background and motivation
The Coronavirus disease (COVID-19) is an infectious disease caused by the SARS-CoV-2 virus. The
virus of COVID-19 can spread from an infected person’s mouth or nose in small liquid particles when
they cough, sneeze, speak, sing or breathe. These particles range from larger respiratory droplets tosmaller aerosols. Anyone can get sick with COVID-19 and become seriously ill or die at any age1.
As of 2022.11.21, the global cumulative number of the COVID-19 cases has exceeded 635 million.
And more than 6 million of people have lost their life due to the impact of the SARS-CoV-2 virus. As
seen in Figure 1.1, even though the number of new daily cases started to decrease dramatically acrossthe world since the February 2022, more than 2 million infection cases are newly reported in the world
during the last week before 2022.11.212.

Figure 1. 1. Newly reported cases of the COVID-19 across the world (Source: WHO COVID intel
database https://worldhealthorg.shinyapps.io/covid/)

In addition to vaccination, the governments across the world have implemented various behavioral
interventions and policies from the strictest lockdowns (with penalties for violations) to the soft

WHO: Health topics/Coronavirus disease (COVID-19) https://www.who.int/healthtopics/coronavirus#tab=tab_1
2 WHO Coronavirus (COVID-19) Dashboard: https://covid19.who.int/data
11Chapter 1 Introduction

proposing changes in behaviors to control the current COVID-19 pandemic. The compulsory social
distancing policy (e.g., lockdown) has been conducted by some governments in the initial phase of the
COVID-19 pandemic and been proven to be effective for controlling the pandemic. However, it mightlead to a serious damage to the economies and people’s well-being. The COVID-19 pandemic is stillrisky to people’ safety but it is difficult to sustain such kind of strict behavioral interventions and polices
due to people’s policy fatigue after the COVID-19 pandemic has lasted more than two years.
Under this background, it is crucial to find a sensible pandemic policymaking approach for
prompting people to make proposing behavior changes. Exploring individual’s behavioral mechanisms
contributes to the reasonable pandemic policymaking which would protect people from the infection
effectively by the soft non-pharmaceutical interventions (NPIs) instead of the strict lockdown.
Meanwhile, the transmission of the COVID-19 pandemic mainly relies on individual’s (offline) social
contacts with others in daily life, hence, to explore individual’s behavioral mechanisms during the
pandemic, a seamless behavioral study focusing on people’s activity-travel behaviors with social
contacts in the process of travels and daily activity is needed.

The risk of the
COVID-19 on
people s safety

Need
Unsustainability ofstrict lockdown policy

Sensible pandemic
policymaking

Based onKnowledge ofindividual s behavioral
mechanism s

Focus onExploring behaviors in
the process of travel withsocial contacts

Refine the behavioral mechanisms
Transmission of virus
via social contact indaily life

Exploring behaviors in
the process of activity
with social contacts

This study
Seamless behavioral
study on activity-travel
behaviors with social
contacts forpolicymaking

Figure 1. 2. Background and motivation of this study

1.2 Research questions and objectives
This dissertation aims to investigate individuals’ behavioral mechanism of activity-travel behavior andsocial contact (referring to offline social contact) under the impacts of COVID-19 in the case of six
developed countries, and to provide the scientific evidence for further pandemic policymaking, hoping
improving the efficiency of pandemic policymaking by decreasing the risk of infection and prompting
people to follow the policy. The investigation is done from various angles by answering the following
2Chapter 1 Introduction

different but connected research questions related to individual-level decision-making mechanisms.
First, COVID-19 has brought about various changes in people’s behaviors; however, the following
question has remained under-explored.
Q-1 How do people’s daily activity-travel and social contact behaviors change over time byconsidering the risk of COVID-19 and the influences of psychological factors?
Second, trip making is one of the virus transmission channels; however, the following questions
have not been investigated by existing studies in a satisfactory way.
Q-2 How are travel mode choices associated with risk perception in different spatial scales,
trust in stakeholders’ policymaking capabilities, and cultural risk factors?
Q-3 How did people prepare for such a pandemic like COVID-19 and how the preparedness
affects travel mode choices under the impacts of COVID-19?
Q-4 How the performing factors of travel behavior (e.g., travel time, travel companion, travel
habit) affect people’s travel mode choice during the COVID-19 pandemic with individuals’
taste variations?
Third, individuals’ social contacts with others during activity participation and travelling are oneof the main channels of virus transmission; however, the following questions have not been well
examined.
Q-5 How are changes in the number of social contacts heterogeneous depending on
demographic characteristics and attributes of activity-travel behavior?
Q-6 How do people behave social contact in different contact modes, including not only
physical or direct contact but also non-physical or indirect contact?
Q-7 Whether and how do people make joint decisions on choices of social contact modes andthe number of contacted persons during the COVID-19 pandemic, and what are theroles/influences of various factors, including demographic attributes, psychological factors,
and attributes of activity-travel behavior?
Fourth, decisions on trip making behavior and social contact may vary across activity settings;
however, such setting-sensitivity analyses have been neglected in existing studies, leading to thefollowing unresolved research question.
Q-8 How do travel mode choice and social contact decisions differ across activity settings,
especially related to leisure?

3

Chapter 1 Introduction

1.3 Research framework
Before the COVID-19 pandemic (2019)

During the COVID-19 pandemic (2020-2021)

timeLife-oriented panel survey of behavioral and psychological adaptations to COVID -19

Comparative survey of activity -travel and social contact before -during COVID -19

Activity-travelsocial contact

Chapterodeling analysis
at the individual level

Behavior 1
Factorcluster 1

Analysisby activity

Behavior 1
Factorcluster 1

Behavior Q
Factorcluster F

Factorcluster F

Chapter

Activity-travelsocial contact

Chapter

Behavior
Travel Q
behavior

Factorcluster 1
Factorcluster F

Behavior 1
Social contact
modes
contacted
persons
Behavior Q

Chapter

Accumulated experience during the pandemic

Pre-pandemic preparedness

ife-oriented approachsychological theories
Hybrid choice models
Copula modeling approach
A approach
A I
approach

Analysis bycontact settings

Policymaking
capability

Attitudes toward
PASS-LASTING policies

Information
reliability

Risk
perception

Trigger to behavioral
changeIndependent
psychological factors
Perceived
Perceived
efficiency
safetyActivity:
Necessity vs Punishment

Cultural risk factors
Hierarchists

Egalitarians

Individualists

Figure 1. 3. Research framework
4

Fatalists

Chapter 1 Introduction

Figure 1.3 shows the conceptual framework of this study. This study has investigated people’s activitytravel and social contact behaviors before (2019) and during (2020-2021) the COVID-19 pandemic
based on a life-oriented panel survey and a comparative survey of behaviors. The solid line in theframework shows the analysis that has been implemented in this study and the dashed line is the analysis
to be implemented for future study. For the methodologies, the life-oriented approach, psychologicaltheories, hybrid choice models, copula modeling approach, PASS approach, and LASTING approach
have been adopted in this dissertation.
A descriptive analysis of social contact across different activity-travel settings is conducted in
Chapter 6. While the modeling analysis at the individual level are conducted in Chapters 4, 5 and 7.
The influencing factors are collected in different cluster, the factors contained not only the COVIDrelated psychological factors (e.g., policymaking capability, attitudes toward PASS-LASTING policies，
information reliability, risk perception, trigger to behavioral change) but also the independent
psychological factors and cultural risk factors. There are some associations between different factor
clusters, meanwhile, these factor clusters could impact the activity-travel and social contact decision
making in direct and indirect ways. People’s behaviors in pre-pandemic are treated as the preparedness
for individuals’ behaviors during the COVID-19 pandemic.
The multiple behaviors analysis of travel behaviors and social contact behaviors in chapter 4 has
examined the impact of people’s psychological factors about the COVID-19 infection risk on their
behavior changes in daily life and verified the dynamic associations between individuals’ accumulated
experience during the pandemic, current behaviors and subjective assessments (i.e., attitudes) of
policymaking. The modeling analysis in Chapter 5 has examined the the impact of psychological factors
on travel mode choice behaviors across different activity settings (or travel purposes) with individuals’
taste variations. While people’s social contact modes and number of contacted persons are investigatedjointly in Chapter 7. The analysis by contact settings in Chapter 7 has shown people’s average
performance of social contact behaviors under the influence of factor clusters.
In addition, the difference of behaviors for different travel purposes/different contact settings are
examined for the travel mode choice behavior and the changes of the number of social contacts directly,
while the joint choice analysis indicates the difference of behaviors across different contact settings by
evaluating the sensitivity of social contact choice to influencing factors.

5Chapter 1 Introduction

1. Contributions of dissertation
The contributions of this study are summarized in academic level and practical level respectively.
(1) Academic contributions
Academic contribution on research framework:
1) A seamless framework of individuals’ pandemic activity-travel behaviors mechanisms withsocial contacts is built.
Academic contributions on research contents based on data:
1) The cultural risk factors basing on the cultural theory of risk are the first time to be applied forthe analysis of individuals’ travel mode choices.
2) The leisure related travel purposes/contact settings for choice analysis are specifically classified
into eating out, physical exercise, party, and cultural leisure in the comparative survey data of activitytravel and social contact before-during COVID-19 of this study.
3) The heterogeneities of the changes in the number of social contacts are revealed not only acrosspersonal attributes but also across individuals’ activity participation attributes for different contact
settings.
4) In addition to the close contact (direct physical contacts or close conversation) behavior,individuals’ behavioral mechanisms for other contact modes (e.g., only non-physical contacts without
direct physical contacts or close conversation) are also investigated in a comprehensive perspective.
Academic contributions on modeling approach:1) The dynamic associations between individuals’ behavior changes over time and psychologicalfactors are the first time to be quantified by a developed dynamic structural equation model (DSEM).
2) Individuals’ taste variations related to preparedness (travel mode choices corresponding todifferent risk levels) and key travel attributes are incorporated into the mixed hybrid choice model withcorrelated latent variables.
3) The copula-based approach is first time to be used for the exploration of the joint behavioral
mechanisms of different indexes for social contacts under the effect of personal attributes, activity
participation attributes and psychological factors.

6

Chapter 1 Introduction(2) Practical contributions
1) The macroscopic policymaking for multiple behaviors and the microscopic policymaking forsingle behavior are complementary to each other. This study has provided a comprehensive
policymaking approach based on findings of behavioral analysis.
2) The strong dynamic effect between people’s recent behaviors and the behaviors in the past
indicate a necessity of the timely adjustment for pandemic policymaking.
3) Individuals’ psychological factors of their risk perception in different spatial scales and theirattitudes to policymaking capacity play an important role in changing the activity-travel behaviors withsocial contacts. They are often ignored in the policymaking process, while this study highlights theirsignificance with the scientific evidence in the case of six developed countries.
4) This study confirmed the logical relation between different psychological factors related to thepandemic policymaking across daily behavior changes and travel mode choice behaviors. Further
pandemic policymaking could refer to this logical relation.
5) This study has revealed the necessity and possibility of the simultaneous impact of pandemic
policymaking approach on different aspects of social contact behaviors. And the policymaking approach
for activity-travel behavior with social contact which is tailored to specific situations has been provided
in this study.

1. Outline of dissertation
This dissertation consists of eight chapters.
Chapter 1 introduces the background, motivation, research questions, objectives, researchframework, contributions, and outline of this dissertation.
Chapter 2 presents the literature reviews of the focused research contents in this dissertation:
behavioral and psychological adaptations to COVID-19, travel mode choice behavior and social contact
behaviors during the COVID-19 pandemic.
Chapter 3 introduces the survey data and modeling approach that used in the methodologies of thisstudy.
Chapter 4 provides a behavior analysis of activity-travel behavior changes during the pandemic in
Japan. The life-oriented panel data of behavioral and psychological adaptations to COVID-19 collectedfrom April to September 2020 in Japan is used. And a dynamic structural equation model (DSEM) isdeveloped to quantify the dynamic associations between individuals’ reliability of information sources,
risk perceptions, attitudes toward COVID-19 policymaking capability, attitudes toward PASSLASTING based policies and the behavior changes over time.
Chapter 5 provides a behavior analysis of individuals’ travel mode choice behaviors during the7Chapter 1 Introduction

COVID-19 pandemic via the travel data in a comparative survey of activity-travel and social contact
before-during COVID-19 conducted in six developed countries (Australia, United States, New Zealand,
Canada, United Kingdom, Japan) in 2021. The latent impacts of risk perception at different spatial
scales, trust toward governments/medical agencies’ capacity to control the pandemic and the culturalorientations related to risk on individuals’ travel mode choices across different travel purposes arequantified by a mixed hybrid choice model (XHCM). The unobserved heterogeneities related to prepandemic preparedness induced by experiencing influenza (measured by risk-concerned travel habits
formed during influenza seasons before the pandemic) and during-pandemic travel attributes arereflected in the mixed logit form as well.
Chapter 6 shows the descriptive analysis of the changes of the number of social contacts before
and during the COVID-19 pandemic. The social contact data for different contact settings (in activity
locations and public transport vehicle) in the comparative survey of activity-travel and social contact
before-during COVID-19 is used to explore the changes of social contacts in the mentioned six
developed countries. The changes of the number of social contacts are obtained by calculating the total
number of social contacts for all participants and the mean number of social contacts for people whohave maintained their daily social contacts before and during the pandemic. The heterogeneities ofsocial contacts across personal attributes and key activity participation attributes are revealed.
Chapter 7 provides a quantitative behavior analysis of people’s joint social contact behaviors insix developed countries during the pandemic. This chapter used the same social contact data for daily
activities in chapter 6. A joint copula-based model is developed to estimate people’s social contact
behaviors represented by the unordered choice of contact modes and ordered choice of the number ofcontacted persons simultaneously. The impacts of people’s demographic attributes, activity
participation attributes and risk-related psychological factors on social contact behaviors are quantified
in the joint copula-based model. Not only people’s average performance of social contact for daily
activities but also the different sensitivity levels of social contact choice to influencing factors acrossdifferent contact settings are estimated.
Chapter 8 provides the conclusions in this dissertation.

8

Chapter 1 Introduction

Chapter 1 Introduction
Chapter 2 Literature

review
Chapter 3 Researchmethodologies

Chapter Dynamic behavior
changes

Chapter Travel mode
choice behaviors

Chapter Chang of social
contact

Chapteroint social
contact behaviors
Chapter Conclusion

Figure 1. . Outline of dissertation

9

Chapter 2 Literature review

CHAPTER 2 Literature review
This chapter provides the literature review of the pandemic-related activity-travel behavior with social
contacts focused by this study. First, individuals’ behavioral changes for daily activity-travel behaviors
during the pandemic and the corresponding influence of psychological factors on behavioral changes
in previous studies are summarized in section 2.1 to give an overview of the impact of the COVID-19
pandemic on people’s activity-travel behaviors. Then, the literature reviews focusing on people’s travel
mode choice behaviors and social contact behaviors, which are two single aspects of the activity-travel
behaviors, are provided in section 2.2 and section 2.3 respectively. Finally, the research gaps of previous
studies are summarized in section 2.4.

2.1 Behavioral and psychological adaptations to COVID-19 for
multiple behaviors
2.1.1 Activity-travel behavior changes during the COVID-19 pandemic
In order to control the spread of COVID-19, governments in different countries have taken various
countermeasures, such as travel restrictions and social distancing. Abu-Rayash and Dincer (2020)
established a model with four indicators (transport efficiency, technology integration, traffic congestion
rate, and accessibility ratio) to analyze changing trends in global aviation and travel in selected cities
worldwide and found dramatic declines in global mobility caused by the various restrictions imposedby authorities. It was further revealed that the transportation sector was virtually non-existent in somecities at certain periods. Shamshiripour et al. (2020) conducted a panel data-based research on therelationships between travel behaviors and the COVID-19 pandemic. They found that since the outbreakof COVID-19, lots of Americans have cancelled their airline travels, and some tried to shift theirmobility styles from public transport and carsharing to active transport (i.e., cycling and walking) andcars. However, not everyone can work at home or have access to alternative travel modes. If
policymakers effectively restricted the use of public transport, users would have to face a higher risk of
exposure to the virus, but if policymakers closed down public transport, travel or commuting would
become inaccessible for people without a car or bike (Brooks et al., 2020). Hotle et al. (2020) showedthat most people intended to seek safety in life. People who are infected want to avoid spreading thevirus, while people who are not infected want to reduce exposure. Males are less likely to alter their
10Chapter 2 Literature review

travel patterns in response to spreading the virus or increasing exposure. The perception of a high-levelrisk at one's workplace does not significantly reduce travel. Stavrinos et al. (2020) analyzed the driving
behavior changes of adolescents during the pandemic in the USA. They found that driving time per
week and vehicle miles driven (VMD) declined after the COVID-19 restrictions from March 14 to April
22, 2020. The range of driving time and VMD declined by more than one third compared to previous
levels, and this trend was especially remarkable for younger teens, non-minorities, females, nonworking teens, and those with higher prosocial tendencies. Moreover, Katrakazas et al. (2020) observedthat with the decrease in the number of running cars, people prefer to drive faster and make more sudden
accelerations.
Activity-travel behavior changes might have negative impacts on people’s psychological states.
COVID-19 caused a career shock for many people around the world, forcing people to adapt as travel
restrictions made it difficult to commute to jobs and companies are closed (Akkermans et al., 2020),
while isolation at home led to a lack of social interactions and self-development, resulting in stress anddepression. Marroquín et al. (2020) investigated whether the policy of stay-at-home orders andindividuals’ personal distancing behaviors are related to mental problems (e.g. depression, generalized
anxiety disorder (GAD), intrusive thoughts, insomnia, and acute stress). They found that most people
have an increasing GAD and feelings of depression during the period when they had to stay at homeand maintain social distancing. What's worse, the proliferation of online information sources further
instigated negative impacts and as a result, some unusual behaviors (e.g., unusual purchases) occurred(Miri et al., 2020; Laato et al., 2020a). Another study in China showed that every time important
negative news about COVID-19 is released, individuals’ negative emotions will increase (Li et al.,2020). Furthermore, De Vos (2020, 2013) revealed that stay-at-home threatened individuals' subjective
well-being, resulting in limited physical activities and social isolation. During the COVID-19 pandemic,
people travelled less but performed more recreational activities and deliberately travelled shorter
distances.

2.1.2 The roles of information reliability in affecting activity-travel behavior
changes
When individuals face a perceived gap in their knowledge, they have a desire to seek additional
information (Cho et al., 2015: p.103). After seeking the information, people also need to process it. The
reliability of information will influence the extent to which a person will seek out risk information indaily life and the extent to which he/she will spend on analyzing the risk information critically(Neuwirth, 1999). For information seeking and processing, the reliability of information sources ishighly associated with one’s perceptions and behavior. During a new pandemic, different information
sources will provide various information to the public, such as people`s vulnerability to the virus, theprobability of infection or the sequelae of disease.

Receiving information from different sources

influences the public’s knowledge about the risks, thereby changing their decisions (Smith, 2006). The
11

Chapter 2 Literature review

reliability of information sources can affect people’s risk communication with external environment
and then affect their behaviors (Williams, 1998). Due to various factors, such as the spread of media,
individuals’ attributes and risk experience, the reliability of information sources may be distorted (Choet al., 2015: p.233). Zhao et al. (2020) conducted a study to test whether COVID-related behaviors willchange according to trust in left-leaning (e.g., CNN) or right-leaning media (e.g., Fox News) sources.
They used panel data in the USA to examine how the preventive and risky behaviors of people, who
trust different media sources, changed from March 10 to June 9. Their results indicated that false
information can be easily disseminated to the public by the media, and that individuals made different
behavioral responses according to the different levels of trust in the media. Individuals clearly do nothave equal beliefs in information from different sources. Lu et al. (2020) analyzed the relationshipbetween the trust of different COVID-19 information sources (health professionals, academic
institutions, government agencies, news media, social media, family, and friends) and individuals’
information sharing behaviors in China. Their results showed that people are prone to exchange theinformation from media or social networks to share their feelings with each other. Meanwhile,
individuals prefer to improve their knowledge about COVID-19 prevention based on information from
authoritative sources, reflecting a higher trust in these sources.

2.1.3 The role of risk perceptions in affecting activity-travel behavior
changes
Individuals’ risk perceptions may affect behavior changes during pandemics. In general, the likelihoodof people following policy measures of protection may be associated with their risk perceptions (Leppinand Aro, 2009; Sadique et al., 2007). When perceiving a risk, people will make a behavior decision
based on their own beliefs and attitudes (Fischer, 2017). A higher risk perception of exposure to a virus
may result in a higher likelihood to change travel behaviors (Pennington-Gray et al., 2011). Furthermore,
Ibuka et al. (2010) conducted a survey in the United States during the H1N1 pandemic and found thatpeople’s perceptions of risk regarding the pandemic increased over time; on the contrary, some
protection behaviors such as the intention to vaccinate decreased.
As in the case of previous pandemics, people’s risk perceptions have had a similar effect on
behavior changes during the COVID-19 pandemic. Hotle et al. (2020) used an ordered logit regression
model to analyze the risk perceptions and risk mitigation decisions with or without infection of COVID19 across the USA. They stated that individuals’ knowledge about COVID-19 has a larger impact on
reducing travels than the recent influenza experience, and people will reduce travel to non-work
locations in which they perceived medium or high risk. In the context of non-binding self-restriction
requests in Japan, Parady et al. (2020) conducted a panel web-survey to target the travel behavior
changes of individuals in the Kanto Region during the COVID-19 pandemic. They used a regression
model and a discrete choice model to estimate the behavioral changes for shopping, eating-out andleisure. The results showed that people’s decline in shopping travels, eating-out and leisure activities
12Chapter 2 Literature review

are associated with other people’s perceptions of self-restriction. Moreover, they suggested thatpolicymakers should report more information about the seriousness of COVID-19 and emphasize thebehaviors of others to the public.

2.1. The role of attitudes toward policymaking in affecting activity-travel
behavior changes
COVID-19 policymaking aims to change people’s various behaviors. Pandemics may change people’s
attitudes toward policymaking (Van Bavel et al., 2020). When people face an external threat (e.g.,
pandemics, natural disasters), they are likely to show an increasing cohesion in their groups (e.g.,nations), and have a higher trust in government staff (Greenaway and Cruwys, 2019; Sibley et al., 2020,
Toya and Skidmore, 2014; Li and Brewer, 2004; Postmes et al., 2013). However, with the increase offinancial problems (e.g., unemployment) and psychological problems (e.g., depression), the level oftrust for institutions and government staff may decrease (Bangerter et al., 2012; Quinn, 2013; Meltzer
et al., 1999). Nevertheless, one coin has two sides: some people may become more suspicious about theinstitutions and fall into the trap of conspiracy theories (Dussaillant and Guzman, 2014; Van Prooijen
and Van Dijk, 2014).
Not only attitudes toward governments (Min et al., 2020; Blendon et al., 2008; Taylor et al., 2009;
Vinck et al., 2019), but also attitudes toward medical institutes (Gilles, 2011) may have a significant
influence on people’s decisions to follow recommended protection measures (e.g., social distancing,washing hands, wearing a mask) during the current pandemic. Some studies showed that recommended
protection behaviors are more likely to happen when there is a positive attitude toward policymaking(Wong et al., 2020; Laato et al., 2020b). Chan et al. (2020a) argued that the attitudes toward
governments and the attitudes toward health care systems may have a contradictory influence on
following the recommended policy measures. Some scholars found that attitudes toward governments
are strongly associated with adherence to health guidelines (Prati et al., 2011; Quinn, 2013). The effectof local governments may be more influential than that of central governments (Shaw et al., 2020). In
addition, when attitudes affect behavior changes, the attitudes are also associated with individuals’ risk
perceptions (Prati et al., 2011). A trust in institutions can affect people’s judgments about risks andbenefits, and consequently can affect their behaviors (Siegrist et al., 2003).

2.1. PASS-LASTING based policymaking
Considering the severity of threats from this virus, it is crucial to make policy decisions in a seamlessand comprehensive way. In this regard, Zhang (2020) proposed a PASS approach for COVID-19
policymaking, especially in relation to the transport sector. The PASS approach emphasizes the different
roles of various stakeholders and argues that COVID-19 policies should be proposed from the following
four perspectives, i.e., Prepare-protect-provide (P), Avoid-adjust (A), Shift-share (S), and Substitute13

Chapter 2 Literature review

stop (S). More than 100 policy measures are proposed by Zhang (2020). As discussed previously,
behavioral interventions are an important category of COVID-19 policies. Related to this, Zhang (2021)
conducted a nationwide retrospective panel survey in Japan at the early stages of the COVID-19
pandemic and revealed that better communication design is crucial to encourage behavior changes for
mitigating the pandemic. To encourage voluntary behavior changes, Zhang (2021) further argued that a
LASTING approach is needed, which involves modifying people’s needs in life (L) and performing theresulting activities (A) at places with proper spaces (S) at the proper time and with the proper timing(TING), as much as possible.

2.2 Travel mode choice behavior during the COVID-19 pandemic
2.2.1 Travel mode choices under the impacts of COVID-19
As stated by WHO, “the virus spreads mainly between people who are in close contact with each other,for example at a conversational distance.”3 In line with such existing evidence, it is reasonably argued
that travel for leisure involving more person-to-person conversation, such as party and eating out, may
by riskier than travel for other purposes. However, as evidenced by the following literature review, there
are limited studies on travel mode choices for leisure purposes and differences of factors (e.g., especiallyrisk-related factors) affecting travel mode choices for different detailed travel purposes have further
remained unknown.
As shown in the definition of the pandemic risk given by Zhang et al. (2021a), trip making andactivity participation are a key part of the whole virus spread process, in the form of exposure andtransmission. Thus, it is necessary to investigate travel mode choices together with activity participation(i.e., travel purposes). Affected by the fear of infection, people may shift from public transport to privatecar, walk and/or cycling (De Vos, 2020, Chen et al., 2022, Zhu et al., 2022). Although travel mode
choices were researched with respect to commuting (Bhaduri et al., 2020; Cusack, 2021; Das et al.,2021) and shopping (Zannat et al., 2021; Shaer et al., 2021) and by classifying travel purposes into
commute, shopping, and other non-commuting purpose (Thombre et al., 2021; Tarasi et al., 2021;
Abdullah et al., 2021a), comparative studies on travel mode choices for more detailed travel purposes(especially for leisure activities) during the COVID-19 pandemic are still very limited.
What kinds of changes in travel mode choices have been revealed
The COVID-19 virus spreads fast from people to people via daily trip making (Hendrickson et al., 2020;3

https://www.who.int/news-room/questions-and-answers/item/coronavirus-disease-covid-19-how-is-ittransmitted (Accessed on May 22, 2022)

14

Chapter 2 Literature review

Abdullah et al., 2021b). As a result, people may have to change some of their travel modes for avoiding
being infected. Shakibaei et al. (2021) revealed that about one-third of the respondents in Istanbul
shifted from public transport to private cars. Zhu et al (2022) found an obvious shift from subways towalking for commuting in 58 cities of 31 countries across the continents of America, Asia, Europe, andOceania. De Vos (2020) and Zhu et al. (2022) observed that people increased walking or cycling forrecreational purposes, such as enjoying the sensation of speed or exposure to the natural environment.
Cusack (2021) found that the essential workers in USA increased active transport for commuting, eventhough they were less satisfied with community support (e.g., safety in busy roads, lack of bicycle lanes)
for active transport choices. Das et al. (2021) showed that an increasing infection risk of shared travelmodes might result in a systematic shift from public transport to car commuting in India, leading toworsened traffic congestion. Abdullah et al. (2021a) confirmed that people in Pakistan significantly
shifted from public transport to private car for long-distance travel (>5 km). Eisenmann et al. (2021)
revealed that during the strictest period of lockdown (i.e., April 2020) in Germany, the share of public
transport decreased, while the share of car users remained stable, indicating that private card became
more important during the pandemic. Przybylowski et al. (2021) found that people in Gdańsk, Poland
shifted from public transport and shared mobility to cars, bicycles and walking significantly during a
lockdown period, while about 75% of the respondents who stopped or restricted the use of public
transport planned to return to using public transport when the infections became stabilized. Focusing
on the post-pandemic transformation in transport systems in Europe and North America, Ciuffini et al.
(2021) showed that active transport is required for reinforcing the resilience of urban transport.
Differences in travel mode choices across travel purposes
Using data collected in Istanbul, Turkey, Shakibaei et al. (2021) found that more people began to shift
from public transport to private car for commuting trips from February 25 to April, 2020 when people
became more sensitive to the virus infection risk and took actions against the pandemic; the decreaseof public transport was more obvious than that of private car and walking from February 25 to April 24,2020 for social/recreational/leisure travels; and private car and walking are the most common modes
being used for social/recreational/leisure travels and shopping travels from March 23 to April 24 whenpeople took actions against the pandemic. Shaer et al. (2021) showed that older adults in Isfahan, Iranincreased the use of active transport for shopping and aimless recreational travels. Targeting India,
Bhaduri et al. (2020) observed that personal vehicles (car and motorbike) experienced the biggest
decrease for commuting, probably because of the shift to telework, and the use of personal vehicles
showed the biggest increase for shopping and other travel purposes, followed by active transport (walkand cycle).

15

Chapter 2 Literature review

Factors associated with travel mode choices under the context of the pandemic
Psychological factors and travel attributes could lead to tremendous changes in travel mode choices
during the COVID-19 pandemic (Chan et al., 2020b; Ding and Zhang, 2021; Zhang, 2021). Among thepsychological factors, risk perception in vehicle is one of the most popular factors to explain travel
mode choices in such a context (Zafri et al., 2022; Chen et al., 2022; Abdullah et al., 2021b). The risk
perception at a larger spatial scale was confirmed to be influential to changes in various behaviors indaily life (Ding and Zhang, 2021). In Ding and Zhang’s study, respondents were asked about whether
they thought the infection risk increased at four spatial levels (the whole country, residence prefecture,
residence municipality, the often-visited places) or not. Another important psychological factor ispeople’s trust in policymakers, Ding and Zhang (2021) found that higher trust in policymakers could
encourage people to reduce the use of public transport and increase the use of car and active transportin Japan. For travel attributes, travel time (Chen et al, 2022; Aaditya

Rahul, 2021), travel habit (Gaoet al., 2020; Bhaduri et al., 2020; Ding and Zhang, 2021) were found to affect travel mode choices.
Aaditya et al., (2021) further observed that when choosing public transport, people are willing to spend
more time to ensure their personal safety.

2.2.2 Influences of risk perception and trust in policymakers on travel mode
choiceRisk perception is a typical factor to describe human behaviors under the threat of risk (Aaditya et al.,2021; Chen et al., 2022), while trust in policymakers determines how people respond to policies (Wonget al., 2020; Laato et al., 2020b). Travel mode choices during the COVID-19 pandemic are found to beaffected by risk perception inside vehicles (Chen et al., 2022; Zafri et al., 2022), risk perception at
activity destinations (Ding and Zhang, 2021; Zhang, 2021), and trust in governments and health/medical
agencies (Sibley et al., 2020; Wong et al., 2020; Laato et al., 2020b; Ding and Zhang, 2021; Zhang,2021).
Zafri et al. (2022) analyzed perceived risks of viral transmission in different travel modes in
Bangladesh and identified influential factors based on an ordinal logistic regression model. It is foundthat people perceived a high risk of viral transmission in bus, moderate risk in shared modes (rickshaw,
auto-rickshaw, ridesharing), and low risk in private modes (private car, motorcycle/scooter, walking,cycling). It is further stated that risk perception about the use of public transport might encourage people
to shift to private modes, which would worsen urban transport problems and undermine sustainabletransportation goals. Targeting travel mode choices in the Netherland, Chen et al. (2022) measured risk
perception using the following three questions based on a five-point Likert scale: whether people think
it is unsafe in public transport, whether people want to share with others in public transport, and whether
people think it unsafe to share with others in public transport. Chen et al. showed that people were prone
to perceive public transport as a riskier mode, in comparison with private modes. Abdullah et al. (2021b)
16

Chapter 2 Literature review

evaluated the risk perception inside transport vehicles by asking questions about whether safety
precautions (social distancing, wearing a facemask, use of hand sanitizers) were taken inside public
transport vehicles, using a five-point Likert scale. They revealed that perceived safety precaution was
significantly related to people’s willingness to choose a public transport.
At the level of activity destinations, risk perception has been measured at levels of country,
residence perfection, and residence municipality, also using a Likert scale (Zhang, 2021; Ding andZhang, 2021). Zhang (2021) estimated a structural equation model based on data collected in Japan toinvestigate the associations between five latent variables (changes in daily life, reliability of information,risk perception, triggers of behavioral changes, and attitudes) during the COVID-19 pandemic. Zhang(2021) found that if people perceived a higher level of risk perception (inside transport vehicles and atother spatial scales, they were more likely to reduce the use of public transport and increase car trips orwalking/cycling trips. The study conducted by Ding and Zhang (2021) also presented similar findings.
People’s trust in governments and health/medical agencies may change during the COVID-19
pandemic (Sibley et al., 2020). Some studies revealed that people may easily follow those recommended
behaviors by governments and health/medical agencies, if people highly trusted them (Wong et al., 2020;
Laato et al., 2020b). Ding and Zhang (2021) used a dynamic structural equation model to examine theeffects of subjective factors on individuals’ behavior changes over time and found that higher trust inpolicymakers could encourage people to make more behavior changes during the pandemic. However,the study by Ding and Zhang (2021) did not incorporate the decision-making mechanisms related totravel mode choices and the analysis of travel modes did not pay attention to potential differences causedby travel purposes.

2.2.3 Influences of cultural risk factors on transportation field
As for cultural risk factors, the cultural theory of risk, which was first proposed by Douglas (1970), hasbeen regarded to be a key theory. The cultural theory of risk treats risk perception as a social process
and argues that the perceived level of risk is affected by underlying cultural biases associated with one’s
worldview (i.e., values and preferred form of social order): i.e., the perceived risk is primarily explainedby the socio-cultural context (Douglas, 1992). The cultural theory of risk emphasizes two dimensionsof group and grid, where group refers to “the degree to which an individual is bonded into social groups”(e.g., “who am I” or “who am I with”) and grid is “the degree to which social interactions should beconstrained by rules and norms” (e., “how should I behave”), where if an individual faces a threat to
his/her ideal cultural cognition or social order, he/she will perceive a higher level of risk (Jenkins-Smithet al., 2009/2019; Kahan et al., 2006; Lodge et al., 2010; McEvoy et al., 2017; Nan et al., 2014;
Ripberger et al., 2012; Silva et al., 2007; Song et al., 2014a,b; Thompson et al., 2018; Wildavsky et al.,
1987; Wildavsky et al., 1990).
As stated in the above references about the two dimensions of group and grid, people can beclassified into four cultural orientations: hierarchism, egalitarianism, individualism, and fatalism.
17

Chapter 2 Literature review

Hierarchism corresponds to a high-level group and a high-level grid. Hierarchists prefer strong groupattachment and clear obedience of social rules, and as a result, they often trust the authority in society
and insist on the contribution to the whole society. Egalitarianism corresponds a high-level group and alow-level grid. Egalitarianists think that social relationship in a group is very important, but they do nottrust or want to follow the rules proposed by the authorities unless these social rules are decided by allgroup members. Individualism is weak in both grid and group dimensions. Individualists, who enjoy anuntrammeled and self-regulated life, prefer competition with an equal opportunity rather than solidarity
in the society and dislike the regulations from the authorities. Finally, fatalism has a high level in thegrid dimension but a low level in the group dimension. Fatalists think that most things in life are totallyout of their control and the fate or luck plays the dominant role in their life. They are not interested in
the social relations in group/society and tend to avoid the social rules if possible.
In the transportation field, there are limited applications related to road traffic safety, but not
focusing on the whole picture of the above four types of cultural orientations. Gaygısız (2010) revealed
that hierarchism is positively associated with traffic fatalities, while egalitarianism shows a negative
influence. Atchley et al. (2014) found similar associations of hierarchism and egalitarianism, not only
related to facilities but also related to traffic crashes and injuries. Solmazer et al. (2016) investigatedwhether and how hierarchism and egalitarianism are associated with fatality rates and law enforcements
and confirmed that hierarchism is positively associated with traffic fatality rates and negatively
associated with law enforcements, which are opposite the results of egalitarianism. Üzümcüoglu et al.
(2018) investigated the associations of not only hierarchism and egalitarianism but also individualismwith traffic law enforcements and drivers’ violation behaviors and found that non-speeding traffic
violations (e.g., aggressive violations, tailgating, and using a mobile phone without a hands-free kit) are
positively correlated with hierarchism and negatively with egalitarianism and individualism.

2.2. Influences of preparedness on under-pandemic travel mode choices
COVID-19 should be regarded as an unpreceded event for most people. Thus, it is difficult to say thatpeople could prepare for it. On the other hand, because some measures against COVID-19 (e.g., mask
wearing, hand washing) are similar to those against influenza. Travel habits formed during influenza
seasons before the COVID-19 pandemic could play a similar role of preparedness for COVID-19.
About the effect of preparedness on people’s travel mode behavior, Gao et al. (2020) investigated
the influences of travel habit, as a proxy variable of preparedness, on travel mode choices. The following
eight questions are used to measure travel habits: (1) I have frequently used car/metro in commuting
for a long time, (2) I habitually used car/metro for commuting, (3) I commute by car/metro without
having to consciously remember, (4) It makes me feel weird if I do not use car/metro in commuting, (5)Besides car/metro, I do not think about other options for commuting, (6) I would find hard not tocommute by car/metro, (7) Commuting by car/metro belongs to my daily routine, and (8) I commuteby car without thinking. Obviously, travel habits are measured without distinguishing between different
18Chapter 2 Literature review

travel modes. Bhaduri et al. (2020) defined travel habit as the most frequently used travel mode before
the pandemic and found a positive effect on travel mode choices during the pandemic. Unfortunately,
it is unclear whether the period before the pandemic included any risky environment. In other words,
only travel habits without paying special attention to any risky choice environments were targeted.
During the COVID-19 pandemic, people may also try to form a kind of risk-concerned or virussensitive travel habit. From such a perspective, Ding and Zhang (2021) incorporated people’s
accumulated behavior changes during the pandemic, as a kind of preparedness, to explain the recentchanges in various behaviors (including travel mode choices) based on a dynamic structural equation
model.

2.2. Applications of hybrid choice models in the COVID-19 context
Hybrid choice model (HCM) is an expanded choice model consisting of structural equations andmeasurement equations as well as a utility equation, where latent variables are included. HCM has aflexible choice structure, which can accommodate a probit-based or logit-based structure (Bhat et al.,2016). Bhat (2015) formulated a generalized heterogeneous data model (GHDM) with a probit-based
structure to jointly estimate mixed types of dependent variables (ordinal/binary variables foroccurrences of weekly non-commute travel, nominal variables for travel mode and residential location
choices, continuous variables for commute distance). Kang et al. (2021) used the above GHDM toinvestigate how people simultaneously choose five binary variables related to pooled ride-hailing. In
comparison with the probit-based model, the logit-based HCM is more widely applied in thetransportation field (Ben-Akiva et al, 2002; Hess et al., 2018; Scagnolari et al., 2015; Thorhauge et al.,2019; Vij and Walker, 2016; Walker et al., 2002). Thus, in this study, we adopt the logit-based HCM.
There are also a lot of applications to the analysis of travel mode choices (Borriello et al., 2019; Hesset al., 2018; Huan et al., 2021; Ingvardson et al., 2021; Kamargianni et al., 2015; Kim et al., 2017;
Mehdizadeh et al., 2019). However, the applications to travel mode choices in the COVID-19 context
are very limited. Only two studies have been found. The first study was done by Aaditya et al., (2021),
who focused on two latent variables of the safety perception about public transport (related to threemeasures of sanitization, crowd management, social distancing) and attitude towards public transport(i.e., whether people believe using a motorized travel mode is a symbol of prosperity, in comparison to
public transport). Aaditya et al. revealed that attitude towards public transport did not influence people’s
willingness to choose a public transport, while the safety perception showed a dominant effect on the
choice of public transport. The second study was conducted by Chen et al. (2022), who targeted four
latent variables of social responsibility, risk perception (using three questions about the perceived riskin public transport, measured in a five-point Likert scale: 1) travel by public transport during the
COVID-19 pandemic is not safe; 2) I do not like sharing space with strangers in using public transport;
3) sharing space with strangers in public transport during the pandemic is unsafe)), fear of infection(using two questions about physiological manifestation of fear, measured in a five-point Likert scale: 1)19Chapter 2 Literature review

my heart races or palpitates when I think about getting COVID-19; 2) my hands become sweaty when
I think about COVID-19) and travel anxiety. Chen et al. incorporated four travel purposes (i.e., work,
shopping, leisure, and social purposes), as a dummy variable, respectively, into a travel mode choicemodel and confirmed the statistical significance of all the latent variables. It is observed that the fear of
infection was positively related with choosing private cars and shared E-bikes, as well as walking, andpeople were more likely to perceive public transportation as a more dangerous mode than private modes.

2.3 Social contact behaviors during the COVID-19 pandemic
2.3.1 Ignorance of indirect social contact
As stated by WHO4, COVID-19 spread between people mainly in three ways: (1) through close contact
with each other (e.g., at a conversational distance), (2) in poorly ventilated and/or crowded indoor
settings, and (3) when touching eyes, nose, or mouth after touching surfaces or objects that have been
contaminated by the virus. Thus, both direct and indirect social contacts could spread the virus. However,existing studies only focus on skin-to-skin touch and face-to-face conversation (Fadilah et al., 2020;
Latsuzbaia et al., 2020; Zhang et al., 2020a; Trentini et al., 2022; Zhang et al., 2021b), by following thedefinition for previous infectious respiratory diseases (Mossong et al., 2008; Hoang et al., 2019; Leunget al., 2017). Existing studies (Brooks-Pollock et al., 2020; Dorélien et al., 2021; Wong et al., 2022;
Tomori et al., 2021; McCarthy et al., 2020; Tizzani et al., 2022; Jarvis et al., 2020) have mainly citedthe definition given by the POLYMOD (Improving Public Health Policy in Europe through Modellingand Economic Evaluation of Interventions for the Control of Infectious Diseases) (Akakzia et al., 2007;
Mossong et al., 2008), which adopts the mean number of contacted persons. As confirmed by otherexisting studies on COVID-19, the COVID-19 virus (i.e., SARS-CoV-2) can travel more than six feetthrough people’s aerosols (Ge et al., 2020) and infect people by their indirect contact with virus-infectedsurfaces (Daraei et al., 2020; Cai et al., 2020; Zhang, Z. et al., 2020). Hence, focusing only on close or
direct contact is insufficient and biased, and it is necessary to also accommodate indirect contact.
McCreesh et al. (2022) implemented one of the few studies investigating people’s indirect contact withothers. However, they only used the contact time and ignored the number of contacted persons becauseof the unavailability of casual contact data.
In summary, most previous studies only focus on close contact (skin-to-skin touch or face-to-faceconversation) behaviors but neglect individuals’ indirect contacts, which could also transmit the virus[Research gap 1].

4

https://www.who.int/news-room/questions-and-answers/item/coronavirus-disease-covid-19-how-is-ittransmitted (accessed on December 15, 2022)

20

Chapter 2 Literature review

2.3.2 Lack of the representation of joint social contact decisions
After the restrictions from the government were lifted gradually in many countries, social contact haveshown an increasing trend, but it is still difficult to return to the pre-pandemic levels (Wong et al., 2022;
Liu et al., 2021; Latsuzbaia et al., 2020). For instance, Zhang et al. (2021b) used the social contact datacollected on March 1-20, 2020 in Shanghai, Shenzhen, Changsha, and the data on May 7-15, 2020 in
Wuhan (about one month after the lockdown was lifted) to compare the changes in contact patterns
across the pre-pandemic, lockdown and post-lockdown periods. The mean number of contacts increased
from 5% to 17% after lockdowns in these cities. Although the intracity mobility has rebounded to prepandemic levels, the number of contacts only recover from one-seventh to one-third of the pre-pandemic
level.
Such a mean number of contacts have been further investigated by associating with different
demographic attributes (e.g., age, gender, household size, occupation) and contact settings/locations(household/home, workplace, school, community, leisure activity, transport, shopping). To be specific,
Tomori et al., (2021) applied a self-reported contact survey data and mobility data (from Google andApple) in German from April to June, 2020 to investigate the reduction of the mean number of social
contacts over time via the age-specific social contact matrices of direct social contacts. Respondents
were asked about the age and sex of their social contacts, the duration they spend with each contact andthe contact setting (household, school, work, transport). In their results, most of the social contacts
occurred in household. After the lockdown measures were relaxed in German, people’s mobility
behavior went back to pre-pandemic levels almost instantly, while the degree of contacts still maintains
a persistent reduction because people may reduce close contacts outside home and keep the social
distance deliberately. Sypsa et al. (2020) conducted a social contact survey in Greece from March 31 toApril 7, 2020, which is during the lockdown in Greece. They collected information about people’s social
contacts approximately two months earlier than the lockdown and during the lockdown. They foundthat daily contacts were reduced by 86.9% during the lockdown. The decrease in contact during
lockdown is most obvious in a household, the school closure measure may lead to an increase in leisurecontact or a decline in work contact. People are prone to contact peers when doing leisure activities,
while that phenomenon is not obvious in other contact settings before the lockdown. Trentini et al.
(2021) collected individuals’ social contact data in the South West Shewa Zone (SWSZ) of Ethiopia
from November 2019 to December 2019 when schools were open as usual. And they explored theimpact of socio-demographic factors and observed mixing patterns on the COVID-19 disease burdenin three spatial scales: remote settlements, rural villages, and urban neighborhoods. For each spatial
scale, there are three contact settings: households, schools, and the general community. Their results
show that the mean number of social contacts (excluding school contacts) is higher in both urbanneighborhoods and remote settlements, but lower in rural villages, especially for these old people who
are over 60 years old. Nevertheless, the mean number of social contacts in school did not show obvious
differences across these three spatial scales. For the mixing patterns, these people aged below 30 years
21Chapter 2 Literature review

old (especially between 10 to 19 years) are more likely to contact their peers in a general community
across the three spatial scales. McCarthy et al. (2020) compared the average number of contacts per dayof one random individual during the pandemic (from January to May 2020) in Ontario, Canada with themean value of contacts in POLYMOD survey data before the pandemic to explore the age-specificsusceptibility of daily contact mixing patterns in workplace, household, school, and community settings.
They found an obvious reduction in the average contact rate (for total contact in all contact settings)
following the implementation of control measures, but the contacts in households increased 13% after
school closure, 45% after the additional physical distancing measures, and 51% after the closure of nonessential businesses. Tizzani et al. (2022) used the CoMix social contact data (a multi-country social
contact survey started in March 2020 and conducted in 19 European countries including respondents’
information on social contact in the contact settings of home, work, school, leisure activities, otherplaces (Verelst et al., 2021)) and POLYMOD data in Italy to investigate the impact of tiered restrictions
on social contacts between December 2020 and April 2021. Tizzani et al. summarized the differencesin the mean number of contacts by waves for CoMix and Polymod from the perspective of age, gender,and household size, and provided an analysis of the difference in the mean number of contacts by age
for different contact settings of home, work, other. Their results show that NPIs reduced the number ofcontacts significantly across the age groups and contact settings. Elderly people aged over 60 had alower number of contacts and a lower decrease of contacts than other age groups. Household contact
did not show obvious differences across the regions with different levels of non-pharmaceuticalinterventions. While the other contacts (such as leisure contact) are more likely to be affected by thestrictness of interventions, especially for younger people. Kiti et al. (2020) used online social contact
survey data obtained from 304 employees of three U.S. companies between April to June 2020 toanalyze the changes in daily contact among workforces. In their study, the collected contacts areclassified into different modes: conversation only (54 %), physical contact only (12 %), bothconversation and physical contact (34 %). The descriptive analysis shows that most contacts of
employees occurred at home (64 %), followed by shopping contacts (14 %). Most daily contacts lasted
more than 4 hours (38 %). The long-lasting contacts occurred at the respondent’s home, while shortlasting contacts occurred outside. More than 60% of the daily contacts are repeated contacts over days.
In summary, previous studies only investigated the number of social contacts (especially only theaverage values) as the indicator of social contact behaviors, and there is no study on social contact
mode choices, except the study by Kiti et al. (2020). ome studies explored the changes in the numberof social contacts for different contact settings, but they tend to categorize the shopping and leisuresettings into “other”, probably leading to seriously biased findings. hese are Research gap 2.

2.3.3 Lack of modeling approaches with the dependence of social contact
behaviors
Concerning the modeling approaches of social contact behaviors, existing studies have mainly focused
22Chapter 2 Literature review

on the number of contacted persons, based on regression models.
Wong et al. (2022) estimated a generalized additive mixed regression model to investigate factors
affecting changes in average daily contacts by country and by contact settings (e.g., at home, at work)
based on a repeated cross-sectional survey implemented in 21 European countries between March 2020
and March 2022, where a zero-inflated Poisson distribution is assumed. Influences of age, household
size, gender, risk perception, measures taken against COVID-19 were quantified.
Latsuzbaia et al. (2020) applied a Poisson regression model to investigate influential factors to thenumber of social contacts, where data were collected in Luxembourg during the lockdown (from March
25 to May 1) and after the lockdown (from June 12 to June 25), with respect to the number of social
contacts in different contact settings (home, work, leisure place, shopping places, etc.). They examined
the influences of age, nationality, and the survey period. To overcome the shortcomings of the Poisson
regression model, which allows for overdispersion (i.e., enables to model data exhibiting more variance),
Zhang et al. (2020a) estimated a negative binomial regression model by conducting social contact
surveys in Wuhan and Shanghai, China on February 1-10, 2020 to investigate the change in people’ssocial contacts. The baseline of the number of contacts is defined as people’s contacts before thepandemic. Gender, age, occupation, and household size are examined. Similarly, Feehan et al. (2021)
also applied a negative binomial regression model to analyze the number of daily contacts in the USA
between March 22 and September 26, 2020. Age, education, ethnicity, household size, residential
location, and gender are incorporated in the analysis. Dorélien et al. (2021) used a negative binomial
regression model to evaluate the number of social contacts from April to May 2020 in Minnesota, USA,
where age, gender, race/ethnicity, region, and other demographic characteristics are included as
explanatory variables. Using data collected in Milan, Italy between July 2020 and March 2021, Trentini
et al. (2022) also estimated a negative binomial regression model by targeting the number of daily
contacts, but under different levels of restrictions.
In summary, the previous social contact modeling methods are only based on the regression
approaches. As a result, the dependence between the number of social contact and contact mode choices
have been ignored. Furthermore, existing modeling analyses only focus on the effects of demographic
characteristics, and the impacts of people’s psychological factors have been ignored. hese are the
Research gap 3 that is identified from the above literature review.

2. Summary
This chapter summarized the research background of the dynamic activity-travel behavior changes andthe behaviors of travel mode choice and social contacts related to the COVID-19 pandemic in previous
studies. Based on the literature review, the research gaps need to be resolved are found as follows.

23

Chapter 2 Literature review

1) Behavioral and psychological adaptations to COVID-19
The dynamic association between people’s behavior changes and their psychological factors related tothe pandemic policymaking haven’t been explored.
2) Travel mode choice behavior during the COVID-19 pandemic
Limited research efforts were made to compare factors affecting travel mode choices for different travel
purposes, especially related to leisure. The studies focusing on the impact of people’s risk perception at
different spatial scales and the trust in governments and health/medical agencies on travel mode choices
are limited. the cultural risk factors have been recognized to determine people’s responses of travel
mode choices to the risks of the COVID-19 pandemic. Preparedness averts peril; however, it hasremained under-explored how people prepared for such a pandemic of COVID-19 and how
preparedness affects travel mode choices under the impacts of COVID-19. Previous studies about theapplications of HCM did not consider individuals’ taste variations and the associations between latent
factors for travel mode choices during the COVID-19 pandemic simultaneously.
3) Social contact behaviors during the COVID-19 pandemic
Most previous studies only focused on the close contact (physical contact or direct conversation)
behaviors but neglected individuals’ social contact behaviors in the forms of other contact modes, suchas the non-close contact, or both of them. Previous studies only studied on the number of social contacts(especially for the changes of the mean numbers) as the indexes of social contact behaviors, the analysis
on people’s contact mode is very limited. Meanwhile, some studies explored the changes of the numberof social contacts for different contact settings, while they tend to categorize the shopping and leisuresettings into “other”, the classification of settings is rough. The previous modeling methodology used
for analyzing social contact is only restricted to the regression approach, so the coupling relationshipbetween the number of social contact and contact mode haven’t been explored in previous studies. And
the most previous modeling approach only focused on the effect of demographic characteristics onsocial contact, the impact of people’s psychological factors was ignored in the modeling process.

24

Chapter 3 Pandemic-responsive methodologies

CHAPTER 3 Pandemic-responsive
methodologies
This chapter introduces the methodology used in this study. A life-oriented panel online survey isconducted in Japan at the end of October 2020 to collect people’s behavior changes under the impact of
the COVID-19 pandemic before September 2020. And an online comparative survey of activity-traveland social contact before-during COVID-19 is conducted in six developed countries (Australia, Canada,
Japan, New Zealand, the US, and the UK) form March to May 2021 to collect people’s behavior
information before May 1st, 2021. Figure 3.1 shows the trend of new daily confirmed cases in Australia(AU), United States (US), New Zealand (NZ), Canada (CA), United Kingdom (UK) and Japan (JP)from April 2020 to May 2021 and targeted survey period of behaviors in this study. It shows that the
number of new daily confirmed cases in latest wave of the pandemic before the survey declined afterJanuary 2021 and remain stable since the end of February 2021. And most of the strict lockdown
measures have been lifted in these six countries before March 2021. The data in life-oriented panel
survey is used for the analysis in chapter 4, while the data in comparative survey of activity-travel andsocial contact before-during COVID-19 is used in chapter 4-7. First, the details of surveys are
introduced in section 3.1, then the applied modeling approaches in this study are introduced in section
3.2.
350000300000250000Survey period of behaviors
in life-oriented survey

Survey period of behaviors in comparative survey of activity-travel and social contact

200000150000100000
50000
0

AU

US

NZ

CA

UK

JP

Figure 3. 1. Targeted survey period of behaviors in two surveys

25

Chapter 3 Pandemic-responsive methodologies

3.1 Nationwide behavioral surveys
3.1.1 Life-oriented panel survey of behavioral and psychological adaptations
to COVID-19

1) Survey design
The questionnaire of life-oriented panel survey has two parts. The first part includes the questions about
individual and household attributes. The second part of the questionnaire focuses on individuals’changes in various behaviors from April 2020 to September 2020 and people’s subjective psychologicalattributes. The former of second part is captured based on the life-oriented approach, while the latterincludes the reliability of major COVID-19 information sources, risk perceptions about the COVID-19
virus, and attitudes toward policymaking in terms of policymakers and health professionals as well as
PASS-LASTING based policy measures in a 5-point Likert scale. The behaviors included in the survey
contain not only travel behaviors but also other life choices and activities, within cities and across cities.
More details of the questionnaire can be found in Appendix 1.

2) Survey implementation
A life-oriented retrospective panel survey was implemented online with respect to 2,643 respondents
living across the whole of Japan, who reported changes in their various life behaviors between April
and September 2020, various attitudinal attributes, and individual socio-demographic attributes. The
collected samples approximately reflect the distributions of age and gender as well as 47 prefectures,covering the whole population of Japan, based on data from the Statistics Bureau of Japan (2021). Table
3.1 shows the differences between the shares of respondents in the survey and those of the population
by regions of Japan. The population in Kanto and Hokkaido are over-represented by 5.9 and 4.4
percentage points, while the population in Chubu and Kinki are under-represented by 4.1 and 3.3
percentage points, respectively. The other differences are all within 2.0 percentage points.

26

Chapter 3 Pandemic-responsive methodologies

Table 3. 1. Share differences of respondents in the survey and the population by regions
Shares of respondents in the survey (%)

Shares of the population

(A)

(B)

Hokkaido

8.6

4.2

4.4

Tohoku

6.2

7.0

-0.8

Kanto

40.3

34.4

5.9

Chubu

12.7

16.8

-4.1

Kinki

14.2

17.5

-3.3

Chugoku / Shikoku

7.1

8.7

-1.6

Kyushu / Okinawa

10.9

11.4

-0.5

Regions

A– B

3) Respondents
Regarding the location of survey respondents, 41.2% resided in megacity regions (e.g., Tokyo, Nagoya,
and Osaka), 29.3% in ordinance-designated cities, and the remaining in local cities. Among all 47
prefectures of Japan, the respondents from Tokyo accounted for the biggest share of 19.3%, followedby those from Kanagawa Prefecture (10.4%) and Hokkaido (8.6%). As shown in Table 3.2, theproportion of female respondents (52.8%) is a little higher than that of males (47.2%). More than halfof the respondents were aged between 30 and 60 years old, 18.2% were less than 30 years old, while
the share of the respondents aged 60+ years old was 23.8%. Most participants were company employees
or self-employed (40.2%), followed by housewives with the second biggest share of 17.1%. Only 7.5%of the respondents were students. Respondents with a university education background (graduated or inschool) and high school (graduated or in school) accounted for 43.3% and 28.3%, respectively. Detailed
questionnaire items are explained in chapter 4.

27

Chapter 3 Pandemic-responsive methodologies

Table 3. 2. Socio-demographic attributes of the survey respondents
Attributes
GenderAge

Occupation

Education
backgroundDefinition
Male
FemaleBelow 30 years old
Between 30 and 60 years old
Over 60 years old
Company employee/self-employed
Civil servants / organizational employees
Faculty and staff of educational institutes such as schools anduniversities
Housewife
Part-time job
Student
Other unemployed (including pensioners)
OthersJunior high school
High school
Specialized training college
Junior college / technical college
University
Graduate School

N
1247
1396
480
1534
629
1062
89

%
47.2
52.8
18.2
58.0
23.8
40.2
3.4

33

1.2

452
356
197
366
88
58
749
278
268
1144
146

17.1
13.5
7.5
13.8
3.3
2.2
28.3
10.5
10.2
43.3
5.5

3.1.2 Comparative survey of activity-travel and social contact before andduring the COVID-19 pandemic
1) Survey design
The questionnaire of comparative survey of activity-travel and social contact before-during COVID-19
consists of four parts.
The first part collects individual and household attributes (e.g., gender, age, resident region,
occupation, household size, marital status, education background and annual household income).
The second part collected people’s social contact information (e.g., contact mode, number ofcontacted persons), average performance information (e.g., frequency, duration), and psychologicalfactors for doing different daily activities before (influenza season in 2019) and during (March 2020 to
May 2021) the pandemic. In this part, seven contact settings are designed to show the heterogeneitiesof the social contacts occurred in different locations of the daily activities (working/studying incompany/school, shopping in supermarket/shopping mall, eating out in restaurant, doing exercise/sports
in indoor places like gym, joining the party in indoor places like bar, culture leisure in indoor places
like cinema, doing medical activities in indoor places like hospital).
The third part of the questionnaire collects people’s social contact information, average
performance information and psychological factors in different transport vehicle types before (influenza

28

Chapter 3 Pandemic-responsive methodologies

season in 2019) and during (March 2020 to May 2021) the pandemic.
The final part collects people’s subjective statements to the reliability of COVID-19 information
sources, risk perceptions about the COVID-19, triggers of the behavior change, attitudes toward
policymakers/health professionals, attitudes toward PASS-LASTING based policy measures, and thecultural risk factors based on cultural theory of risk in a 5-point Likert scale.
The questionnaires can be found in Appendix 2 and 3.

2) Survey implementation
In this study, the retrospective survey was conducted online in six developed countries (Australia: AU,
United States: US, New Zealand: NZ, Canada: CA, United Kingdom: UK, and Japan: JP) from March
to May 2021. With the assistance of a major international survey company, the online questionnaires
were distributed to respondents by keeping the sample distributions of age, gender and regions withineach country to be consistent with the corresponding population distributions 5 . More than 1,000
respondents in each country (AU: 1125, US: 1297, NZ: 1193, CA: 1176, UK: 1305, JP: 1169) provided
valid information about activity and travel behaviors during and before the COVID-19 pandemic,
various psychological factors, and individual/household attributes. In total, data from 7,245 respondents
are used in this study.
Regarding the distribution of survey data by regions, most respondents in six countries resided inmegacity regions such as the New South Wales/Australian Capital Territory in AU (32.8%), California
in US (12.1%), Auckland/Northland in NZ (32.2%), Ontario in CA (35.5%), Greater London in UK(12.2) and Kanto in JP (34.1%). Table 3.3 shows the differences between the regional shares of
respondents in the sample and in each country (at the time of February 2021). The shares of populationin Auckland, Northland (NZ), Ontario (CA), North West, North East and South East (UK) are underrepresented by 4.6, 3.5 and 2.2 percentage points, respectively, while the gaps between the sample andthe population in other areas are all less than 2.0 percentage points. Note that the sample
representativeness could also be defined using other sample attributes. Thus, it can be said that thecurrent survey sample roughly represents the whole population in these six countries.

5

Australian Bureau of Statistics: https://www.abs.gov.au/ (2020)
U.S. Census Bureau: https://www.census.gov/ (2020)
Stats NZ: https://www.stats.govt.nz/ (2018)
Statistics Canada: https://www.statcan.gc.ca/en/start (2020)
Office for National Statistics - GOV.UK: https://www.gov.uk/government/organisations/office-for-nationalstatistics (2020)
Statistics Bureau of Japan: https://www.stat.go.jp/english/ (2020)29

Chapter 3 Pandemic-responsive methodologies

Table 3. 3. The shares of respondents and population by regions

Country

Australia
(AU)

US

New
Zealand(NZ)

Canada(CA)

UK

Regions
New South Wales, Australian Capital
Territory
Queensland
South Australia, Northern Territory
Victoria, Tasmania
Western Australia
Arizona, New Mexico, Colorado
Washington, Alaska, Hawaii, Oregon,
Wyoming, Montana, Idaho, Nevada,
Utah
California
Texas
FloridaNorth Carolina, South Carolina, Georgia
Virginia, West Virginia, Maryland,
Delaware, Washington, D.C., Tennessee,
KentuckyArkansas, Mississippi, Oklahoma,
Louisiana, Alabama
New York, Connecticut, Rhode Island,
Massachusetts, Vermont, New
Hampshire, Maine
New Jersey, Pennsylvania
Wisconsin, Illinois, Indiana
Michigan, Ohio
Minnesota, North Dakota, South Dakota,
Missouri, Kansas, Nebraska, Iowa
Auckland, Northland
Bay of Plenty, Gisborne
Canterbury
Manawatu-Wanganui, Taranaki, Hawke's
Bay
Marlborough, Nelson, Tasman, West
Coast, Southland, Otago
WaikatoWellington
Alberta, Northwest Territories
British Columbia, Yukon
Manitoba, Nunavut, Saskatchewan
New Brunswick, Nova Scotia, Prince
Edward Island, Newfoundland andLabrador
Ontario
QuebecScotlandNorthern Ireland, Wales
North West, North East
Yorkshire and The HumberWest Midlands
East Midlands
South WestSouth East30

Shares of
respondents in thesurvey (%)(A)

Shares of the
population (%)(B)

Deviationof share(%)(A – B)

32.8

33.7

-0.9

19.9
8.9
26.5
11.9
6.5

20.1
7.9
27.9
10.4
5.7

-0.2
1.0
-1.4
1.5
0.8

6.5

5.8

0.7

12.1
8.3
6.6
8.3

12.3
8.5
6.6
8.5

-0.2
-0.2
0.0
-0.2

8.5

8.8

-0.3

6.2

6.1

0.1

9.3

9.6

-0.3

6.9
7
7.2

6.9
8.2
6.7

0.0
-1.2
0.5

6.6

6.3

0.3

32.2
8.8
13.7

36.8
7.7
13.0

-4.6
1.1
0.7

11.6

10.8

0.8

12.6

11.6

1.0

9.9
11.2
12.3
14.9
8.1

9.4
10.7
11.3
14.6
6.3

0.5
0.5
1.0
0.3
1.8

7.8

6.2

1.6

35.5
21.4
8.7
9.0
12.8
9.2
8.8
8.5
9.4
11.7

39.0
22.6
8.5
7.5
15.0
8.4
8.9
7.1
8.5
13.9

-3.5
-1.2
0.2
1.5
-2.2
0.8
-0.1
1.4
0.9
-2.2

Chapter 3 Pandemic-responsive methodologies

Country

Shares of
respondents in thesurvey (%)
(A)
9.7
12.2
4.5
7.7
34.1
16.6
18.6
7.3
11.2

Regions
East of England
Greater London
Hokkaido
Tohoku
Kanto
Chubu
Kinki
Chugoku , Shikoku
Kyushu , Okinawa

Japan
(JP)

Shares of the
population (%)
(B)
9.1
13.1
4.4
7.4
34.5
16.9
18.2
6.7
11.9

Deviationof share(%)(A – B)
0.6
-0.9
0.1
0.3
-0.4
-0.3
0.4
0.6
-0.7

3) Respondents
Table 3.4 shows the attributes of all respondents in the six countries. The percentage of female (51.0%)
is slightly higher than that of male (49.0%), which is consistent with the population percentages (i.e.,
female: 50.7%, male: 49.3%). Almost half of the respondents were between 30 and 59 years old (49.6%).
As the evidence of population aging in some developed countries, the share of respondents aged 60 orabove 60 years old (29.6%) was bigger than the share of young respondents below 30 years old (20.8%).
The sample in each country has similar distributions of gender and age to the whole population.
Company employees, self-employed and other unemployed (including pensioners) account for morethan half of all the respondents (total: 64.0%). Most of the respondents have an education backgroundof undergraduate/bachelor (total: 32.9%), followed by college and vocational school (total: 22.5%), andhigh school and senior high school (total: 19.8%).
Table 3. . Distribution of attributes for all respondents in six developed countries
Attributes
GenderAge

Occupation

Education
backgroundDefinition
Male
Female< 30 years old
30–59 years old
60 years old or above
Company employee/self-employed
Civil servants / organizational employees
Faculty and staff of educational institutes
such as schools and universities
Housewife
Part-time job
Student
Other unemployed (including pensioners)
Others
No formal qualification
Elementary school
Middle school/ Junior highschool/Secondary school
High school/ Senior high school
College/ Vocational school
Undergraduate/ Bachelor
Master degree
Doctoral degree

31

AU
(%)
49.0
51.0
23.6
49.7
26.7
38.3
6.5

US
(%)
49.0
51.0
20.7
50.1
29.2
36.1
3.6

NZ
(%)
48.5
51.5
22.4
50.4
27.2
39.3
6.5

CA
(%)
48.6
51.4
20.6
51.1
28.3
38.4
4.7

UK
(%)
49.9
50.1
19.3
52.7
28.0
43.8
6.5

JP
(%)
48.6
51.4
18.5
42.9
38.6
40.2
3.3

Total
(%)
49.0
51.0
20.8
49.6
29.6
39.4
5.2

3.0

4.0

4.4

3.6

3.1

0.7

3.1

7.9
12.4
3.3
23.6
5.0
0.2
2.5

6.5
8.3
4.8
27.8
8.9
0.5
0.5

6.4
10.0
4.4
23.9
5.1
2.8
1.9

5.7
8.0
5.5
28.7
5.4
0.1
0.7

5.9
7.0
3.9
23.6
6.2
1.4
0.5

18.4
10.5
3.2
19.8
3.9
0.1
0.0

8.3
9.3
4.2
24.6
5.9
0.8
1.0

13.6

0.5

13.1

1.4

20.2

2.2

8.6

14.6
26.2
26.7
14.1
2.1

20.4
27.8
26.0
19.9
4.4

14.4
20.1
35.2
10.6
1.9

20.2
31.0
33.7
11.3
1.6

20.2
12.1
30.4
13.0
2.2

29.0
18.3
46.1
3.3
1.0

19.8
22.5
32.9
12.1
2.3

Chapter 3 Pandemic-responsive methodologies

3.2 Modeling application
The dynamic structural equation model and mixed hybrid choice model are developed and applied inchapter 4 and chapter 5 for the behaviors analysis of dynamic activity-travel behavior changes and travel
mode choice respectively, while the Copula model is used in chapter 7 for the joint analysis of the social
contact behaviors.

3.2.1 Modeling of temporal dynamics of behavioral and psychological
adaptations to COVID-19: Dynamic structural equation model (DSEM)

Structural equation models (SEM) are the models that reveal the association between latent variables
by observable indicators (Bielby et al., 1977; Bagozzi et al., 1988; Duncan, 2014). It can help people tomeasure the latent variables indirectly. However, the traditional SEM can’t indicate the dynamic
relationship between individuals’ behavior changes over time. Hence, a dynamic structural equation
model (DSEM) is developed in this study to show how the accumulation of changes in behaviors in thepast could affect the most recent changes in behaviors directly or indirectly through other latentvariables.

3.2.2 Hybrid choice modeling of heterogeneous preparedness and correlated
cultural and psychological factors in response to COVID-19: Mixed hybrid
choice model (XHCM)

Hybrid choice model (HCM) is a widely used model for transport studies that can indicate the impactof the latent variables on the discrete choice (Vij et al., 2014; Kim et al., 2017; Aadity et al., 2021).
However, the HCM structure in previous studies usually ignore the association between latent variablesor the influence of the unobserved heterogeneities caused by individuals’ taste variations. to resolve thisproblem, a mixed hybrid choice model with the association between latent variables (XHCM) isdeveloped in this study. To the best of the authors knowledge, this study is the first time to use a hybrid
choice model to investigate the travel mode choice behaviors with the association between latent
32

Chapter 3 Pandemic-responsive methodologies

variables and individuals’ taste variations simultaneously.

3.2.3 Dependence modeling of multi-faceted social contact decisions under
the impacts of COVID-19: Copula model

The copula-based model is a multivariate functional form to derive the joint distributions of given
marginal distributions (Trivedi et al., 2007). The term copula is first proposed by Sklar (1973) in English,
and the applications of copula have been widely extended to other fields of economic research since thebeginning of 21st century (Nelsen, 2007; Trivedi et al., 2007; Schmidt, 2007). The copula approach
could form the joint distribution and estimate the dependency between random variables without
restrictive distribution assumptions for the specific univariate marginal distributions (Irannezhad et al.,2017; Trivedi et al., 2007; Schmidt, 2007; Meloni et al., 2011). Moreover, compared with othermultivariate correlation methods (e.g., Pearson, Kendall’s tau, Spearman’s rho), copulas can obtain
nonlinear central dependence and tail dependence in both symmetric and asymmetric forms (Frey et al.,
2001; Trivedi et al., 2007).

33

Chapter Capturing temporal dynamics of behavioral and psychological adaptations to COVID-19

CHAPTER

Capturing temporal dynamics of

behavioral and psychological adaptations to
COVID-19.1 Introduction
The COVID-19 pandemic has caused more than 100 million infections and more than 2 million deaths
as of the end of January 2021.6 Before vaccinations were widely available, governments implemented
various behavioral interventions, from forced lockdowns (with penalties for violations) to voluntarychanges in behaviors via communication campaigns. Even though daily new cases started to decline
across the world by the end of the first week of January 2021, the numbers remain higher than 600,000
cases per day. In Japan, the target country of this study, more than 370,000 cumulative infection cases
were confirmed by the end of January 2021, with three peaks observed in April 2020, August 2020, andthe first two weeks of January 2021. Daily new cases increased about three times from the first peak tothe second peak, and about four times from the second peak to the third peak. For policy measures,Japan has mainly relied on “soft” behavioral interventions which request people to change their
behaviors, particularly regarding interactions with social contacts. Strict lockdown has not been adopted.
Some key measures taken from March to September 2020 are summarized below.
March 2: All elementary, secondary and high schools in Japan temporarily suspended until thespring break (April 6)
March 19: Governmental task force releases recommendations on behavioral changes to containthe novel coronavirusMarch 25: The governor of Tokyo requests people to exercise self-restraint (e.g. limit trips outsideof home) on weekends
March 30: The governor of Tokyo further requests people to exercise self-restraint on both
weekends and weekdaysApril 7: State of emergency declared for prefectures with major infection outbreaks
April 9:Government requests companies to telework and stagger commuting times

April 11: Government requests people to reduce trips by at least 70%April 12: Avoid 3Cs (Closed spaces, Crowded places, and Close-contact settings) at workplaceApril 16: State of emergency declared for the whole country
May 4: State of emergency for the whole country extended until the end of May
May 14: State of emergency lifted for 39 prefecturesMay 21: State of emergency lifted for prefectures with major outbreaksMay 25: State of emergency lifted for the whole country
6

https://www.worldometers.info/coronavirus/ (Accessed on January 31, 2021)

34

Chapter Capturing temporal dynamics of behavioral and psychological adaptations to COVID-19







May 26: Public advised to avoid unnecessary inter-prefectural travel until the end of May
June 5: Public recommended to stop overseas trips
June 18: Inter-regional travel restrictions relaxedJune 19: Government releases a smartphone app COCOA (COVID-19 Contact-Confirming
Application) which informs people about physical contacts with infected persons
July 22: Go To Travel Campaign launched
August 28: Government announces goal of providing vaccines to all citizens by the first half ofnext year
Research has shown that human behaviors change over time. Fatigue from behavioral

interventions may weaken the effects of policies. Such behavioral dynamics should be addressed in
COVID-19 policymaking. However, as the review by Zhang et al. (2021a) found, existing efforts arevery limited. This chapter aims to fill this important research gap by analyzing data from a six-monthlife-oriented panel survey of behavioral and psychological adaptations to COVID-19 conducted inJapan using a dynamic structural equation model (DSEM). The latent psychological factors of thereliability of major COVID-19 information sources, risk perceptions about the COVID-19, attitudes
toward policymakers and health professionals and attitudes toward PASS-LASTING based policy
measures are considered in the structure of DSEM.

.2 Data and descriptive analysis
In the survey, 2,643 respondents provided valid answers about behavior changes from April to
September 2020, and psychological factors that may be associated with behavior changes. Behaviors
are captured based on the life-oriented approach (Zhang, 2014, 2017; Zhang and Van Anker, 2017),
focusing on both intercity and intracity behaviors, which are further grouped into different life domains.
Psychological factors include not only subjective evaluations of reliability of COVID-19 information
and risk perceptions, but also attitudes toward policymaking. The attitudes further include subjective
assessments of policymaking capacity related to governments and health professionals, and willingness
to cooperate with policy measures based on the PASS approach (“Prepare-protect-provide”, “Avoidadjust”, “Shift-share”, and “Substitute-stop”) (Zhang, 2020) and the LASTING approach (“Life needs”,“Activity participation”, “Space”, and “Time-timing”) (Zhang, 2021). The PASS approach argues that
COVID-19 policies should be made in a seamless and comprehensive way and the LASTING approachemphasizes the role of people’s willingness to cooperate with policymaking via voluntary behavioral
changes. Before and during the period of April-September 2020, many policies were implemented,
which may or may not have affected the respondents’ behaviors and attitudes. This study focuses on
individual persons, but it is unknown how every individual perceived all of these policies. Therefore,
instead of directly focusing on actual policies common to everybody, the study asked each respondent
to report how he/she perceived existing policies that are classified based on the PASS and LASTING
approaches, i.e., PASS-LASTING policies. The descriptive analysis of modeling factors is introduced
as follows.

35

Chapter Capturing temporal dynamics of behavioral and psychological adaptations to COVID-19

1) Reliability of COVID-19 information sources (RELIABILITY)
10 questions were included to evaluate the reliability of major COVID-19 information sources: i.e.,
official sources from the central government and local governments, authoritative sources from experts
and medical institutes, domestic and overseas news on mass media, sources of SNS (Facebook, Twitter,etc.), search engines (e.g., Google, Yahoo), and information from personal social networks. The
reliability degree is assessed via a 5-point scaling method (1: very low, 2: low, 3: neither, 4: high, 5:very high). As shown in Table 4.1, the information from medical institutes is perceived to be reliable (4:high or 5: very high) by the largest proportion of respondents (64.4%), and the second largest group of
respondents believed that the information announced by local governments (60.3%), the central
government (56.9%), and domestic news (54.6%) are reliable. On the other hand, only 10.5% of
respondents perceive the information from SNS to be reliable, while more than 40% think the SNS
information reliability is low or very low. Experts should have played important roles in helping thecontrol of the pandemic; however, their opinions are trusted by only 38.1% of respondents. This
indirectly suggests that scientific knowledge about COVID-19 is still limited or not publicly acceptable,
and as a result, is not sufficient to convince the majority of the general public.
2) Risk perceptions about the virus infection (RISK_PERCEPT)
Risk perceptions about the virus infection are evaluated by asking each respondent to report how muchhe/she agrees with each of the following eight questions: (1) the risk of infection in the whole of Japanis increasing; (2) the risk of infection in the residence prefecture is increasing; (3) the risk of infectionin the residence municipality is increasing; (4) the risk of infection in the place that he/she often visits(workplaces, schools, supermarkets, restaurants, gyms, etc.) is increasing; (5) the risk of infection in a
crowded train or bus is high; (6) the virus is a very horrific disease; (7) the risk of infection of COVID19 for himself/herself is high; and (8) he/she is susceptible to seasonal flu. Similarly, the assessment
was done based on a 5-point scale (1: strongly disagree, 2: somewhat disagree, 3: neither, 4: somewhat
agree, 5: strongly agree). It is revealed (see Table 4.2) that 67.9% of respondents perceived the risk levelin the whole of Japan to be increasing, while 66.9% thought the COVID-19 virus is very horrific. But
the share of respondents who perceived an increasing level of infection risk in the residence
municipality is more than 26 points lower than those perceiving an increasing level across the wholecountry. Only 28.9% think that places they visit frequently are risky. As expected, a large share (60.1%)
of respondents reported that the risk of infection in a crowded train or bus is high. In spite of widespread
infections across the whole country, only 29.5% perceived the risk of infections for themselves as being
high.

36

Chapter Capturing temporal dynamics of behavioral and psychological adaptations to COVID-19

Table . 1. Evaluation of reliability of COVID-19 information sources
Reliability

Mean

Low orvery low

NeitherHigh orvery highThe information announced by the central government(homepage information, news, public relations

3.5

11.9%

31.2%

56.9%

3.6

9.6%

30.1%

60.3%

Experts’ opinions (through various channels)

3.2

14.3%

47.6%

38.1%

Domestic news

3.5

10.9%

34.5%

54.6%

Overseas news

3.0

19.4%

56.1%

24.5%

Medical institutes

3.7

6.4%

29.2%

64.4%

Workplaces / schools

3.0

16.8%

59.7%

23.5%

2.6

41.0%

48.5%

10.5%

Search engines: Google, Yahoo, etc.

2.9

23.6%

56.8%

19.6%

Personal social network: acquaintances, colleagues, etc.

2.9

23.2%

62.2%

14.6%

magazines, etc.)
The information announced by local governments(homepage information, news, public relations
magazines, etc.)

SNS (Social Networking Service): Facebook, LINE,
Twitter, WhatsApp, etc.

Table . 2. Risk perceptions about the virus infection
Risk perceptions
The risk of infection in the whole of Japan isincreasing
The risk of infection in the residence prefecture isincreasing
The risk of infection in the residence municipality
is increasing

Mean

Somewhat orstrongly disagree

Neither

Somewhat orstrongly agree

3.7

12.2%

19.9%

67.9%

3.5

19.2%

26.0%

54.8%

3.2

25.7%

32.8%

41.5%

3.0

31.3%

39.8%

28.9%

3.6

13.6%

26.3%

60.1%

3.8

12.3%

20.8%

66.9%

3.1

22.6%

47.9%

29.5%

2.3

62.0%

25.3%

12.7%

The risk of infection in the frequently-visitedplaces (workplaces, schools, supermarkets,
restaurants, gyms, etc.) is increasing
The risk of infection in a crowded train or bus is
high
The COVID-19 virus is a very horrific disease
The risk of infection of the COVID-19 virus foroneself is high
The respondent is susceptible to seasonal flu

37

Chapter Capturing temporal dynamics of behavioral and psychological adaptations to COVID-19

3) Attitudes toward COVID-19 policymaking capability (ATTD_CAPACITY)
Attitudes toward policymaking capability were investigated by asking respondents to evaluate the levelsof expertise, enthusiasm, competence, and trust of governments and health/medical institutes, based on
a 5-point scaling method (1: strongly disagree, 2: somewhat disagree, 3: neither, 4: somewhat agree, 5:strongly agree). These attribute measurements are closely related to the capacity of policymakers andhealth professionals. Unfortunately, as shown in Table 4.3, only 21.3% and 48.1% of respondents
thought policymakers and health professionals are capable of dealing with COVID-19. The trust levelin governments is less than 30% and the perceived competence level of governments is only about 20%.
Health and medical institutes were perceived to be capable in terms of trust (48.1%) and expertise(40.7%). It is therefore logical to interpret this lower level of capacity assessment as being related tothe increasing trend of infections in Japan since July 2020.

Table . 3. Attitudes toward COVID-19 policymaking capability
Somewhat
AttitudeMeanor strongly

Somewhat
Neitherdisagree(Expertise) Japanese health and medical institutions
have sufficient expertise for COVID-19(Expertise) Japanese government has sufficientexpertise for COVID-19(Expertise) The local government in the residence
municipality has sufficient expertise for COVID-19(Enthusiasm) Can feel the central government's
enthusiasm in preventing the spread of COVID-19

or strongly
agree

3.1

26.3%

33.0%

40.7%

2.8

36.3%

38.1%

25.6%

2.9

31.5%

44.0%

24.5%

2.7

41.3%

33.3%

25.4%

2.9

32.7%

39.2%

28.1%

2.7

41.2%

37.5%

21.3%

2.8

33.6%

43.1%

23.3%

3.3

18.5%

33.4%

48.1%

(Enthusiasm) Can feel the residence local
government's enthusiasm in preventing the spreadof COVID-19(Competence) The central government has acompetent management system and measures toprevent the spread of COVID-19(Competence) The local government in theresidence municipality has a competentmanagement system and measures to prevent the
spread of COVID-19(Trust) Can trust that Japanese health and medical
institutions can prevent the spread of COVID-19

38

Chapter Capturing temporal dynamics of behavioral and psychological adaptations to COVID-19

Somewhat
AttitudeMeanor strongly

Somewhat
Neitherdisagree(Trust) Can trust that the central government can
prevent the spread of COVID-19

or strongly
agree

2.8

36.7%

37.2%

26.1%

3.0

27.6%

43.4%

29.0%

(Trust) Can trust that the local government in theresidence municipality can prevent the spread of
COVID-19

4) Attitudes toward PASS and LASTING based policy measures (ATTD_PASS_LASTING)
Considering the severity of the threat of this virus, COVID-19 policies should be made in a seamlessand comprehensive way. In line with such considerations, two types of COVID-19 policymaking
methodologies have been proposed in the literature: one is based on the PASS approach (Zhang, 2020)
and the other is the LASTING approach Zhang (2021). The PASS approach argues that COVID-19
policymaking should be made from Prepare-protect-provide (P), Avoid-adjust (A), Shift-share (S), andSubstitute-stop (S) perspectives with respect to various stakeholders. The LASTING approach targets
individual persons and suggests the importance of individuals to adapt needs in life (L) andconsequently participate in activities (A) at locations with proper space (S) and at proper time andtiming (TING), based on individuals’ voluntary behavioral changes. In theory, both approaches areapplicable not only to the transport sector but also other sectors. Here, respondents were asked to reporttheir attitudes toward such policies from their personal viewpoints using a 5-point scaling method (1:strongly disagree, 2: somewhat disagree, 3: neither, 4: somewhat agree, 5: strongly agree). The
questions of PASS and LASTING policy measures are shown in Table 4.4.

Table . . Attitudes toward PASS and LASTING based policy measures

Policy measures
PrepareProtect
PASS

ProvideAvoid

You are well prepared for emergencies like this
COVID-19 pandemic.
In order to prevent yourself or others from infection,
you keep your physical distance, even if you find it isvery inconvenient.
In order to prevent yourself or others from infection,
you are willing to provide your details of behavior
history to medical and government agencies.
In order to prevent yourself or others from infection,
you will avoid dense activities / actions with three Cs(closed spaces, crowded places, and close contact),
even if you find it is very inconvenient

39

Positive(strongly orsomewhatagree)

Negative(strongly orsomewhatdisagree)

31.6%

23.0%

50.9%

10.4%

46.7%

17.8%

66.7%

7.7%

Chapter Capturing temporal dynamics of behavioral and psychological adaptations to COVID-19

Policy measures
Adjust
Shift

Share

Substitut
e

Stop

Life
needs
LASTING

Activity,
Space,
Time and
TimingIn order to prevent yourself or others from infection,
you will adjust your activity / action schedule or plans
In order to prevent yourself or others from infection,
it is better to shift the work style into telework and use
transportation modes other than public transportation,even if you find it is very inconvenient.
In order to prevent yourself or others from infection,
you are totally not reluctant to share your behavior
history and health status information with others
In order to prevent yourself or others from infection,
it's better to switch to online activities, postpone
travel, reduce the number and distances of trips, andstop unnecessary activities, even if you find it is very
inconvenient.
In order to prevent yourself or others from infection,it’s better to stop activities that involve traveling andavoid parties at home, even if you find it is very
inconvenient.
In order to keep yourself or others from infection, you
totally accept changing the needs in your life, andyour lifestyles, even if you find it is very
inconvenient.
In order to keep yourself or others from infection, you
totally accept changing your daily activities, places
and time and timing of activity participation, even if
you find it is very inconvenient.

Positive(strongly orsomewhatagree)

Negative(strongly orsomewhatdisagree)

54.8%

10.8%

41.9%

14.1%

41.4%

19.5%

58.7%

9.3%

65.0%

7.2%

49.5%

13.1%

49.3%

13.4%

Table 4.4 shows that there are more respondents showing a positive attitude than a negative attitude.
The largest group of respondents prefer “avoid” measures (66.7%) and “stop” measures (65.0%),
followed by “substitute” (58.7%), “adjust” (54.8%), and “protect” measures (50.9%). Even though“prepare” measures are less preferred, the share is still larger than 30%. The other three types of PASS
measures are preferred by more than 40% of respondents, respectively. Related to the LASTING
policies, about half of respondents are willing to change the needs in life and lifestyles and the resulting
daily activities, places and time/timing of activity participation. For both PASS and LASTING policies,
the gaps between agreements and disagreements are between 21.9 and 59.0 points, except in the caseof the “prepare” measures (only 8.6 points). All these data suggest that a large number of respondents
recognize the importance of PASS and LASTING policies and are willing to cooperate with COVID19 policy making and implementation. Even though preparedness is extremely important in the fightagainst pandemics, there are still 23.0% of respondents who dislike the “prepare” measures. Information
sharing is also important to control the current pandemic; 19.5% of respondents are totally not reluctant
to share their behavior histories and health status information with others. With regard to social
interactions, negative attitudes may further affect other persons’ behaviors. Thus, it is not unrealistic toassume that such reluctance has strongly hindered the progress of flattening the pandemic curve.

40

Chapter Capturing temporal dynamics of behavioral and psychological adaptations to COVID-19

5) Changes in behaviors
Changes in behaviors were investigated by asking respondents to report whether they changed a certain
behavior in each month of April to September 2020 (during the pandemic), compared with thecorresponding month in 2019 (before the pandemic).
These collected behavior changes come from different domains in life. For instance, the question“Whether have more teleworking and studying at home” belongs to the domain of work. The life choices
also include the behavior changes of social life (avoid having visitors from places with severe infection),family life (avoid visiting family), leisure/recreation (domestic/overseas travel decreased), three Cs
behavior (avoid going out to a crowded place; avoid activities in a closed space where there is contact
with people; avoid talking to people at a close distance) and daily travel behavior (attendance/leaving
time of work became more flexible; travel by public transport decreased; travel by car increased; travel
by walking and biking increased; fewer detour behaviors after leaving workplace or school; lessshopping in stores). The trends of behavior changes targeted in this study are shown in Figure 4.1.
Figure 4.1 reveals a decreasing trend from April to September for most behaviors, indicating that
individuals’ willingness to cooperate in preventing the virus spread declined over time. In other words,
fatigue from behavioral interventions probably occurred. The behavior change percentage of “Domestic
travel decreased” has the largest gap between April and September with 18.5 points, followed by“Travel by public transport decreased” with a gap of 11.6 points. Even though some behavior changes
only showed a small gap (e.g., “Avoid having visitors from places with severe infection”: 0.9 points;“Travel by walking and biking increased”: 1.8 points), it should be noted that they increased first andthen peaked in May: this fluctuating trend led to a very small change, which shouldn’t be ignored. Morethan three quarters of respondents chose the three Cs activities (“Avoid going out to a crowded place”
from 90.0% to 80.2%, “Avoid activities in a closed space where there is contact with people” from 89.8%
to 80.4%; “Avoid talking to people at a close distance” from 81.8% to 76.2%). Other behavior changes
also showed a relatively high change ratio ranging between 20.7% and 62.4%. For instance, “Domestic
travel decreased” and “Travel by public transport decreased” declined from 58.8% to 40.3% and from
61.4% to 49.8%, respectively. Unexpectedly, “More flexible commuting time” (from 23.8% to 20.7%)
and “More teleworking” (from 26.1% to 20.2%) were less preferred.

41

Chapter Capturing temporal dynamics of behavioral and psychological adaptations to COVID-19

Daily travel behavior

Less shopping in stores
Fewer detour behaviors after leaving workplace orschoolTravel by walk and bike increased
Travel by car increased
Travel by public transport decreased

Work

Social Family Leisure and Activities with three
life
life
recreation
Cs

Attendance / leaving time (commuting / returningtime) became more flexible
Avoid talking to people at a close distance
Avoid activities in a closed space where there iscontact with people
Avoid going out to a crowded place
Overseas travel decreased

Domestic travel decreased
Avoid visiting family
Avoid having visitors from places with severe
infection
More teleworking and studying at home0
SeptemberAugustJuly

10 20 30 40 50 60 70 80 90 100
June

May

April

Figure . 1. Changes in behaviors from April to September 2020

42

Chapter Capturing temporal dynamics of behavioral and psychological adaptations to COVID-19.3 Methodology and framework

Reliability of COVID-19
information sources[RELIABILITY]

Risk perception about
the virus infection[RISK_PERCEPT]

Accumulation of changes
in behaviors in the past
[ACCU_CHANGE]The most recent changes
in behaviors[RECENT_CHANGE]

Attitudes toward
COVID-19 policymaking
capability[ATTD_CAPACITY]

Attitudes toward PASS andLASTING based policy
measures[ATTD_PASS_LASTING]

Figure . 2. Conceptual framework about cause-effect relationships

In this study, a dynamic structural equation model (DSEM) is developed, as shown in Figure 4.2. It is
assumed that accumulation of changes in behaviors in the past (ACCU_CHANGE) first affects theformation of various attitudes, and then the attitudes further affect the most recent changes in behaviors(RECENT_CHANGE). ACCU_CHANGE is further assumed to influence RECENT_CHANGE, i.e.,
the existence of state dependence is assumed. In other words, a sequential cause-effect relationship isassumed. This assumption reflects the decision-making mechanisms related to human choice behaviors.
Looking at existing studies in the general literature, almost all of them only investigated a limited
number of behaviors while studies in the field of transportation mainly investigated travel behaviors.
As revealed by Zhang (2014, 2017) from both conceptual and empirical perspectives, people’s various
life choices (e.g., residence, neighborhood, health, education, work, family life, leisure and recreation,
finance, and travel behavior) are interdependent. As a part of life choices, travel behavior is associatedwith other life choices (Zhang and Van Acker, 2017; Zhang and Jiang, 2020). Capturing changes invarious life behaviors caused by the COVID-19 pandemic, it is better to break the existing boundaries
of behavior research between different disciplines. The life-oriented approach provides a theoreticalframework for people-centered cross-sectoral policymaking. In this analysis, ACCU_CHANGE is
measured as the sum of 0-1 variables from April to August 2020 with respect to each behavior.
RECENT_CHANGE indicates a change in September 2020. Among the different types of attitudes,

43

Chapter Capturing temporal dynamics of behavioral and psychological adaptations to COVID-19

first, reliability of COVID-19 information sources (RELIABILITY) is assumed to influence risk
perceptions about the virus infection (RISK_PERCEPT). This is straightforward because humanjudgement/perception relies on information searching. Next, attitudes toward COVID-19 policymaking
capability (ATTD_CAPACITY) are assumed to be associated with RISK_PERCEPT. This is because if
people think policymakers are capable of dealing with COVID-19, then they may think COVID-19 isunder better control and therefore feel a higher level of safety. Furthermore, ATTD_CAPACITY isassumedtoinfluence

attitudes

towardPASSandLASTING

based

policymeasures(ATTD_PASS_LASTING) from an individual perspective. This final assumption is made byconsidering that if people think policymakers are capable of dealing with COVID-19, they are more
likely to cooperate in changing their behaviors voluntarily.

. Result and discussion
The DSEM model in this study was estimated using IBM SPSS Amos Ver 24. In total, 2,643 respondents
were included in the analysis. The generalized least squares (GLS) approach was used for estimation.
The RMSEA is 0.054, which is below 0.08 showing a good fit. If there are various variables included
in a structural equation model, it may be difficult to achieve a GFI (goodness-of-fit) bigger than 0.9;
the threshold of GFI can therefore be loosened to 0.8 (Doll et al., 1994; Seyal et al., 2002). For example,
some scholars also used a result with a threshold of 0.8 for GFI to make excellent studies on transport
policy analysis (Zhang, C. and Liu et al., 2019; Oña et al., 2013). The GFI in this study is 0.804, showing
an acceptable fit for the data.

4.4.1 Standardized direct effects between latent variables
RECENT_CHANGE was originally assumed to be affected by ACCU_CHANGE. However,
introducing such state dependence did not work, in the sense that converged estimation results could
not be obtained. Therefore, the influence of state dependence is ignored. Table 4.5 shows the results ofstandardized direct effects between latent variables, without the effects of state dependence. It is foundthat all these direct effects are statistically significant at 1% or 5% level. This confirms that all the
assumptions made in Figure 4.2 hold in this study. In other words, the conceptual framework formed in
Figure 4.2 is empirically applicable.
First, concerning the influence of ACCU_CHANGE on other latent variables, it is found that it ismost influential to RISK_PERCEPT (0.785) and ATTD_CAPACITY (0.718), in a direct way. The direct
effect of ACCU_CHANGE on ATTD_PASS_LASTING is smallest and the effect on RELIABILITY ismoderate. Second, as for RISK_PERCEPT, there are three direct effects from RELIABILITY,
ATTD_CAPACITY, and ACCU_CHANGE. ACCU_CHANGE shows the largest influence (0.785),
followed by ATTD_CAPACITY (-0.519) and RELIABILITY (-0.155). Third, two direct effects are
44

Chapter Capturing temporal dynamics of behavioral and psychological adaptations to COVID-19

confirmed on ATTD_PASS_LASTING. ACCU_CHANGE shows a larger direct effect (0.423) than
ATTD_CAPACITY (0.102). Finally, among all the direct effects, RECENT_CHANGE is mostly
affected by ATTD_CAPACITY (0.493), followed by RISK_PERCEPT (0.329), and RELIABILITY(0.262). ATTD_PASS_LASTING shows the smallest influence but is still significant.

4.4.2 Standardized total effects between latent variables
Table 4.6 shows the results of standardized total effects between latent variables. First,
RECENT_CHANGE is mostly influenced by ACCU_CHANGE (0.648). Thus, even though the direct
effect from ACCU_CHANGE on RECENT_CHANGE could not be observed, the influence of state
dependence (i.e., the influence of the previous behaviors on the present behavior) exists, but in an
indirect way via attitudinal factors. This result further supports the developed sequential modelingstructure. The second largest total effects on RECENT_CHANGE come from ATTD_CAPACITY(0.331)and

RISK_PERCEPT(0.329),

where

RISK_PERCEPT

ismostlyaffectedby

ATTD_CAPACITY (-0.517). This suggests that policymaking capacity is most influential in inducingchanges in behaviors. ATTD_PASS_LASTING, mostly affected by ACCU_CHANGE, is estimated tobe least (but significantly) influential for RECENT_CHANGE.

Table . . Standardized direct effects between latent variables
Relationships
Influence of ACCU_CHANGE on
RELIABILITY
ATTD_CAPACITY
RISK_PERCEPTATTD_PASS-LASTING
Influence of RELIABILITYRISK_PERCEPTRECENT_CHANGE
Influence of ATTD_CAPACITY on
RISK_PERCEPTATTD_PASS-LASTING
RECENT_CHANGE
Influence of RISK_PERCEPT on
RECENT_CHANGE
Influence of ATTD_PASS-LASTING on
RECENT_CHANGE
Note: ** significant at 5% level and *** significant at 1% level.

45

Parameter

Std. err.

t value

sig.

0.587
0.718
0.785
0.423

0.592
0.540
0.947
0.371

5.315
5.259
4.601
4.622

***
***
***
**

-0.155
0.262

0.047
0.003

-3.441
4.385

***
***

-0.517
0.102
0.493

0.101
0.046
0.006

-7.144
2.288
5.173

***
***
***

0.329

0.003

4.796

***

0.069

.002

2.024

**

Chapter Capturing temporal dynamics of behavioral and psychological adaptations to COVID-19

Table . . Standardized total effects between latent variables
Exogenous variables

ACCU_CH

ATTD_CARELIABILIATTD_PASS-RISK_PEANGEPACITYTYLASTING

RCEPT

0.069

0.329

Endogenous variables
ATTD_CAPACITY

0.718

RELIABILITY

0.587

ATTD_PASS-LASTING

0.497

0.102

RISK_PERCEPT

0.323-0.517-0.155

RECENT_CHANGE

0.648

0.331

0.211

The direct effects shown in Table 4.5 indicate that ACCU_CHANGE mostly affects
RISK_PERCEPT and ATTD_CAPACITY with similar influencing sizes. However, the total effects
suggest that the influencing size of ACCU_CHANGE on ATTD_CAPACITY is 0.718, which is 2.2
times larger than the size on RISK_PERCEPT (just 0.323). Concerning ATTD_PASS_LASTING,
relative influences of ACCU_CHANGE and ATTD_CAPACITY do not change when reflecting indirect
effects: the influencing size of ACCU_CHANGE is more than 4.0 times higher than that of
ATTD_CPACITY. ACCU_CHANGE shows the least direct effect on ATTD_PASS_LASTING;
however, its total effect on ATTD_PASS_LASTING becomes larger than RISK_PERCEPT. Regarding
RISK_PERCEPT, it is not mostly affected by ACCU_CHANGE, as suggested by indirect effects;
instead, it is mostly influenced by ATTD_CAPACITY.

4.4.3 Features of latent variables
Estimation results about the relationships between latent variables and observed variables (i.e.,estimation results of the measurement equations) are shown in Table 4.7 with standardized values. All
the measurement parameters are statistically significant at 1% level (note: one parameter is fixed to be
1 when estimating other measurement parameters for each latent variable).

Changes in behaviors (ACCU_CHANGE

RECENT_CHANGE)As for changes in behaviors in each month, both ACCU_CHANGE and RECENT_CHANGE arecharacterized by "avoid going out to a crowded place” (0.691 | 0.637), “avoid activities in a closed space
where there is contact with people” (0.684 | 0.642), and “avoid talking to people at close distance”(0.677 | 0.615). As shown in Figure 4.1, these behaviors show larger changes over the survey period,
whose shares range between 76.2% and 90.0%. There are four other observed variables with larger
standardized parameter values, including, “travel by public transport has decreased” (0.614 | 0.607),“avoid visiting family” (0.574 | 0.580), “less shopping in stores” (0.565 | 0.496) and “avoid having
visitors from places with severe infection” (0.551 | 0.550).

46

Chapter Capturing temporal dynamics of behavioral and psychological adaptations to COVID-19

Attitudinal attributes
In the case of ATTD_PASS_LASTING, the standardized parameters range between 0.538 (“prepare”
measure) and 0.770 ("adjust” measure). This latent variable ATTD_PASS_LASTING is mostly featured
by people’s attitudes toward “adjust” measure (0.770), “avoid” measure (0.764), and “protect” measure(0.700). The second group with larger parameter values include "substitute” measure (0.697), “stop”
measure (0.649), “life needs” measure (0.668), “activity, space, time” measure (0.681), and “shift”
measure (0.633). The third group includes “share” measure (0.572), “provide” measure (0.563), and“prepare” measure (0.537). Concerning attitudes toward policymaking capacity (ATTD_CAPACITY),
all observed variables of expertise, competence, enthusiasm, and trust related to governments and healthprofessionals are significantly large: i.e., their standardized parameters range between 0.676 and 0.890.
This suggests that all these variables can better represent how people perceive the policymaking
capacity of governments and health professionals. Looking at risk perceptions about the virus infection(RISK_PERCEPT), RISK_PERCEPT seems to effectively capture them, with “the risk of infection in
the whole of Japan is increasing” (0.902), “the risk of infection in the prefecture where you live isincreasing” (0.873), and “the risk of infection in the municipalities where you live is increasing” (0.897).
Furthermore, focusing on reliability of COVID-19 information sources (RELIABILITY),RELIABILITY well presents the reliability of “the information announced by the central government”(0.880) and “the information announced by local governments” (0.882).

Table . . Standardized effects between latent variables and observed variables
Parameter
RELIABILITY
The information announced by the central government (homepage
information, news, public relations magazines, etc.)
The information announced by local governments (homepage information,
news, public relations magazines, etc.)std. err.

0.021

Experts' opinions (through various channels)

0.501

0.022

Domestic news

0.446

0.022

Overseas news

0.414

0.023

Medical institutes

0.537

0.021

Workplaces / schools

0.403

0.021

0.070
0.212
0.159

0.024
0.022
0.021

0.873

0.038

The risk of infection in the residence municipality is increasing

0.897

0.042

0.529

0.039

0.433

0.030

47

44.59
9
22.76
4
19.99
1
17.02
3
25.44
8
16.87
4
2.994
8.855
6.793

***
***
***
***
***
***
***
***
***

0.902

The risk of infection in the residence prefecture is increasing

The risk of infection in the frequently-visited places (workplaces, schools,
supermarkets, restaurants, gyms, etc.) is increasing
The risk of infection in a crowded train or bus is high

sig.

0.880
0.882

SNS (Social Networking Service): Facebook, LINE, Twitter, WhatsApp, etc.
Search engines: Google, Yahoo, etc.
Personal social network: acquaintances, acquaintances, colleagues, etc.
RISK_PERCEPTThe risk of infection in the whole of Japan is increasing

t value

28.66
2
26.61
4
15.77
8
16.62

***
***
***
***

Chapter Capturing temporal dynamics of behavioral and psychological adaptations to COVID-19
Parameterstd. err.

COVID-19 is a very horrific disease

0.464

0.031

The risk of infection of COVID-19 for oneself is high

0.434

0.028

0.212

0.027

The respondent is susceptible to seasonal flu
ATTD_CAPACITY(Expertise) Japanese health and medical institutions have sufficient expertisefor COVID-19(Expertise) The central government of the residence municipality has
sufficient expertise for COVID-19(Expertise) The local government in the residence municipality place has
sufficient expertise for COVID-19(Enthusiasm) Can feel the central government's enthusiasm in preventing the
spread of COVID-19.
(Enthusiasm) Can feel the residence local government's enthusiasm inpreventing the spread of COVID-19.
(Competence) The central government has a competent management system
and measures to prevent the spread of COVID-19.
(Competence) The local government in the residence municipality has acompetent management system and measures to prevent the spread of
COVID-19.
(Trust) Can trust that Japanese health and medical institutions can prevent the
spread of COVID-19(Trust) Can trust that the central government can prevent the spread of
COVID-19(Trust) Can trust that the local government in the residence municipality can
prevent the spread of COVID-19
ATTD_PASS-LASTING
L
Life needs
ASTING

P

0.808

0.031

0.796

0.030

0.836

0.047

0.838

0.045

0.839

0.044

0.890

0.044

0.676

0.034

0.864

0.047

0.860

0.043

***
***
***

38.02
1
37.81
0
28.77
7
30.40
4
29.89
8
31.74
5
31.50
1
29.84
9
31.33
1

***
***
***
***
***
***
***
***
***

0.668
0.681

0.021

Prepare

0.537

0.034

Protect

0.700

0.033

Provide

0.563

0.038

Avoid

0.764

0.035

Adjust

0.770

0.036

Shift

0.633

0.039

Share

0.572

0.039

Substitute

0.697

0.036

Stop

0.649

0.036

A

S

S

48

sig.

0.722

Activity, Space, Time and Timing

ACCU_CHANGE
Attendance / leaving time (commuting / returning time) became more
flexible
Decreased detour behavior after leaving company or schoolMore teleworking and studying at home
Less shopping in stores
Avoid going out to a crowded place
Avoid participating in activities at a closed space where there are directcontacts with people
Avoid talking to people at a close distance
Avoid visiting family
Avoid having visitors from places with severe infection
Travel by public transport decreased
Travel by car increased
Travel by walking and biking increased
Domestic travel decreasedt value
8
17.28
2
16.96
8
9.667

48.21
5
23.36
2
29.39
2
24.30
8
28.86
2
30.20
7
25.24
1
23.77
2
27.71
1
25.35
3

***
***
***
***
***
***
***
***
***
***

0.210
0.417
0.290
0.565
0.691
0.684

0.435
0.280
0.903
0.746

5.957
5.638
5.393
5.484

***
***
***
***

0.716

5.472

***

0.677
0.574
0.551
0.614
0.408
0.441
0.378

0.801
0.860
0.709
0.864
0.494
0.555
0.516

5.450
5.416
5.404
5.434
5.040
5.136
5.098

***
***
***
***
***
***
***

Chapter Capturing temporal dynamics of behavioral and psychological adaptations to COVID-19

Overseas travel decreased
RECENT_CHANGE
Attendance / leaving time (commuting / returning time) became more
flexible
Decreased detour behavior after leaving company or schoolMore teleworking and studying at home
Less shopping in stores
Avoid going out to a crowded place
Avoid participating in activities at a closed space where there are directcontacts with people
Avoid talking to people at a close distance
Avoid visiting family
Avoid having visitors from places with severe infection
Travel by public transport decreased
Travel by car increased
Travel by walking and biking increased
Domestic travel decreased
Overseas travel decreased

Parameter
0.196

std. err.
0.242

t value
3.927

sig.
***

0.379
0.290
0.496
0.637

0.355
0.275
0.723
0.758

6.092
5.652
5.693
5.848

***
***
***
***

0.642

0.740

5.853

***

0.615
0.580
0.550
0.607
0.433
0.441
0.322
0.171

0.719
0.813
0.671
0.677
0.464
0.462
0.439
0.218

5.778
5.783
5.785
5.810
5.349
5.292
5.120
3.697

***
***
***
***
***
***
***
***

0.230

4.4.4 Associations of observed variables with latent variables
It is important to rebuild confidence in using public transport; however, as shown in Table 4.2, morethan 60% of respondents perceive the use of public transport to be risky. The parameter of the influenceof RISK_PERCEPT on “the risk of infection in a crowded train or bus is high” is 0.433. This value is
not that large; however, it sufficiently shows that perceptions of higher risk in using public transport not
only lead to a decline in public transport ridership (as measured by “travel by public transport has
decreased”), but is also associated with major avoidance behaviors, including "avoid going out to a
crowded place”, “avoid activities in a closed space where there is contact with people”, “avoid talking
to people at close distance”, and “avoid visiting family”. A statistically significant connection with“avoid having visitors from places with severe infection” further suggests that risk perceptions aboutpublic transport are not independent of other types of risk perceptions.
Even though ATTD_PASS_LASTING has the least influence on RECENT_CHANGE, it isstatistically significant. The standardized parameter values indicate that policy measures of “avoid”,“adjust”, “protect”, “substitute”, “stop”, and LASTING are mainly attributable to the above avoidance
behaviors.
Furthermore, the above avoidance behaviors are more likely to result from people’s perceptions
about the competence of local governments and their trust in both local and central governments. Thisobservation reaffirms the importance of communication between governments and the general public.
Enhancing the reliability of the information announced by both local and central government seems
necessary. The largest influence of ACCU_CHANGE on RECENT_CHANGE may suggest that policy
interventions should be made as early as possible. Early interventions may help people to form new
habits under the current pandemic. Such new habits may help COVID-19 policymaking work more
effectively.

49

Chapter Capturing temporal dynamics of behavioral and psychological adaptations to COVID-19. Conclusion
Scientifically-sound evidence is crucial to support COVID-19 policymaking. However, existing studies
have neglected the dynamic associations between policymaking and temporal behavior changes. This
chapter has made one of the first attempts in the literature to fill this important research gap. It used anationwide retrospective life-oriented panel survey of behavioral and psychological adaptations to
COVID-19 with respect to 2,643 individuals’ various behavior changes from April to September 2020
in Japan. The collected data had a similar age-gender-region distribution to that of the whole population.
A dynamic structural equation model was developed to quantify the effects of reliability of
information sources, risk perceptions, attitudes toward COVID-19 policymaking capability and PASSLASTING based policies on the most recent changes in behaviors. The above psychological factors
were further explained by accumulated behavior changes in the past. Capturing such dynamic
relationships, and the focus on policymaking, differentiates the current study from existing studies on
COVID-19. The major findings are summarized as follows:
1) More than 60% of respondents perceived that the risk of infection was increasing across thewhole country, and that risk of infection in a crowded train or bus is high. Even though about 70% of
respondents perceived that the COVID-19 virus is very horrific, less than 30% reported that they felt
the places they frequently visited were risky, and less than 40% perceived that the risk in their residence
municipalities is increasing. Such a low perception ratio may be because less than 30% of respondents
think the risk of infection of the COVID-19 virus for themselves is high.
2) The DSEM estimation results confirmed a statistically-significant sequential cause-effectrelationship between accumulated behavior changes in the past, psychological factors, and the mostrecent behavior changes. In other words, accumulated behavior changes in the past affect all thesubjective psychological factors, which further influence the most recent behavior changes. Suchsequential dynamics reflect human decision-making mechanisms.
3) The DSEM estimation results indicate that the various behavior changes are mostly
characterized by avoidance behaviors: avoid going out to a crowded place; avoid participating in
activities at a closed space where there are direct contacts with people; avoid talking to people at a close
distance; and avoid visiting family.
4) The most recent behavior changes are most affected by accumulated behavior changes in thepast, in an indirect way, because the direct effects are not observed but the largest total effects are
confirmed. This observation further supports the assumed sequential relationships in Figure 4.2.
5) Effects of subjective assessments of policymaking on the most recent behavior changes are
significant but moderate. Within the attitudes toward policymaking, attitudes toward policymaking
capacity are more influential than willingness to follow PASS-LASTING based policy measures. At thesame time, it is also estimated that people who perceive policymakers to have higher expertise level, be
more enthusiastic and competent, and to be more trusted, are more likely to follow PASS-LASTING
50

Chapter Capturing temporal dynamics of behavioral and psychological adaptations to COVID-19

based policy measures.
6) High risks of using public transport are found to have a significant influence on the most recent
behavior changes, together with other risk perception factors. Meanwhile, a large association between
accumulated behavior changes and risk perceptions is also revealed.
7) The information announced by authorities (i.e., central/local governments, experts, medical
institutes) has the most influence on people’s perception of information reliability. Individuals’ risk
perceptions at the country and city levels are more likely to affect whether people change their behaviors.
Having emphasized the significant findings from this study, its shortcomings and corresponding
improvements for the future should be mentioned. First, as this case study was conducted in Japan, thefindings may be specific to Japan. It is desirable to conduct further studies in other countries in order tomake international comparisons. Second, this study adopts a sampling-based analysis and as a result,the findings may not be generalizable. Accordingly, it is important to integrate this sampling-based
analysis with Big Data and Open Data based analyses. Third, policymaking is addressed in this study
via the help of psychological factors, which are crucial to better reflect personal feelings about
policymaking processes. However, detailed policy measures should be directly examined, even though
this involves the difficult task of assessing the impacts of policy decisions, made at an aggregate level,on every individual. Last but not least, different countries have taken various policy measures. Somemeasures are common, but there are also many unique measures. Broad international comparative
research should be promoted, with the support of sufficient research funds. Such investments inscientific research are crucial to prevent and control future pandemics.

51

Chapter Influences of preparedness and correlated cultural and psychological factors on choice
behaviors in response to COVID-19: A case study on travel mode choices

CHAPTER

Influences of preparedness andcorrelated cultural and psychological factors on
choice behaviors in response to COVID-19: A
case study on travel mode choices.1 Introduction
The COVID-19 pandemic has caused various changes to the transport sector and as a result, enormous
efforts have been made to address such changes (e.g., Rothengatter et al., 2021; Zhang et al., 2021b;
Zhang and Hayashi, 2022a,b). Even though there are a rich body of studies on transport and COVID19, there are still many unresolved research issues, among which travel mode choices are an underresearched topic. Therefore, this study focuses on the travel mode choices under the COVID-19
pandemic.
Decline in public transport and increase in car and active transport have been commonly observedin countries all over the world on one hand, while existing studies have mainly targeted a specificcountry on the other, leading to the observed factors affecting travel mode choices to be largely
dependent on the targeted country. The risk of COVID-19 infection is obviously the key factor thatshould be influential to travel mode choices; however, the reality suggests that the risk perception is notalways the same among people. Unfortunately, such a heterogeneity has not been well investigated fromperspectives of theoretical thinking and improvements of both survey and modeling methods. To
enhance people’s proper perception about infection risk, efforts of both health/medical agencies andgovernments are crucial but unfortunately, such efforts have not been always highly evaluated bygeneral public (Zhang, 2021). In this regard, the role of trust in health/medical agencies andgovernments has remained ill explored. Risk communication is essential to the pandemic policymaking;however, existing communication practices are often ineffective and sometimes even improper (Zhang,2021). There are various reasons for such an inconvenient practice, among which the ignorance ordisrespect of cultural aspects related to people’s decisions on the way to be involved in the pandemic
policymaking process may be one of the reasons. While preparedness is crucial to survive frompandemics (Zhang, 2020; Zhang et al., 2021b), it is unclear whether preparedness is helpful or not. In
the case of travel mode choices, travel habit has been long recognized to be a key factor affecting travel
52

Chapter Influences of preparedness and correlated cultural and psychological factors on choice
behaviors in response to COVID-19: A case study on travel mode choices

behavior. Such a habit, especially formed during influenza seasons is a kind of preparedness; however,such during-influenza risk-concerned travel habits formed before the COVID-19 pandemic have notbeen investigated in the context of COVID-19.
Keeping the above unresolved research issues in mind, this study makes several initial attempts torevisit factors affecting the travel mode choices under the COVID-19 pandemic, to the best of authors’knowledge. First, this chapter examines the factors to travel mode choices by combining data (valid
sample: 7,245 respondents) collected from six developed countries (Australia, Canada, Japan, New
Zealand, the US, and the UK) in March-April 2021. Second, cultural risk factors, classifying people
into four types, are introduced to the analysis. Third, the travel habits (corresponding to different risk
levels) formed for the survival during influenza seasons before the COVID-19 pandemic areincorporated into the analysis. Fourth, the “risk perception” – “trust” – “cultural risk factors”
relationships are jointly reflected in the representation of travel mode choices based on a mixed hybrid
choice model, where a mixed logit model is adopted to properly reflect the influences of unobserved
heterogeneities (or taste variations) related to key travel attributes. Combining data from the six
countries is expected to better capture the role of cultural risk factors in people’s decisions on travel
mode choices. The above modeling analysis is conducted with respect to major travel purposes(work/study, shopping, eating out, physical exercise, party, cultural leisure, and medical activities).

.2 Data and descriptive analysis
A comparative survey of activity-travel and social contact before-during COVID-19 was conducted in
all the six developed countries (Australia, Canada, Japan, New Zealand, the US, and the UK), which
includes daily travel mode choices with respect to the following main travel purposes (or activities):
work/study, shopping (at supermarket/shopping mall), eating out (at restaurants), physical exercise (atindoor places (e.g., gym)), party (at indoor public places (e.g., bar)), cultural leisure (e.g., visiting
cinema, concert hall, etc.), and medical activities (e.g., see a doctor, purchase medicine). Travel mode
choices are asked with respect to both before and during the COVID-19 pandemic. The beforepandemic period refers to popular influenza seasons when people may also need to pay attention to be
infected by influenza viruses. Travel modes contain shared mobility (Uber/DiDi/Grab, etc.), taxi, bus,rail (train, metro, street car, etc.), private car, and active transport (walk, bicycle).
During the COVID-19 pandemic, some individuals may totally stop several activities as aprotective measure (Zhang, 2020). Because this study investigates travel mode choices, data without
out-of-home activities are excluded. As a result, the sample sizes for different travel purposes are 3,776
respondents for work/study; 7,042 for shopping; 4,412 for eating out; 3,366 for physical exercise; 2,998
for party; 3,039 for cultural leisure; and 5,545 for medical activities. Obviously, during the pandemic,
there were more people who worked/studied from home and reduced the participation to party, culturalleisure and physical exercise. The smallest reduction was shopping. In this study, travel mode choices
53Chapter Influences of preparedness and correlated cultural and psychological factors on choice
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are analyzed for different travel purposes, separately.
Table 5.1 shows the distributions of respondents’ gender, age, and number of household membersby travel purpose. Male respondents performed more daily activities than female during the COVID19 pandemic, except shopping and medical activities. Among all age groups, young adults (20–29 years
old) showed the largest share of participating in out-of-home activities, followed by the 30–39 group.
The traveling share was the smallest for the 15–19 group, followed by the groups aged 60 and above.
Among all household groups, the respondents living in a two-member household account for the largest
share of the participation in out-of-home activities, while the one-member, three-member and fourmember households have similar shares.
Table . 1. The sample distributions of gender, age, and number of household members by travel
purposes
Travel purpose
Individual andhousehold attributes
FemaleGender
Male
15–19
20–29
30–39
40–49
Age
50–59
60–64
65–69
70 and above
1
2
Number of3household4
members
5
6 and above

Work/
Study
(%)
46.3
53.7
2.9
26.8
21.8
19.9
16.2
4.7
3.5
4.3
21.0
27.0
20.9
20.7
6.8
3.5

Shopping
(%)
51.0
49.0
1.9
18.8
16.7
16.2
16.8
7.1
8.3
14.1
21.0
36.0
19.2
15.7
5.3
2.9

Eating
out
(%)
48.5
51.5
2.3
23.3
19.3
17.0
14.9
5.1
6.4
11.8
18.3
33.8
20.3
18.3
6.0
3.3

Physical
exercise
(%)
45.2
54.8
2.6
26.7
21.7
17.2
13.3
4.3
5.4
8.8
17.0
30.2
21.5
20.6
6.5
4.2

Party
(%)
44.7
55.3
2.5
27.9
22.1
18.3
13.1
3.7
4.6
7.9
16.8
30.5
21.4
20.8
6.5
4.0

Culturalleisure
(%)
45.3
54.7
2.4
27.6
22.3
17.3
13.1
4.1
4.3
8.9
20.0
27.9
21.4
20.2
6.4
4.1

Medicalactivities
(%)
50.6
49.4
1.9
19.1
16.7
15.7
15.9
6.7
8.5
15.3
20.5
36.1
19.1
16.2
5.3
2.9

The descriptive analysis of modeling factors is introduced in subsequent sections.

5.2.1 Choices of travel modes
In this retrospective survey, respondents were asked to report their main travel modes for different travel
purposes (=activities), including shared mobility (Uber/ DiDi/ Grab, etc.) and taxi, bus, rail (train, metro,streetcar, etc.), private car, and active transport (walk, bicycle) for both before and during the COVID19 pandemic.
Figure 5.1 shows the travel mode choice shares during the pandemic. Private car accounts for a
preponderant share of 59.0% or above for all travel purposes, where the share even reaches 73.1% for
shopping. The second largest shares are observed with respect to active transport, between 11.0% (forparty) and 19.6% (for physical exercise), which are however much smaller than the car shares. The third
54

Chapter Influences of preparedness and correlated cultural and psychological factors on choice
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largest shares are related to rail, ranging between 3.6% (for shopping) and 10.6% (cultural leisure). The
shares of shared mobility and taxi and bus are similar.

Medical activities

5.3

6.2

Cultural leisure

4.9

7.1

10.6

65.9

11.5

Party

6.1

6.9

10.4

65.6

11.0

Physical exercise

4.3

6.9

Eating out

4.7

5.7

Shopping

4.0 4.3 3.6

Work/study

5.3
0%

6.0

68.9

8.5

60.7

7.1

7.7
10%

13.6

19.6

71.2

11.3

73.1

9.9
20%

15.0

59.0
30%

Share mobility & taxi

40%
Bus

50%
Rail

18.1
60%

Private car

70%

80%

90%

100%

Active transport

Figure . 1. Choices of travel modes by travel purpose during the COVID-19 pandemic

Table 5.2 shows the changes (measured by a percentage value) in travel mode choices and trip
reductions before and during the COVID-19 pandemic. It is found that the pandemic led to an obvious
reduction of people’s daily trips, especially for leisure purposes (cultural activities: -31.6%, eating out:
-29.8%, party: -29.4%, physical exercise: -22.4%). The shopping trips show the least decline (-6.0%),
followed by medical activities (-9.1%) and work/study (-9.4%). Trips of shopping, commuting andmedical activities decreased less than other leisure trips, which may be because these three kinds oftrips are essential to people. About the travel mode changes, it is observed that the largest decline inmode choice shares was the use of rail for cultural leisure (-51.6 percentage points) and the least changes
were the use of private car for work/study (-1.8 points) and shopping (+1.8 points). Larger declines
were found with respect to private car for cultural leisure (-48.4), bus for cultural leisure (-46.4), sharedmobility and taxi for party (-45.2) and rail for party (-45.0). Among all travel modes, public transportmodes (bus and rail) showed the largest declines. Comparing all travel purposes, cultural leisure andparty showed the largest declines in all travel modes. Looking at those travel modes with an increased
share, they are active transport for shopping (+10.8) and for work/study (+8.3) and shared mobility andtaxi for medical activities.

55

Chapter Influences of preparedness and correlated cultural and psychological factors on choice
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Table . 2. Changes in travel mode choices and trip reduction before and during the COVID-19
pandemic
SharedTravel purpose

mobility &taxi (%)

Bus

Rail

Private car

Active

(%)

(%)

(%)

transport (%)

Trip
reduction(%)

Work/study

-17.4

-36.5

-36.6

-1.8

8.3

-9.4

Shopping

-18.2

-24.2

-28.6

1.8

10.8

-6.0

Eating out

-31.9

-43.9

-27.8

-31.6

-21.6

-29.8

-15.2

-34.5

-22.0

-27.9

-20.6

-22.4

Party

-45.2

-32.5

-45.0

-41.6

-39.6

-29.4

Cultural leisure

-34.8

-46.4

-51.6

-48.4

-36.5

-31.6

5.4

-27.8

-24.6

-12.2

-17.3

-9.1

Physical
exercise

Medicalactivities

Note: The values (percentage points) are the shares during COVID-19 minus those before COVID-19.

5.2.2 Psychological risk factors
1) Risk perception
Risk perception has been revealed to affect people’s various behaviors performed during the COVID19 pandemic (e.g., Chan et al., 2020; Ding and Zhang, 2021; Zhang, 2021). In the survey, individuals’
risk perceptions about the infection of COVID-19 are measured at the following four spatial levels: (1)in the whole country, (2) in the residence municipality, (3) at respondents’ frequently-visited places (e.g.,
workplaces, schools, supermarkets, restaurants, gyms), and (4) inside crowded public transport vehicles,
by asking the following question, where a five-point Likert scale (i.e., 1: strongly disagree, 2: somewhat
disagree, 3: Neither agree nor disagree, 4: somewhat agree, 5: strongly agree) is adopted.
o what extend do you agree or disagree to each of the following statements related to COVID-19?

1.

The COVID-19 outbreak in your residence country is severe than many other countries.

2.

The infection risk of COVID-19 in your residence country is high3.

The infection risk of COVID-19 in your residence region (prefecture, province,state) is high4.

The infection risk of COVID-19 in your residence municipality is high5.

The infection risk of COVID-19 in the place where you often go (workplaces,
schools, supermarkets, restaurants, gyms, etc.) in your daily life is high6.

The infection risk of COVID-19 in crowded train or bus is highTo ensure the fit of model, only the question 2, 4, 5 and 6 are included in the model, the56

Chapter Influences of preparedness and correlated cultural and psychological factors on choice
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distributions of these questions are summarized in Figure 5.2.
As shown in Figure 5.2, most of the respondents thought that the infection risk in crowed public
transport vehicles increased when performing all types of activities during the COVID-19 pandemic(the “agree” (strongly agree or somewhat agree) responses range from 59.9% to 66.8%). The “agree”
shares for high-risk perception in the whole country are from 43.7% to 50.7% (especially forwork/study). The “agree” shares are obviously higher than the shares of “disagree” (strongly disagree
or somewhat disagree) in crowed public transport vehicles and the whole country; however, thedifferences between “agree” and “disagree” are minor in the residence municipality or those frequently
visited places in people’s daily lives. The respondents who had a travel for work/study (agree: 41.2%,
disagree: 35.5%) and physical exercise (agree: 39.5%, disagree: 39.1%) thought the risk of infection in
the residence municipality increased. However, for respondents travelling for other purposes, more
people disagree that the risk of infection increased during the pandemic (agree: from 34.7% to 38.2%,
disagree: from 41.2% to 44.1%). More respondents who had a travel for work/study (agree: 43.2%,
disagree: 31.9%), physical exercise (agree: 39.8%, disagree: 35.4%), party (agree: 40.0%, disagree:
36.8%) and cultural leisure (agree: 40.6%, disagree: 36.1%) thought that their frequently-visited places
were riskier during the COVID-19 pandemic, while interestingly, those people travelling for eating out
are less likely to think that it is riskier at the restaurants they often visited (agree: 34.7%, disagree:
40.2%). The shares of “agree” and “disagree” for the risk in frequently visited places do not show
significant differences for shopping and medical activities. Thus, both similarities and dissimilarities
are observed with respect to the risk perception at the four spatial scales, which motivated us to make

Frequently-visited Crowded public
placestransport vehicles

use of all of them in the modeling modeling analysis.

strongly agree

somewhat agree
neither agree nor disagree
somewhat disagreestrongly disagreestrongly agree
somewhat agree
neither agree nor disagree
somewhat disagreestrongly disagree

Residencemunicipality

strongly agree
somewhat agree
neither agree nor disagree
somewhat disagree

Whole country

strongly disagreestrongly agree
somewhat agree
neither agree nor disagree
somewhat disagreestrongly disagree
0.0%
Medical activities

5.0%
Cultural leisure

10.0%
Party

15.0%

20.0%

Physical exercise

25.0%
Eating out

30.0%

35.0%

Shopping

Figure . 2. The distributions of risk perception scores

57

40.0%
Work/study

45.0%Chapter Influences of preparedness and correlated cultural and psychological factors on choice
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2) Trust in governments and health/medical agencies
Governments and health/medical agencies are the key decision makers in the fight against COVID-19.
People’s trust in these decision makers has been influential to behavioral adaptation to the COVID-19
pandemic (Laato et al., 2020b; Sibley et al., 2020; Zhang, 2021). In this survey, the following question
is prepared to measure people’s trust in governments and health/medical agencies, using the same fivepoint Likert scale, as described above.
o what extend do you agree or disagree to each of the following statements related to COVID-19?

1.

You can trust your residence country's health and medical institutions in preventing the
spread of COVID-19.

2.

You can trust your residence country's central government in preventing the spread of
COVID-19.

3.

You can trust your residence municipality's local government in your residence place inpreventing the spread of COVID-19.

As shown in Figure 5.3, most of the respondents agreed that they trusted governments andhealth/medical agencies in preventing the spread of COVID-19 in the sense that the “agree” shares
related to health/medical agencies range from 63.6% to 67.7%, central government from 59.7% to64.4%, and local government from 59.9% to 64.2%. Only less than 20% of the respondents had anegative attitude (distrust) to governments and health/medical agencies. The “agree” shares of the trustin health/medical agencies are slightly higher than that to central and local governments, while the
distrust in central government (from 15.7% to 20.3%) is slightly more obvious than that to local
government (from 14.6% to 16.2%). People who had a travel for shopping (agree: 66.7%) and eatingout (agree: 66.3%) have a slightly bigger share of trust in health/medical agencies than people withother travel purposes. People who traveled for party (central government: 64.4%; local government:
64.2%) and cultural leisure (central government: 63.8%; local government: 63.7%) are more likely toshow a trust in governments.

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Chapter Influences of preparedness and correlated cultural and psychological factors on choice
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Local government

strongly agree
somewhat agree
neither agree nor disagree
somewhat disagreestrongly disagree

Central government

strongly agree
somewhat agree
neither agree nor disagree
somewhat disagree

Health/medical agencies

strongly disagreestrongly agree
somewhat agree
neither agree nor disagree
somewhat disagreestrongly disagree
0.0%
Medical activities

5.0%

10.0%

Culture leisure

15.0%
Party

20.0%

25.0%

Physical exercise

30.0%
Eating out

35.0%
Shopping

40.0%

45.0%

50.0%

Work/study

Figure . 3. The distributions of trust scores

5.2.3 Cultural risk factors
To mitigate the impacts of risk perception on people’s behavioral adaptions to the COVID-19 pandemic,
it is necessary to recognize the existence of people’s various heterogeneities related to risk, especiallyknowing the empirical evidence that differentiated risk communication is crucial for controlling the
pandemic effectively (Zhang, 2021). In this regard, the grid-group cultural theory of risk (Thompson etal., 2018; Wildavsky et al., 1987; Wildavsky et al., 1990) becomes relevant to this study, which classifies
people into four types: hierarchist, egalitarian, individualist, and fatalist. Previous research assumed
that an individual should belong to one of these four types; however, in reality, it may be difficult toclassify him/her into one type. Instead of identifying one specific type, this study asked respondents tojudge by themselves. To this end, the following question is asked in this study to capture such culturalrisk factors, using the same five-point Likert scale, as described above, where the first statement is about
hierarchist, the second about egalitarian, the third about individualist, and the last about fatalist.

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Chapter Influences of preparedness and correlated cultural and psychological factors on choice
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o what extend do you agree or disagree to each of the following statements related to COVID-19?

1.

You are a person who leaves important decisions to experts or government and support
the social order.

2.

You agree with that everyone is equal and the good of the many people comes before
the good of any individual, the risk decision should be made by all people instead of asmall elite or authority.

3.

You agree with that individual freedom is absolutely important, and individual choices
should not be constrained by society and other people?

4.

You agree with that there is little you can do to control the environment so that you will
receive whatever fate throws at you and try not to know or worry about it.

Figure 5.4 shows the shares of the four types of cultural risk factors or cultural orientations. There
are more respondents showing a tendency of being an egalitarian (66.6%–69.3%) or a hierarchist(57.6%–60.0%) than those being an individualist (46.6%–54.5%) or a fatalist (42.2%–52.0%).
Hierarchists and egalitarians have no obvious differences across travel purposes (less than 4 percentage
point). In contrast, individualists and fatalists are mainly observed with respect to party (individualists:
54.5%; fatalists: 52.0%). Similar distributions of “agree” and “disagree” are found for the pairs ofegalitarians-hierarchists and individualists-fatalists. Thus, there are more respondents showing agroup/altruism orientation than those with ego-oriented cultural risk factors. On the other hand,egalitarians and hierarchists should be opposite to each other, as argued in the grid-group cultural theoryof risk; however, the response overlaps between egalitarians and hierarchists were found, suggestingthat a person can be an egalitarian and a hierarchist simultaneously. It is hard to define a boundary
between egalitarians and hierarchists, or between individualists and fatalists in reality. It is therefore
important to conduct more comprehensive analyses of the cultural theory of risk. This study attempts
to provide additional insights related to the cultural risk factors in the context of COVID-19 andtransport.

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Chapter Influences of preparedness and correlated cultural and psychological factors on choice
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strongly agree

Fatalists

somewhat agree
neither agree nor disagree
somewhat disagreestrongly disagree

Individualists

strongly agree
somewhat agree
neither agree nor disagree
somewhat disagreestrongly disagree

Egalitarians

strongly agree
somewhat agree
neither agree nor disagree
somewhat disagreestrongly disagree

Hierarchists

strongly agree
somewhat agree

neither agree nor disagree
somewhat disagreestrongly disagree
0.0%
Medical activities

5.0%

10.0%

Culture leisure

15.0%

Party

20.0%

Physical exercise

25.0%
Eating out

30.0%

35.0%

Shopping

40.0%

45.0%

Work/study

Figure . . The distributions of cultural risk factors by travel purpose

5.2.4 Pre-pandemic preparedness
Travel mode choices made before the pandemic are regarded as an indicator of travel habit, which is
expected to affect travel behavior in the present. Generally speaking, people tend to avoid extra thinking
and repeat some travel behaviors (e.g., travel mode choices) in daily life (González et al., 2017, Gao etal., 2020; Haggar et al., 2019). This kind of phenomenon is known as travel habit (Gärling
2003, Lanzini

Axhausen,Khan, 2017), which is a spontaneous behavioral reaction caused by individuals’

psychological inertia (Sommer, 2011; Haggar et al., 2019). Such an inertia is built by people’s travel
experience for a long time (Kitamura

Van Der Hoorn, 1987). As a result, the inertia is difficult tochange and plays a crucial role in affecting individuals’ choices of travel modes (Cherchi

Manca,

2011; Gao et al., 2020; González et al., 2017).
To analyze the effects of pre-pandemic preparedness on individuals’ travel mode choices during
the pandemic, risk-concerned travel mode choices made before the pandemic (i.e., during influenza
61

Chapter Influences of preparedness and correlated cultural and psychological factors on choice
behaviors in response to COVID-19: A case study on travel mode choices

seasons) are classified into three types: the travel habits with high risk, middle risk, and low risk (Zafri
et al., 2022). Public transport (bus and rail) is regarded to have high risk because people may havenumerous direct or indirect contacts with others in the same vehicle. Shared mobility and taxi areclassified to have middle risk because even though users may contact some others, but the number ofin-vehicle social contacts can be controlled. Using a private car, an individual does not need to contact
any others expect their companions they know. Active transport (walk, cycling) is conducted in an open
outdoor environment. Hence, private car and active transport are regarded to have low risk. Figure 5.5
shows the distribution of travel habits. It is found that most people have a travel habit with low risk
before the pandemic for all travel purposes. This is most obvious for shopping (83.7%) and medical
activities (82.2%). The travel habit with high risk accounts for a bigger share in the cases of work/study(21.3%), cultural leisure (17.4%), party (17.5%) and physical exercise (16.4%).

Medical activities

12.2

5.6

82.2

Cultural leisure

17.4

9.4

73.2

Party

17.5

9.2

73.3

Physical exercise

16.4

6.1

77.5

Eating out

14.7

7.3

78.0

Shopping

9.5

Work/study

6.8

83.7

21.3
0%

10%

9.7
20%

69.0
30%

40%

high risk

50%

middle risk

60%

70%

80%

90%

100%

low risk

Figure . . The distributions of risk-concerned travel habits (or pre-pandemic preparedness) bytravel purpose

5.2.5 Travel attributes
For a proper understanding of travel mode choices, travel time and travel companion are further
investigated in the survey, by reflecting the findings from existing references (Chen et al, 2022;
Efthymiou et al., 2016; Guo et al., 2020; Ho and Mulley, 2015; Weiss and Habib, 2020; Wu et al., 2011).
1) Travel time
In the survey, travel time was measured using the following four categorical scales: (1) less than 10
mins, (2) 10 mins – 30 mins, (3) 30 mins – 1 hour, and (4) 1 hour or longer. The distribution of travel
62

Chapter Influences of preparedness and correlated cultural and psychological factors on choice
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time is shown in Table 5.3. The second category shows the largest shares for all travel purposes,
following by the first category for the travel purposes other than party and cultural leisure. There are
more than 10% of respondents who had to take 1 hour or longer to go to workplace or go to school.
There are more medical activities conducted within a travel time less than 10 minutes than the travel
time between 30 minutes and 1 hour. Activities mainly conducted within a travel time less than 30 mins
were shopping (82%), medical activities (74.1%) and eating out (73.2%). Activities needing a travel
time longer than 30 mins include work/study (36.1%), party (36%) and cultural leisure (37.9%). In themodeling analysis of this study, each category of travel time was transformed into a continuous value
for the chosen travel mode and for the unchosen travel modes, their travel times were calculated as themean of travel time of all other respondents who lived in same area with him/her.
Table . 3. The distribution of travel time during the COVID-19 pandemic
Travel timecategories

Work/
Study
(%)

Shopping

PhysicalExercise
(%)

Party

(%)

Eating
out
(%)

(%)

Culturalleisure(%)

Medicalactivities(%)

Less than 10 mins

26.5

35.7

23.9

27.7

20.9

17.0

28.5

10 mins – 30 mins

37.4

46.3

49.3

42.1

43.1

45.1

45.6

30 mins – 1 hour

25.5

14.6

21.6

23.4

27.0

29.4

18.5

1 hour or longer

10.6

3.4

5.2

6.8

9.0

8.5

7.4

includethree2) Travel companion
Travelcompanions

types:

travelwithfamily(including

spouse/couple,

children/grandchildren, parents/grandparent), travel with friends (including close friend, otheracquaintances), and travel alone. Respondents need to make a choice from these three options. As shownin Figure 5.6, during the COVID-19 pandemic, 60.1% of the respondents traveled alone for medical
activities (60.1%) and 50.6% traveled alone for shopping, while 62.0% traveled with family for eatingout. Other larger shares of traveling with family are cultural leisure (51.5%) and party (48.9%), followed
by traveling with family for shopping (42.0%) and traveling alone for physical exercise (41.3%). As fortraveling with friends, the largest share was found with respect to party (33.8%), while the smallest
share was travel for shopping. Comparing all types of travel companions, traveling with family is more
popular during the pandemic.

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Chapter Influences of preparedness and correlated cultural and psychological factors on choice
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Medical activities

30.4

9.5

Cultural leisure

51.5

Party

48.9

Physical exercise

60.1

24.9

33.8

38.8

Eating out

41.3

21.3

42.0
0%

10%

20%

17.3

19.9

62.0

Shopping

23.6

7.4
30%

40%
family

16.7

50.6
50%

friend

60%

70%

80%

90%

100%

alone

Figure . . The distributions of travel companions by travel purpose during the COVID-19
pandemic.3 Methodology and framework
5.3.1 Framework of XHCM
Here, a hybrid choice model (HCM) with three correlated latent variables (risk perception, trust, andcultural risk factors) is built to analyze travel mode choices under the impacts of COVID-19, where thestructural equations represent the endogenous relations between the three latent variables are introduced
to the utility equation together with during-pandemic travel attributes, pre-pandemic preparedness andindividual/household attributes. To capture heterogenous influences of preparedness and travel
attributes, a mixed logit model is adopted in HCM. As a result, a mixed hybrid choice model (XHCM)
is built in this study, as shown in Figure 5.7, where both direct and indirect effects of the latent variables
are incorporated.

64Chapter Influences of preparedness and correlated cultural and psychological factors on choice
behaviors in response to COVID-19: A case study on travel mode choices
Psychological/cultural risk
factors
Risk
perception

During-pandemic travel
attributes- Wholecountry- Residence
municipality

Travel time
Travel companion- with family- with friend

utility

Trust- Centralgovernment- Localgovernment

Pre-pandemic preparedness
Travel mode with high risk
Travel mode with middle risk
(reference for estimation)- Frequentlyvisited places- Crowded
publictransport
vehicles- Health/medical
agencies

Individual/household
attributes
GenderAge
Number of household
members

Choice of travel modes

Travel mode with low risk

Car

Activetransport
Rail

Bus

Culturalrisk factors- hierarchists- egalitarians- individualists- fatalists

Shared mobility or taxi
(reference alternative for estimation)

Figure . . A mixed hierarchical hybrid choice model (XHCM)

Existing studies showed that individuals’ attitude factors are affected by cultural variables (Verweij,
2005; Nan et al., 2014; Atchley et al., 2014), and people’s attitude toward agency is significantly
associated with risk perception (Ding and Zhang, 2021; Prati et al., 2011; Siegrist et al., 2003).
Considering the above existing evidence and its unconfirmed effects in the case of travel mode choices
under the COVID-19 pandemic, Figure 5.7 adds a link from cultural risk factors to trust and a link fromtrust in risk perception. Thus, the structure of HCM is used to capture the endogenous relations between
these three psychological/cultural risk factors.
Risk perception and trust in agency’s risk management are two crucial components of thepsychological analysis of risk (Slovic et al., 1991; Slovic, 1993; Dedeoğlu et al., 2021; Liu et al., 2020;
Alaszewski, 2003). Trust and risk perception are the mirror image to each other (Das et al., 2004). In
this study, risk perception is represented by four observed variables measured at four spatial levels: thewhole country, residence municipality, frequently visited places, inside public transport vehicles. Trustis measured as attitudes toward central government, local government, and health/medical agencies, etc.
Cultural risk factors are measured using four questions about how much a respondent think that he/she
is a person of hierarchists, egalitarians, individualists, and fatalists, respectively, based on the cultural
theory of risk (Thompson et al., 2018; Douglas, 1970; Douglas, M. 1982). The three latent variables ofrisk perception, trust and cultural risk factors are further explained by respondents’ individual andhousehold attributes (i.e., age, gender, and number of household members).
Because travel time (Molin

Timmermans, 2010; Guo et al., 2020; Chen et al., 2022), travelcompanion (LaMondia et al., 2010; Thrane et al., 2015; Böcker et al., 2016), travel habit (González etal., 2017; Lanzini

Khan, 2017; Gao et al., 2020) are crucial to travel mode choice decisions, all thesevariables are also introduced into the XHCM.
65

Chapter Influences of preparedness and correlated cultural and psychological factors on choice
behaviors in response to COVID-19: A case study on travel mode choices

5.3.2 Equations included in the XHCM
1) Structural and measurement equations
The structural equations in the XHCM are defined as follows:
𝐿𝑉𝑛,𝑐𝑢𝑙 = ∑𝑘 Υ𝑐𝑢𝑙,𝑘 𝑋𝑛,𝑘 + 𝜂𝑛,𝑐𝑢𝑙 , 𝜂𝑛,𝑐𝑢𝑙 ~𝑁(0, 𝜎𝜂2𝑐𝑢𝑙 )(5-1)

𝐿𝑉𝑛,𝑡𝑢 = ∑𝑘 Υ𝑡𝑢,𝑘 𝑋𝑛,𝑘 + Υ𝑐𝑡 𝐿𝑉𝑛,𝑐𝑢𝑙 + 𝜂𝑛,𝑡𝑢 , 𝜂𝑛,𝑡𝑢 ~𝑁(0, 𝜎𝜂2𝑡𝑢 )(5-2)

𝜂𝑛,𝑟𝑝 ~𝑁(0, 𝜎𝜂2𝑟𝑝 )(5-3)

𝐿𝑉𝑛,𝑟𝑝 = ∑𝑘 Υ𝑟𝑝,𝑘 𝑋𝑛,𝑘 + Υ𝑡𝑟 𝐿𝑉𝑛,𝑡𝑢 + 𝜂𝑛,𝑟𝑝 ,where 𝐿𝑉𝑛,𝑐𝑢𝑙 , 𝐿𝑉𝑛,𝑡𝑢 , and 𝐿𝑉𝑛,𝑟𝑝 [𝑳𝑽𝒏,𝒍 are individual n’s latent variables representing risk
perception (rp), trust (tu), and cultural risk factors (cul), respectively; 𝑋𝑛,𝑘 is individual n’s kth
attribute (e.g., age, gender, and the number of household members), and Υ𝑐𝑢𝑙,𝑘 , Υ𝑡𝑢,𝑘 , and Υ𝑟𝑝,𝑘 [𝜰𝒍,𝒌
are the unknown parameters associated with the three latent variables; Υ𝑐𝑡 is the coefficient describing
the influence of cultural risk factors on trust and Υ𝑡𝑟 is the coefficient indicating the influence of trust
on risk perception [𝜰𝒓 : Υ𝑐𝑡 , Υ𝑡𝑟 ; 𝜂𝑛,𝑐𝑢𝑙 , 𝜂𝑛,𝑡𝑢 , 𝜂𝑛,𝑟𝑝 are the error terms of the three latent variables,
respectively, each of which follows a normal distribution [𝜼𝒏,𝒍 .
A latent variable usually represents a common factor behind two or more some observed variables.
Such a relationship is expressed as the following measurement equation:
𝐼𝑛,𝑙,𝑞 = ζ𝑙,𝑞 𝐿𝑉𝑛,𝑙 + 𝛿𝑛 , 𝛿𝑛 ~𝑁(0, 𝜎δ2 )(5-4)

where, 𝐼𝑛,𝑙,𝑞 is individual n’s qth observed variable corresponding to the lth latent variable 𝐿𝑉𝑛,𝑙(l = rp, tu, cul) and ζ𝑙,𝑞 is the unknown parameter representing an association degree of 𝐿𝑉𝑛,𝑙 with
𝐼𝑛,𝑙,𝑞 , and 𝛿𝑛 is a normally-distributed error term.

2) Utility equation
In addition to the above three latent variables, there are also other factors that are used to directly explain
the choice utility. Such a utility function is expressed below.
𝑈𝑛,𝑚 = ∑𝑠 𝛽𝑚,𝑠 𝑋𝑛,𝑚,𝑠 + λ𝑟𝑝,𝑚 𝐿𝑉𝑛,𝑟𝑝 + λ𝑡𝑢,𝑚 𝐿𝑉𝑛,𝑡𝑢 + λ𝑐𝑢𝑙,𝑚 𝐿𝑉𝑛,𝑐𝑢𝑙 + 𝜀𝑛,𝑚(5-5)

Here, 𝑈𝑛,𝑚 is the utility that individual n’s chooses travel mode m, 𝑋𝑛,𝑚,𝑠 indicates n’s sthvariable that is used to directly explain the choice utility of travel mode m (as shown in Section 6,
individual attributes are excluded from Equation (5-5) by referring to existing studies (e.g., Gao et al.,2020; Kamargianni et al., 2015; Kim et al., 2017; Scagnolari et al., 2015; Thorhauge et al., 2019)).
λ𝑟𝑝,𝑚 , λ𝑡𝑢,𝑚 , and λ𝑐𝑢𝑙,𝑚 [𝝀𝒍,𝒎represent the influences of the three latent variables 𝐿𝑉𝑛,𝑟𝑝 , 𝐿𝑉𝑛,𝑡𝑢 ,and 𝐿𝑉𝑛,𝑐𝑢𝑙 on the utility of travel mode m. And 𝜀𝑛,𝑚 is an error term, which is assumed to follow an
66

Chapter Influences of preparedness and correlated cultural and psychological factors on choice
behaviors in response to COVID-19: A case study on travel mode choices

independent and identical Gumbel distribution. This distribution results in the so-called multinomial
logit (MNL) model under the random utility maximization principle.
In this study, travel time, travel companion and pre-pandemic travel habits (i.e., preparedness)formed to avoid being infected are used as 𝑋𝑛,𝑚,𝑠 . To capture the unobserved heterogeneities in thesethree observed travel-related attributes, each of them is assumed to have a random parameter 𝛽𝑚,𝑠 ,0which follow a normal distribution with mean 𝛽𝑚,𝑠and standard deviation 𝜎𝑠 . Related to travel time,such a normal distribution assumption allows the co-existence of negative and positive parameters. The
logit model with unknown randomly distributed parameters related to explanatory variables is called
mixed logit model. The model consisting of equations (5-1) – (5-5) with a mixed logit structure is called
mixed hybrid choice model (i.e., XHCM).

5.3.3 Estimation
In this study, the above-built mixed hybrid choice model (XHCM) is estimated based on a maximum
likelihood method, where the likelihood function is expressed as follows:
𝐿𝑛 =𝑋𝑛,𝑚,𝑠 , 𝑳𝑽𝒏,𝒍 ;) ∏𝑞 𝑓𝐼 (𝐼𝑛,𝑙,𝑞 |𝐿𝑉𝑛,𝑙 ; ζ𝑙,𝑞 ) ∏𝑙 𝑓𝐿 (𝐿𝑉𝑛,𝑙 |X 𝑛,𝑘 , 𝐿𝑉𝑛,𝑗 ; Υ𝑙,𝑘 , Υ𝑗 ) 𝑑𝐿𝑉𝑛,𝑙∫𝐿𝑉 ∏𝑚 𝑓𝑌 (𝑦𝑛,𝑚 | 𝛽 , 𝛌𝑛,𝑙𝑚,𝑠 𝒍,𝒎(5-6)
where, l indicates one of the three latent variables of risk perception, trust and cultural risk factors;
Υ𝑗 refers to Υ𝑐𝑡 or Υ𝑡𝑟 ; 𝑓𝑌 (∙) (equation (5-7)) indicates the probability that individual n chooses
travel mode m under the influences of explanatory variables 𝑋𝑛,𝑚,𝑠 and latent variables 𝑳𝑽𝒏,𝒍; 𝑓𝐼 (∙)(equation (5-8)) is the joint density function related to each measurement equation, 𝑓𝐿 (∙) (function (59)) represents the joint density function of each structural equation, and 𝑦𝑛,𝑚 (equation (5-10)) is adummy variable indicating whether individual n chooses travel mode m or not (yes: 1, no: 0).

𝑒𝑥𝑝(∑𝑠 𝛽𝑚,𝑠 𝑋𝑛,𝑚,𝑠 + λ𝑟𝑝,𝑚 𝐿𝑉𝑛,𝑟𝑝 + λ𝑡𝑢,𝑚 𝐿𝑉𝑛,𝑡𝑢 + λ𝑐𝑢𝑙,𝑚 𝐿𝑉𝑛,𝑐𝑢𝑙 )𝑓𝑌 (∙) = (∑ℎ∈𝐻 𝑒𝑥𝑝(∑𝑠 𝛽ℎ,𝑠 𝑋𝑛,ℎ,𝑠 + λ𝑟𝑝,ℎ 𝐿𝑉𝑛,𝑟𝑝 + λ𝑡𝑢,ℎ 𝐿𝑉𝑛,𝑡𝑢 + λ𝑐𝑢𝑙,ℎ 𝐿𝑉𝑛,𝑐𝑢𝑙 )1

𝐿𝑉𝑛,𝑙 −∑𝑘 Υ𝑙,𝑘 𝑋𝑛,𝑘

𝜂,𝑙

𝜎𝜂,𝑙

𝑓𝐿 (∙) = 𝜎 𝛷 (1

𝐼𝑛,𝑙,𝑞 −ζ𝑙,𝑞 𝐿𝑉𝑛,𝑙

(5-7)

)

(5-8)

) {corresponding to equation (5-1)}(5-9a)

𝑓𝐼 (∙) = 𝜎𝛿,𝑞1

𝐿𝑉𝑛,𝑙 −∑𝑘 Υ𝑙,𝑘 𝑋𝑛,𝑘 −Υ𝑟 𝐿𝑉𝑛,𝑗

𝜂,𝑙

𝜎𝜂,𝑙

𝑓𝐿 (∙) = 𝜎 𝛷 (

𝑦𝑛,𝑚

)

𝛷(

𝜎𝛿,𝑞

) {corresponding to equations (5-2) and (5-3)} (5-9b)
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Chapter Influences of preparedness and correlated cultural and psychological factors on choice
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𝑦𝑛,𝑚 = {1, 𝑖𝑓

𝑈𝑛,𝑚 = max{𝑈𝑛,ℎ }(5-10)ℎ

0,

𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒

Here, Φ(⋅) is the probability density function of a standard normal distribution.
As for the identification issues of the above XHCM, details refer to existing studies (e.g., Vij et al.,2014; Walker et al., 2004). To estimate this model, the software Apollo (Hess et al., 2019) was used,
where a simulated estimation approach is generated by random Halton draws (Train, 2009).

. Result and discussion
To run the above XHCM, 100 Halton draws were applied for each random parameter. For each activity
category, the calculation time ranged between 31.82 min and 93.83 min, using a desktop PC (Inter ®
CoreTM i7-9700 / 14 Core / 3.00GHz). Tables 5.4–5.6 shows the modeling estimation results with respect
to the following seven trip purposes: work/study, shopping, eating out, physical exercise, party, culturalleisure, and medical activities. As shown in Table 5.4, the Rho-squared values of the XHCM models
range from 0.47 to 0.62, indicating that the modeling accuracy is sufficiently high. There are totally 502
unknown parameters in Tables 5.4-5.6, among which 304 parameters (60.6%) are statisticallysignificant. Concerning trip purposes, the model for medical activities has the largest share (77.0%) ofsignificant parameters, while the model for work/study has the smallest share (41.4%). As for structuralparameters of latent variables, even though there are more insignificant parameters representing thedirect effects of risk perception, trust and cultural risk factors, there are more significant parametersindicating the influence of cultural risk factors on trust and the influence of trust on risk perception. All
the above observations suggest that the adopted XHCM is acceptable to conduct analyses for achieving
the research purpose of this study.

Table . . Estimation results of mixed hybrid choice model: Utility equation
Travel purposes
Explanatory variables

Travel time

Work/Study

Est.
Mean μ -0.05
Bus
STDEV σ 0.12
Mean μ -0.02
Rail
STDEV σ 0.08
Mean μ 0.01
Car
STDEV σ 0.01
Mean μ -0.17
Activetransport STDEV σ 0.20
Mean μBus
STDEV σ Mean μ
Rail
STDEV σ -t-val.
-3.50
7.76
-2.35
8.42
2.12
2.22
-7.55
8.37
-

Shopping

Eating out

Est.
-0.06
0.08
-0.07
0.10
-0.01
0.01
-0.24
0.22
-0.75
0.96
-0.75
2.69

Est.
-0.09
0.16
-0.06
0.09
-0.01
0.01
-0.12
0.13
-0.81
0.87
-0.35
2.16

t-val.
-4.04
8.63
-3.12
4.64
-4.14
0.41
-5.50
6.00
-2.25
1.61
-1.79
4.78

68

t-val.
-4.70
6.73
-2.94
4.48
-2.75
2.64
-5.84
11.55
-1.81
1.53
-0.72
4.39

Physical
exerciseEst. t-val.
0.02 0.85
0.15 8.33
0.01 0.09
0.15 10.03-0.06 -5.07
0.23 8.17
0.03 1.51
0.10 5.07-2.18 -1.82
3.54 2.94-3.01 -2.53
4.29 3.89

Party
Est.
-0.13
0.19
-0.04
0.08
0.01
0.01
-0.11
0.15
1.92
3.24
-1.18
2.35

t-val.
-4.57
5.94
-2.41
5.07
0.61
1.06
-4.96
7.24
2.46
3.00
-1.23
2.73

CulturalleisureEst. t-val.
-0.05 -1.14
0.13 3.59
0.03 2.88
0.07 8.23
0.01 0.79
0.01 2.73-0.11 -6.73
0.13 7.42-0.73 -1.59
0.05 0.06-0.66 -1.70
0.79 1.34

Medicalactivities
Est. t-val.
-0.24 -6.60
0.41 8.18-0.03 -3.12
0.06 5.52
0.01 -0.37
0.01 1.26-1.69 -5.29
1.32 5.33-1.41 -3.13
1.23 2.55-0.67 -1.98
0.45 0.83

Chapter Influences of preparedness and correlated cultural and psychological factors on choice
behaviors in response to COVID-19: A case study on travel mode choices
Travel purposes

Work/Study

Physical
exerciset-val. Est. t-val. Est. t-val. Est. t-val.
-1.13 -1.07 -0.76 -2.04 -3.22 -1.70
1.27 3.06 2.40 6.49 5.07 4.33
-1.66 -5.61 -1.05 -2.51 -4.09 -3.21
0.41 1.10 0.45 1.10 3.90 3.12
-49.60 -7.85 -0.01 -0.02 -2.04 -1.65
41.79 8.86 0.53 0.76 3.18 2.45
-0.54 -1.01 0.91 1.40 -2.64 -2.13
2.50 2.67 1.30 1.75 3.99 3.44
-0.52 -1.12 -0.33 -0.64 -2.79 -2.27
2.43 3.26 1.69 2.75 4.72 3.72
-6.36 -4.30 -0.03 -0.05 -3.61 -2.94
3.83 2.98 0.07 0.10 2.94 2.44
5.33 4.31 11.71 4.84 6.02 1.71 3.72
4.50 0.75 0.94 5.99 5.03 2.36 2.20
5.19 5.72 12.45 3.96 4.84 2.09 4.62
3.84 4.14 4.36 1.79 1.45 1.60 1.56
3.52 1.47 4.03 1.91 2.55 0.84 1.50
2.07 0.55 0.63 3.18 2.94 1.86 1.60
4.76 4.92 10.78 3.67 4.83 0.77 1.02
3.48 3.04 3.63 4.14 4.03 0.13 0.10
4.24 4.80 3.82 3.43 4.23 1.02 1.70
3.74 1.07 0.95 6.16 11.21 1.70 1.42
3.92 7.01 5.86 3.51 4.81 1.77 3.40
3.21 6.97 6.44 0.75 2.01 1.51 1.28
5.05 7.54 6.29 5.72 8.76 4.96 7.56
5.15 3.19 3.33 3.87 8.48 2.18 1.86
5.17 9.15 6.82 6.29 9.58 3.34 3.95
4.44 5.11 4.93 4.22 9.15 0.36 0.26
0.06 -5.62 -1.52 -10.27 -0.58 -10.13 -0.83
0.06 -12.86 -7.12 -14.29 -0.94 -15.21 -0.73
0.03 0.28 0.17 18.63 1.36 -58.95 -3.07

Est.
-1.54
4.23
-2.17
1.57
1.91
3.33
-0.55
2.27
-1.28
3.71
-1.46
0.85
2.45
2.58
3.42
0.80
2.16
3.59
3.39
4.77
0.91
2.27
3.33
0.52
5.67
5.20
6.06
5.47
-3.30
-1.83
8.08

t-val.
-2.05
5.45
-2.33
1.68
2.28
2.96
-0.53
2.29
-1.35
3.30
-1.40
0.77
5.61
2.75
6.91
0.91
3.04
2.31
6.17
4.22
0.69
2.64
2.08
0.39
4.07
4.99
4.38
5.16
-0.18
-0.03
0.17

CulturalMedicalleisureactivities
Est. t-val. Est. t-val.
-0.50 -1.67 -1.54 -6.38
1.05 2.39 0.30 0.76
-0.71 -1.95 1.84 1.07
1.40 2.26 7.20 3.33
-0.22 -0.42 9.18 5.16
0.59 0.66 15.28 6.06
-0.24 -0.58 -3.76 -4.96
0.54 0.65 3.72 4.62
-0.34 -1.04 1.36 0.96
0.76 1.35 28.75 6.54
-0.28 -0.68 -47.43 -7.98
0.72 1.11 58.84 9.40
2.89 4.61 11.18 8.38
4.30 6.41 14.33 8.10
1.85 3.83 4.14 4.77
5.52 7.06 3.22 2.76
0.24 0.65 1.00 1.28
0.52 0.78 3.87 2.72
1.74 3.99 14.48 5.82
1.77 2.69 7.79 3.84
1.20 2.21 6.95 9.10
3.95 10.36 14.48 8.82
0.34 0.67 1.82 4.76
4.39 11.03 2.30 5.08
2.70 9.32 4.21 13.6
1.50 7.95 0.31 1.27
3.28 11.09 16.72 6.12
1.69 9.30 7.11 6.19
-0.89 -0.20 34.26 2.91
10.79 3.44 7.41 1.69
-9.76 -3.03 -11.53 -1.96-1.63 -0.55 -6.46 -0.39 -33.02 -1.98

3.94

0.06

-4.34 -1.39 -13.63 -0.45

Explanatory variables
Est.
TravelMean μCar
companion of
STDEV σ family
Mean μActivetransport STDEV σ Mean μBus
STDEV σ Mean μRailTravelSTDEV σ companion ofMean μfriendCar
STDEV σ Mean μ
Activetransport STDEV σ Mean μ 3.00
Bus
STDEV σ 4.34
Mean μ 2.24
Pre-pandemic
Rail
STDEV σ 3.38
preparedness:
Travel habit
Mean μ 1.17
Carwith high risk
STDEV σ 1.91
Mean μ 2.52
Activetransport STDEV σ 3.27
Mean μ 15.14
Bus
STDEV σ 6.17
Mean μ 13.83
Pre-pandemic
Rail
STDEV σ 5.23
preparedness:
Travel habit
Mean μ 17.98
Carwith low risk
STDEV σ 9.65
Mean μ 18.83
Activetransport STDEV σ 7.53
Bus
0.33
Rail
0.23
Direct effect of risk
Car
0.16
perception (rp)Active0.33 0.06
transport
Bus-2.10 -0.09
Rail-1.22 -0.09
Direct effect of trust (tu)Car
-1.36 -0.08
Active-1.49 -0.08
transport
Bus-0.23 -0.28
Rail-0.22 -0.40
Direct effect of cultural risk
Car
-0.28 -0.42
factors (cul)Active-0.35 -0.47
transport
Structural equation: Influence of-0.61 -0.72
cultural risk factors on trust (ct)
Structural equation: Influence of trust
1.58 0.70
on risk perception (tr)AIC
123876.3
BIC124478
LL(0, choice)-6077.2
LL(final, choice)-3222.4
Rho-squared
0.47

Shopping

Eating out

Party

15.66 0.93 36.02 0.55 -5.31 -0.54 -1.50 -0.19 2.60 0.20 -45.70 -1.90
17.68 2.02 50.23 0.85 4.08 0.51 -1.05 -0.05 -30.87 -2.94 -9.84 -1.41
2.23 0.24 -65.29 -1.14 -8.15 -0.22 2.49 0.17 28.04 2.66 15.15 1.57
11.41 0.73 23.23 0.40 -13.07 -0.55

1.06

5.62 1.33 1.58 0.58 4.98 0.40
25.61 6.71 2.47 0.84 15.57 0.90
-2.72 -1.04 -3.66 -1.22 42.97 1.36

0.03 0.03 -0.43 -0.50 -6.34 -3.51
0.11 0.04 0.69 0.09 -1.38 -1.78-0.88 -0.48 -1.59 -0.23 1.95 2.07

-2.94 -0.82-0.61 -0.22 -0.73 -0.24

0.58

0.17 18.20 0.69

0.05 12.48 1.39 18.16 0.45

2.58

0.51

-0.34 -4.00 -0.47 -3.41 -1.69 -9.07 -1.69 -4.21

3.81

2.90

1.02

2.32

2.87 13.88

1.94

2.64

8.49

0.92

3.07

228759.8229554.1-11333.7-4255.5
0.62142787.1
143541.6-7100.8-2897.9
0.59

2.21

0.24

1.11

109941.8110673.3-5417.4-2847.9
0.47

1.02

1.17

97181.797903.4-4825.1-2422.7
0.5098947.699670.5-4891.1-2469.1
0.50

180531181304.9-8924.3-3460.3
0.61

Note: The values shown in grey indicate insignificant parameters and the other values represent significant parameters withthe significance level of 5% or lower.

69

Chapter Influences of preparedness and correlated cultural and psychological factors on choice
behaviors in response to COVID-19: A case study on travel mode choices
Table . . Estimation results of mixed hybrid choice model: Measurement equations
Latent variables

Observed variables
whole country

Risk perception
(rp)

Trust
(tu)

Residencemunicipality
frequently-visitedplaces
crowded public
transport vehicles
Health/medical
agencies

Work/
Shopping
study
Est. t-val. Est. t-val.

Eating out

Physical
Party
CulturalexerciseleisureEst. t-val. Est. t-val. Est. t-val. Est. t-val.

Medicalactivities
Est. t-val.

0.57 0.83 0.29

8.64

0.46 2.77 1.04 6.15 3.15 0.94 0.07

2.90

0.34 3.92

0.59 0.83 0.30

8.68

0.49 2.76 1.12 6.10 3.35 0.94 0.07

2.89

0.35 3.86

0.55 0.83 0.27

8.52

0.44 2.75 1.04 6.09 3.11 0.94 0.07

2.90

0.32 3.86

0.35 0.83 0.16

7.73

0.28 2.71 0.59 5.69 1.91 0.94 0.04

2.91

0.20 3.92-1.12 -0.45 -0.87 -8.26 -0.86 -3.76 0.68 3.41 0.62 1.74 -0.10 -3.37 -0.23 -4.80

central government-1.33 -0.45 -1.05 -8.29 -0.98 -3.80 0.76 3.41 0.64 1.74 -0.10 -3.35 -0.28 -4.62

local government-1.07 -0.45 -0.89 -8.30 -0.84 -3.75 0.65 3.36 0.54 1.75 -0.08 -3.28 -0.23 -4.67

Hierarchists

0.01 0.10 -0.26 -11.09 -0.08 -1.66 -0.16 -4.76 -0.05 -1.32 -0.12 -3.69 -0.01 -0.95

Egalitarians

0.01 1.19 -0.15 -6.97 -0.09 -2.02 -0.14 -3.91 -0.07 -1.98 -0.12 -3.79 -0.02 -2.30

Individualists

0.07 1.68 0.02

Cultural risk factors(cul)Fatalists

0.95

0.15 6.84 0.10 2.33 0.13 5.98 0.12

5.46

0.09 7.16

0.09 1.72 -0.01 -0.33 0.17 8.09 0.09 2.47 0.19 8.91 0.16

7.38

0.11 8.42

Note: The values shown in grey indicate insignificant parameters and the other values represent significant parameters withthe level of 5% or lower.

Table . . Estimation results of mixed hybrid choice model: Structural equations
Latent variables

Risk perception
(rp)

Trust
(tu)

Cultural risk factors
(cul)

Work/
Shopping
study
Est. t-val. Est. t-val.

Physical
Party
CulturalexerciseleisureEst. t-val. Est. t-val. Est. t-val. Est. t-val.

Medicalactivities
Est. t-val.

Gender

0.41 0.76 0.08

0.23 1.69 0.15 3.53 0.13 1.09 0.12

0.41 6.88

Age-0.08 -0.47 -0.01 -1.43 -0.06 -1.69 -0.03 -4.30 -0.03 -2.05 -0.04 -0.28 -0.12 -6.39

Explanatory variables

Number of household
members

1.56

Eating out

0.32

0.04 0.23 0.05

2.45

0.08 1.80 0.01 1.20 0.02 0.91 -0.01 -0.07 0.13 4.33

Gender

1.88 0.66 0.09

3.09

0.54 3.31 0.19 3.73 1.75 2.54 -1.99 -1.70 -2.24 -2.23

Age-0.53 -0.60 -0.02 -4.78 -0.14 -2.99 -0.03 -2.31 -0.49 -2.72 0.72

1.89

0.72 2.70

Number of household
members

0.47 0.55 0.02

1.96

0.16 2.77 0.04 2.39 0.43 2.22 -0.80 -1.91 -0.98 -2.78

Gender

3.06 1.46 0.09

2.71

1.07 4.53 0.16 3.60 1.07 3.68 0.77

Age

-0.91 -1.34 -0.04 -6.26 -0.28 -4.12 -0.02 -2.58 -0.30 -3.76 -0.24 -4.87 -0.67 -9.04

Number of household
members0.84 1.27 0.02

1.82

0.31 3.52 0.03 2.19 0.26 2.75 0.24

3.65

4.70

2.23 7.87

0.90 7.44

Note: The values shown in grey indicate insignificant parameters and the other values represent significant parameters withthe significance level of 5% or lower.

5.4.1 Features of psychological factors
The estimation results of the measurement equations for the three types of psychological factors (i.e.,risk perception, trust, and cultural risk factors) are shown in Table 5.5. In total, there are 54 parametersthat are estimated to be significant (71.1% of the 77 unknown parameter), indicating that the constructed
three latent variables are suitable to capture risk perception, trust, and cultural risk factors.
Looking at the estimated parameters of the four observed variables for all the seven trip purposes,
70

Chapter Influences of preparedness and correlated cultural and psychological factors on choice
behaviors in response to COVID-19: A case study on travel mode choiceseven though the correlations between the risk perception at the four spatial scales are between 0.72 and
0.82, all the estimated parameters with respect to all the seven trip purposes are positive, and nocontradictory or irrational results are obtained. This supports the joint use of the four measurements ofrisk perception. The latent variable “risk perception” is obviously characterized by people’s perceptionof the infection risks at a relatively large area (the whole country, the residence municipality, and afrequently visited place), rather than within a crowded public transport vehicle (Shopping: 0.16; Eatingout: 0.28; Physical exercise: 0.59; Cultural leisure: 0.04; Medical activities: 0.20).
As for the latent variable “trust”, its three observed variables have similar parameter values,
indicating that people’s trust related to COVID-19 policy measures comes from not only governments
but also medical agencies.
In the case of the latent variable “cultural risk factors”, comparing the four observed variables“hierarchists”, “egalitarians”, “individualists”, and “fatalists” (correlations: two correlations are lessthan 0.10, two are less than 0.20, and the remaining two are less than 0.45), the former two variables
with significant parameters have negative parameter values, while the latter two with significant
parameters all have positive parameter values. Comparing the parameter values, “hierarchists” (highlevel group and high-level grid: experts and government are trusted, and the social order is supported)and “egalitarians” (high-level group and low-level grid: equality of all people and the risk decision by
all people are emphasized) mainly represent the cultural risk factors of shopping (hierarchists: -0.26;
egalitarians: -0.15) and physical exercise (hierarchists: -0.16; egalitarians: -0.14), where the role ofgroup is strong. In contrast, “individualists” (low-level group and low-level grid: the individual freedom
is highly prioritized) and “fatalists” (low-level group and high-level grid) mainly explain the culturalrisk factors of eating out (individualists: 0.15; fatalists: 0.17), party (individualists: 0.13; fatalists: 0.19)
and medical activities (individualists: 0.09; fatalists: 0.11), where the role of group becomes weak. As
for cultural leisure, the cultural risk factors are mainly featured by “fatalists” (0.16): the respondents of“fatalists” believe in fate, because he/she think there is little he/she can do to control the environment,
and they think that socially defined aspects (social classification, regulation or other external
constraints) impost more on an individual.

5.4.2 Influences of individual and household attributes on psychological factors
Table 5.6 shows that 39 parameters of gender, age and number of household members are statisticallysignificant. These three individual and household attributes are influential to all the three latent variablesof risk perception, trust and cultural risk factors for medical activities, but are not influential in the caseof work/study. These three individual and household attributes affect trust when travelling for shopping,eating out, physical exercise, and party, and cultural risk factors when travelling for eating out, physicalexercise, party, and cultural leisure. Differently, these three individual and household attributes are notthat influential to risk perception in the sense that there are only seven significant parameters out of 21
relevant parameters (and there is no significant parameter for work/study and eating out). In the case of71

Chapter Influences of preparedness and correlated cultural and psychological factors on choice
behaviors in response to COVID-19: A case study on travel mode choices

cultural risk factors, they are further affected by gender and age when travelling for shopping.
As for the influences of individual and household attributes on risk perception, people living in ahousehold with more members are mostly likely to perceive a higher level of infection risk at various
spatial levels in the cases of participating in shopping (0.05) and medical activities (0.13), than thoseliving together with a smaller number of household members. Male respondents tend to perceive ahigher level of infection risk at various spatial levels in the cases of physical exercise (0.15) and medical
activities (0.41). Younger respondents perceive a higher level of risk at various spatial levels whenparticipating in physical exercise (-0.03), party (-0.03), and medical activities (-0.12), than older
respondents.
About the influences of individual and household attributes on trust, because the result in Table
5.5 shows a heterogeneity of the meaning of latent variable across travel purposes (Shopping, Eatingout, Cultural leisure and Medical activities have negative sign, but Physical exercise has opposite sign),
we need to state the influence of individual and household attributes on trust by combining the resultsin Table 5.5 and Table 5.6. For instance, the estimated coefficients of physical exercise in Table 5.5 have
opposite signs to other activities but the results of physical exercise in Table 5.6 are same with otheractivities. Meanwhile, the estimated coefficients of medical activities in Table 5.5 have same signs withother activities but its results in Table 5.6 are opposite to other activities. Thus, physical exercise andmedical activities show similar results of the influence of individual and household attributes on trust,which are opposite to other activities. Based on that, we can find people living in a household with moremembers and male persons tend to show a higher level of trust in governments and health/medical
agencies in the cases of physical exercise (0.19/0.04 in Table 5.6, positive in Table 5.5) and medical
activities (-2.24/-0.98 in Table 5.6, negative in Table 5.5), while people living in a household with moremembers and male persons demonstrate less trust in governments and health/medical agencies in thecases of shopping and eating out. On the other hand, higher trust is observed with respect to older
persons when traveling for shopping and eating out, while less trust is further observed for older persons
when participating in physical and medical activities.
Concerning the influences of individual and household attributes on cultural risk factors, female
persons are more likely to be a hierarchist in the cases of physical exercise and cultural and an egalitarianin the cases of eating out, physical exercise, party, cultural leisure, and medical activities, but less likely
to be an individualist and a fatalist in the cases of eating out, physical exercise, party, cultural leisure,and medical activities.
Older persons tend to be a hierarchist in the cases of physical exercise and cultural leisure and an
egalitarian when participating in eating out, physical exercise, party, cultural leisure, and medical
activities; however, older persons are less likely to be an individualist and a fatalist in the cases of eatingout, physical exercise, party, cultural leisure, and medical activities.
People living together with a smaller number of household members are more likely to be ahierarchist in the cases of physical exercise and cultural leisure and an egalitarian in the cases of eatingout, physical exercise, party, cultural leisure, and medical activities, but less likely to be an individualist

72

Chapter Influences of preparedness and correlated cultural and psychological factors on choice
behaviors in response to COVID-19: A case study on travel mode choices

and a fatalist in the cases of eating out, physical exercise, party, cultural leisure, and medical activities.

5.4.3 Influences of psychological factors on travel mode choices
The correlations between latent variables are considered via a hierarchical structure in the XHCM ofthis study. Hence, expect the direct effects (see Table 5.4), some latent variables may have indirect
effects on travel mode choice through other latent variables. Here, the latent variables “trust” can affectthe utility function indirectly through “risk perception”, while the latent variables “cultural risk factors”
show indirect impact on utility through “risk perception” and “trust”. To investigate the influences ofpsychological/cultural factors on travel mode choices, the total effect (the sum of direct effect andindirect effect) of each latent variable is calculated via the equation (5-1) (5-2) (5-3) and (5-5) as theevidence of quantitative analysis. The result of total effect is summarized in Table 5.7.
As observed in Table 5.7, risk perception is only assumed to have a direct effect on travel mode
choices during the COVID-19 pandemic, while the other two latent variables “trust” and “cultural risk
factors” are assumed to have not only a direct effect but also indirect effect(s) on travel mode choices.
Observing the total effects shown in Table 5.7, about half of their parameters are statistically significant
in either a direct or an indirect way. As shown in Table 5.4, trust affects risk perception positively for
all trip purposes. All observed variables related to risk perception have positive parameters. Except
physical exercise, all other observed trust-related variables have negative parameters (see Table 5.5).
The above results indicate that higher distrust in governments and medical agencies leads to a higherlevel of perception about the infection risk. This is consistent with the observation by Ding and Zhang(2021). For the trip purpose “work/study”, both direct and indirect effects are not significant, meaningthat neither of risk perception, trust and cultural risk factors does not affect the travel mode choice forwork/study. Considering that we collected data from six developed countries in March-April 2022, thisobservation is surprising. Note that respondents were asked to compare travel mode choices before andduring the COVID-19 pandemic, where the pandemic period covered the whole period up to the end of
the survey. Thus, the travel mode choices during the pandemic indicate people’s average choices. The
above observation means that when people have to go to work/study on average days, they will choose
whatever travel mode without considering any psychological factors. This is probably due to theessential feature of work/study for most people’s daily lives, at least in part. In other words, if
psychological factors may matter to commuting/school mode choices, they may only work tentatively
for a certain period, but not over a long period. This needs to be further confirmed based on a panel
survey of travel mode choices over a long period, which should be left as an important research issue
for the future.
Influences of the three types of psychological factors on travel mode choices for the trip purposes
other than work/study are analyzed below.

73Chapter Influences of preparedness and correlated cultural and psychological factors on choice
behaviors in response to COVID-19: A case study on travel mode choices
Table . . Total effects of trust and cultural risk factors on the utility of travel mode choice
Latentvariables
Risk
perception(rp)

Trust (tu)

Cultural risk
factors (cul)Travel
modes
Bus
Rail
Car
Activetransport
Bus
Rail
Car
Activetransport
Bus
Rail
Car
Activetransport

Work/
Study
0.33
0.23
0.16

Shopping-5.62-12.860.28

Eatingout-10.27-14.29
18.63

Physical
exercise-10.13-15.21-58.95

-3.30
-1.83
8.08

Culturalleisure
-0.89
10.79
-9.76

Medicalactivities
34.26
7.41
-11.53

0.33

-1.63

-6.46

-33.02

3.94

-4.34

-13.63

-1.6
-0.9
-1.1

-0.5
-19.2
3.0

16.1
22.5
-29.1

-7.7
0.4
-22.3

-4.9
-2.9
10.7

0.3
-2.4
2.3

-14.2
-3.0
4.5

-1.0

6.7

10.7

-21.0

5.1

1.0

5.6

0.7
0.3
0.4

5.8
32.1
-3.8

-6.0
-8.1
10.0

18.1
14.8
80.7

8.3
5.0
-19.0

0.5
-8.4
7.1

-20.8
-4.5
6.6

0.2

-5.2

-4.4

53.7

-9.2

3.2

8.3

Party

[Note Grey-colored: none of the parameters related to the total effect is statistically significant.

Risk perception
In the case that the parameter of an observed risk perception variable and that representing the effectsof the latent variable “risk perception” on a travel mode have a different sign (or a same sign), if an
individual perceives a higher risk of infection, he/she is less likely (or more likely) to choose the travel
mode, in comparison to shared mobility and taxi. Concretely speaking, during the COVID-19 pandemic,
considering that shared mobility and taxi are given as a reference for mode choices, rail is not preferred
for shopping (-12.86), but preferred for cultural leisure (10.79), while bus is preferred for medical
activities (34.26). These observations may be due to that cultural leisure and medical activities are
generally less frequent than shopping, and/or the places of performing cultural leisure and medical
activities are located in a convenient built environment to use rail and bus. Unexpectedly, car is not
more attractive than shared mobility and taxi for physical exercise (-58.95), cultural leisure (-9.76), andmedical activities (-11.53) during the pandemic. This result is not intuitive, but probably due to theplaces of performing these three activities are not convenient to use a car (e.g., lack of parking slots or
expensive parking fees).
rust
It is assumed that trust not only directly affects travel mode choices but also indirectly affects throughrisk perception. Similar to the above case of risk perception, the parameters of observed trust variables
and those representing the effects of the latent variable “trust” on travel modes have both consistent andinconsistent signs. If the parameter signs are consistent (or inconsistent) for a certain travel mode, inthe case that an individual trusts governments and/or health/medical agencies, he/she is more likely (or74Chapter Influences of preparedness and correlated cultural and psychological factors on choice
behaviors in response to COVID-19: A case study on travel mode choices

less likely) to choose the travel mode, by comparing with shared mobility and taxi. Concretely speaking,if an individual trusts governments and/or health/medical agencies, he/she is more likely to use rail for
shopping and cultural leisure as well as medical activities, bus for shopping and medical activities, carfor eating out; in contrast, he/she is less likely to use active travel modes for all trip purposes except
party, car for the trip purposes other than eating out, rail and bus for eating out, and bus for culturalleisure, because of the opposite signs of the result in Table 5.7 and Table 5.5. Even though up to thesurvey period, the governments of the targeted six countries had taken various protective measures (e.g.,requirements of wearing mask, disinfection, and keeping social distance) for all types of travel modes,
the above seemingly contradictory observations suggest different trip makers’ complicated responses tothe pandemic, involving both logical and illogical perception about the infection risk, which issignificantly associated with the trust in governments and medical agencies. The above observations reconfirm the importance of differentiated risk communication with respect to different activities, where
different stakeholders should play different roles in the communication, as evidenced by Zhang (2021).
Cultural risk factors
Cultural risk factors are assumed to not only directly affect travel mode choices but also indirectly affectthrough trust and further risk perception. Most of the observed variables of cultural risk factors are
statistically significant, among which “hierarchists” and “egalitarians” have negative parameters and“individualists” and “fatalists” have positive parameters.
Respondents who highly think that important decisions should be made by experts/governments(hierarchists) and/or by all people (egalitarians) are less likely to choose rail and bus for shopping,
physical exercise and party, but they are more likely to choose rail and bus for eating out and medical
activities. Those of “hierarchists” and “egalitarians” also prefer the use of rail for cultural leisure. For
the use of rail and bus, “individualists” and “fatalists” show a completely opposite preference for all
trip purposes other than work/study.
Concerning car and active travel mode, they are preferred by “individualists” and “fatalists” forphysical exercise, cultural leisure, and medical activities, where car is also preferred by “individualists”
and “fatalists” for eating out. Car and active travel mode are further preferred by “hierarchists” and“egalitarians” for shopping and party, where active travel mode for eating out is also preferred by“hierarchists” and “egalitarians”. Individualists and fatalists pay more attention to their own benefits
and convenience than the benefit and convenience of the whole society: this can better explain why they
prefer the use of car and active travel mode more than other modes.
In the cases of shopping, eating out, physical exercise and party, individualists/fatalists (rather thanhierarchists/egalitarians) are more likely to have higher distrust in governments and health/medical
agencies. In contrast, if people travel for cultural leisure and medical activities, individualists/fatalists
would highly distrust governments and health/medical agencies.
The above differences in preference between hierarchists/egalitarians and individualists/fatalists

75Chapter Influences of preparedness and correlated cultural and psychological factors on choice
behaviors in response to COVID-19: A case study on travel mode choices

obviously come from the feature that hierarchists/egalitarians are more likely to follow the social orderand respect equality among people (e.g., to obey social/physical distancing rules during the pandemic
for avoiding the infection), while individualists/fatalists concern too much about themselves.

5.4.4 Influences of pre-pandemic preparedness on travel mode choices
Pre-pandemic preparedness, i.e., risk-concerned travel habits formed before the COVID-19 pandemic,
is measured using three dummy variables: travel habit with high risk (i.e., public transport: rail and bus),
travel habit with middle risk (i.e., shared mobility and taxi), and travel habit with low risk (i.e., car andactive travel mode), where the travel habit with middle risk is treated as a reference during the modeling
estimation. In other words, the XHCM model only includes the travel habit with high risk and that withlow risk. It is found that most of the travel habit parameters (both high risk and low risk) (49 parameters)
are statistically significant, re-confirming the role of past choice behaviors in explaining the current
travel mode choices, but in the context of COVID-19.
Looking at the interval estimates of travel habit parameters (see Table 5.8), the only consistently
positive parameter of travel habit with high risk is observed with respect to the use of bus for shopping(2.840, 5.780). Those consistently positive parameters of travel habit with low risk are related to theuse of bus for work/study (3.047, 27.233) and shopping (2.703, 6.897) , rail for work/study (3.579,
24.081) and eating out (2.040, 4.980) as well as party (2.311, 4.349), car for shopping (1.288, 13.792)
and physical exercise (0.687, 9.233) as well as medical activities (3.602, 4.818), and active transport
for work/study (4.071, 33.589), physical exercise (2.634, 4.046) and medical activities (2.784, 30.626).
A positive parameter of travel habit means that the corresponding travel habit (either high or low risk)
is associated with the use of one of the above-mentioned travel mode choices.
Even though preparedness with respect to any infection risk level is, on average, positively related
to choices of a majority of travel modes, there are surely a not small portion of respondents who dislike
traveling by a certain travel mode if they experienced some infection risks of influenza viruses before
the COVID-19 pandemic.

5.4.5 Influences of other factors on travel mode choices
ravel time
In the survey, travel time was only collected with respect to the chosen travel modes. Thus, it isnecessary to be included into the travel mode choice model. Here, the travel time of those unchosen
travel modes was imputed using that of other respondents who lived in the same residence region andchose the same travel mode, with respect to each activity category, separately.
Concerning the modeling results, a majority of mean parameters of travel time are statisticallysignificant and have a logical negative sign, indicating that trip makers dislike longer travel time, on76

Chapter Influences of preparedness and correlated cultural and psychological factors on choice
behaviors in response to COVID-19: A case study on travel mode choices

average. This result is consistent with existing studies in the COVID-19 context (Aaditya

Rahul,

2021; Sun et al., 2020). Unfortunately, travel time does not affect travel mode choices for participatingin physical exercise (except private car: -0.511, 0.391), cultural leisure (except rail: -0.107, 0.167, active
transport: -0.365, 0.145), and it does not affect choice of private car for medical activities. On the other
hand, looking at the interval estimates of travel time parameters with 95% confidence level (see Table5.8), it is found that a value range from negative to positive is observed for all relevant parameters.
Positive values of travel time parameters mean that during the COVID-19 pandemic, some people had
to choose some travel modes with a longer travel time. The mean travel time parameters of private car
for work/study (mean: 0.01, see Table 5.4) and rail for cultural leisure (mean: 0.03, see Table 5.4) arealso positive, while their interval estimates also include negative parameter values.
It should be noted that all the above observations show significant unobserved heterogeneities, as
shown by those significant standard deviation parameters of travel time.
ravel companion
Among all the 48 mean parameters of travel companion (family and friends) introduced in the XHCM,
21 are statistically significant. Among these 21 significant parameters, 18 have a negative value,
meaning that when traveling together with family/friends, people tend to avoid using the corresponding
travel modes. Active transport is not preferred in the case of travelling together with family and/or
friends for shopping (mean: -1.66/-6.36, see Table 5.4), physical exercise (mean: -4.09/-3.61), travellingwith family for eating out (mean: -1.05) and party (mean: -2.17), and travelling with friends for medical
activities (mean: -47.43). In the cases of active travel for shopping (-2.464, -0.856) and eating out (1.932, -0.168), the interval estimates with 95% confidence level (see Table 5.8) only include negativevalues, which further support the above observations. The use of bus (rail) is not preferred by people
traveling with family/friends for shopping (mean: -0.75 for family; -49.60 for friend, see Table 5.4) andphysical exercise (mean: -3.01 for family; -2.64 for friend). People travelling with family dislike theuse of private car for eating out (mean: -0.76), party (mean: -1.54) and medical activities (mean: -1.54),
while private cars are not preferred by people travelling with friends for physical exercise (mean: -2.79).
Looking at the three positive and significant mean parameters, when travelling with family/friends forparticipating in a party, people are more likely to use a bus (mean: 1.92/1.91), while people travelingwith friends for medical activities also tend to use a bus (mean: 9.18).
It should be noted that most of the above observations show significant unobserved heterogeneities,
as shown by those significant standard deviation parameters of travel companion.

77

Chapter Influences of preparedness and correlated cultural and psychological factors on choice
behaviors in response to COVID-19: A case study on travel mode choices
Table . . Interval estimates (9 % confidence level) of the parameters of travel attributes and prepandemic preparedness
Parameters following a normal
distribution
Travel time
Bus
Rail

Travel companionof family

Privatecar
Activetransport
Bus
Rail

Travel companionof friend

Privatecar
Activetransport
Bus
RailPre-pandemic
preparedness:
Travel habit withhigh risk

Pre-pandemic
preparedness:
Travel habit withlow risk

Privatecar
Activetransport
Bus
Rail
Privatecar
Activetransport
Bus
Rail
Privatecar
Activetransport

Work/study

Shopping

Eating out

Physical
exercise

Party

CulturalleisureMedicalactivities(-0.285,0.185)(-0.177,0.137)(-0.010,0.030)(-0.562,0.222)(-0.217,0.097)(-0.266,
0.126)(-0.030,0.010)(-0.671,0.191)(-2.632,
1.132)(-6.022,4.522)(-3.619,
1.359)(-2.464, 0.856)(-131.508,
32.308)(-5.440,
4.360)(-5.283,
4.243)(-13.867,
1.147)(2.840,5.780)(-2.394,
13.834)(0.392,2.548)(-1.038,
10.878)(2.703,
6.897)(-6.651,
20.671)(1.288,13.792)(-0.866,
19.166)(-0.404,
0.224)(-0.236,0.116)(-0.030,0.010)(-0.375,0.135)(-2.515,0.895)(-4.584,
3.884)(-5.464,3.944)(-1.932, 0.168)(-1.049,
1.029)(-1.638,
3.458)(-3.642,2.982)(-0.167,
0.107)(-6.900,
16.580)(0.452,7.468)(-4.323,
8.143)(-4.444,
11.784)(-8.644,
15.504)(2.040,4.980)(-1.865,
13.305)(-1.981,14.561)(-0.274,
0.314)(-0.284,0.304)(-0.511,0.391)(-0.166,0.226)(-9.118,
4.758)(-11.418,
5.398)(-13.157,
6.717)(-11.734,
3.554)(-8.273,
4.193)(-10.460,
5.180)(-12.041,
6.461)(-9.372,
2.152)(-2.916,
6.336)(-1.046,5.226)(-2.806,
4.486)(0.515,1.025)(-2.312,4.352)(-1.190,4.730)(0.687,9.233)(2.634,4.046)(-0.502,0.242)(-0.197,
0.117)(-0.010,0.030)(-0.404,
0.184)(-4.430,
8.270)(-5.786,
3.426)(-9.831,6.751)(-5.247,0.907)(-4.617,8.417)(-4.999,
3.899)(-8.552,
5.992)(-3.126,0.206)(-2.607,7.507)(1.852,
4.988)(-4.876,
9.196)(-5.959,
12.739)(-3.539,5.359)(2.311,
4.349)(-4.522,15.862)(-4.661,
16.781)(-0.305,
0.205)(-0.107,0.167)(-0.010,0.030)(-0.365,0.145)(-0.828, 0.632)(-2.208,
0.888)(-2.558,
1.558)(-3.454,2.034)(-1.376,
0.936)(-1.298,
0.818)(-1.830,
1.150)(-1.691,
1.131)(-5.538,
11.318)(-8.969,
12.669)(-0.779,
1.259)(-1.729,
5.209)(-6.542,8.942)(-8.264,8.944)(-0.240,
5.640)(-0.032,
6.592)(-1.044,0.564)(-0.148,0.088)(-0.010,0.030)(-4.277,
0.897)(-3.821,1.001)(-1.552,
0.212)(-2.128, 0.952)(-12.272,
15.952)(-20.769,
39.129)(-11.051,
3.531)(-54.990,
57.710)(-162.756,
67.896)(-16.907,
39.267)(-2.171,
10.451)(-6.585,8.585)(-0.788,29.748)(-21.431,
35.331)(-2.688,6.328)(3.602,4.818)(2.784,30.626)(-5.506,
11.506)(-4.385,8.865)(-2.574,
4.914)(-3.889,
8.929)(3.047,27.233)(3.579,24.081)(-0.934,
36.894)(4.071,33.589)Note: The values shown in grey indicate insignificant parameters and the other values represent significant
parameters with the significance level of 5% or lower.

78

Chapter Influences of preparedness and correlated cultural and psychological factors on choice
behaviors in response to COVID-19: A case study on travel mode choices. Conclusion
This research has revealed how psychological/cultural risk factors and pre-pandemic preparedness areassociated with individuals’ travel mode choices during the COVID-19 pandemic, by estimating amixed hybrid choice model with correlated latent variables. Risk perception and trust as well as culturalrisk factors are especially emphasized, where correlations between these factors are jointly incorporated
through the concept of the latent variable. Preparedness is reflected in the modeling analysis in the formof risk-concerned travel habits formed to survive from influenza before the COVID-19 pandemic. The
modeling results are compared across travel purposes. Here, main findings are summarized.
First, the use of public transport decreased significantly due to the shock of COVID-19 pandemic
for all daily travel purposes. This observation is consistent with existing studies (e.g., Eisenmann et al.,2021; Abu-Rayash et al., 2020; Crowley et al., 2021). While public transport is preferred for shoppingand medical activities in a pandemic era with the lowest decrease. Shared mobility and taxi are lesspreferred for leisure activities when comparing with pre-pandemic era with a higher decrease. Travel
mode choices for commuting during the pandemic are considerably different from those for other travel
purposes in the sense that people are less likely to change their travel mode choices for commuting.
Second, risk perception, trust, and cultural risk factors are influential to travel mode choices for
shopping, physical exercise, cultural leisure and medical activities; however, their influences cannot be
confirmed with respect to work/study. Party-related travel mode choices are only affected by culturalrisk factors (Egalitarians: coefficient (-0.07), t-value (-1.98); Individualists: coefficient (0.13), t-value(5.98); Fatalists: coefficient (0.19), t-value (8.91)) and travel mode choices for eating out are notaffected by risk perception. These three latent factors affect travel mode choices in both direct andindirect ways and both expected and unexpected influences are detected, where there are moresignificant indirect effects of trust and cultural risk factors than direct effects.
Third, risk perception is found to be more remarkable at larger spatial scales (coefficient: from0.07 to 1.12) than inside crowded public transport vehicles (coefficient: from 0.04 to 0.59). Such a jointreflection of different types of risk perception presents a more reliable understanding of risk perception
for supporting effective pandemic policymaking. People perceiving a higher level of infection risk are
more likely to reduce the use of public transport for some high-frequency daily activities (e.g.,
shopping) during the COVID-19 pandemic (-12.86). On the other hand, public transport is preferred forsome low-frequency activities (e.g., cultural leisure, medical activities) by some people with a higherlevel of risk perception (10.79/34.26). In the cases of cultural leisure, medical activities and physicalexercise, shared mobility and taxi are more preferred than car by people having a higher-level of risk
perception.
Fourth, travel mode choices are affected by trust in both governments and health/medical agencies
in a similar way, where trust is positively influential to risk perception (from 0.92 to 2.89). People whotrust governments and health/medical agencies prefer the use of rail for shopping and cultural leisure as
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Chapter Influences of preparedness and correlated cultural and psychological factors on choice
behaviors in response to COVID-19: A case study on travel mode choices

well as medical activities, bus for shopping and medical activities and car for eating out, but do notprefer the use of active travel modes for all trip purposes (except party), rail and bus for eating out, andbus for cultural leisure.
Fifth, the four types of cultural orientations affect travel mode choices in two differentiated ways.
Concretely, the group-oriented hierarchism and egalitarianism are a pair with higher similarities, which
influences are very different from the pair of individualism and fatalism. The two ways of the influencesof cultural orientations show diverse influencing directions and magnitudes depending on travel modes
and purposes. Hierarchists and egalitarians are mainly reflected in travel mode choices for shopping
and physical exercise, while individualists and fatalists are dominating in travel mode choices for othertravel purposes. Hierarchists and egalitarians are less likely to choose public transport mode for
shopping, physical exercise and party, but they are more likely to choose rail and bus for eating out andmedical activities. Meanwhile, car and active travel mode are further preferred by hierarchists andegalitarians for shopping and party, where active travel mode for eating out is also preferred byhierarchists and egalitarians. Individualists and fatalists show a completely opposite preference for all
trip purposes except work/study. Car and active travel mode are preferred by individualists and fatalists
for physical exercise, cultural leisure, and medical activities, where car is also preferred byindividualists and fatalists for eating out.
Sixth, different from existing studies, this research has examined the role of preparedness byfocusing on risk-concerned travel habits formed before the COVID-19 pandemic, which were used tosurvive from influenza viruses. Such a focus on influenza allows us to better capture the influences ofpreparedness on travel mode choices during the pandemic. As a result, preparedness is surely influential
to travel mode choices, but the influences are not consistent across a majority of travel purposes.
This research has investigated travel mode choices with respect to different travel purposes,
separately, where for each purpose, the samples without the purpose have been excluded from themodeling analysis. Such a kind of analysis has its own rationality, because the purpose of this researchis to clarify influential factors to travel mode choices. Representing choices of different travel modes isalso for a better understanding about how people attempt to avoid being infected. From such aconsideration, avoiding the participation in out-of-home activities is probably a much more effective
way to keep away from viruses. Thus, it is also worth analyzing travel mode choices by incorporating
their interactions with choices of activities, as argued by the popular activity-based approach. In
addition, in relation to the virus infection, a proper understanding of social contact cannot be neglected:
for example, how and how long to contact with other persons during travel and during performing anactivity, and how many persons are involved in social contact, etc. Furthermore, more importantly, it isnecessary to accumulate more data about the infection risk levels within different types of transport
vehicles (e.g., bus, subway, ridesharing vehicle, taxi), allowing policymakers to make scientificallysound and practically effective/efficient policies to control the ongoing COVID-19 pandemic andprepare well for future pandemics. Last but not the least, more countries should be added for deriving
more general findings based on cultural factors and social norms.

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Chapter Differences of social contacts by activity settings before and during the COVID-19
pandemic and associational analysis

CHAPTER

Differences of social contacts byactivity settings before and during the COVID19 pandemic and associational analysis.1 Introduction
Social contact is an important route of transmission for the SARS-CoV-2. The mechanism oftransmission of the COVID-19 pandemic can be expressed by the changes of individuals’ social contacts.
As the COVID-19 pandemic has been lasting for more than two years, more and more countries begin
to lift restriction measure of COVID-19 since 2021 gradually. People must get used to living in life withthe pandemic. As a result, to prepare for the complete end of the epidemic in the near future, it isnecessary to focus on people’s behavioral changes of social contacts between the pre-pandemic andpandemic era so as to provide more scientific evidence for further academic research or policymaking.
Previous studies only focused on the heterogeneities of social contacts across a few limited
individuals’ attributes (e.g., age, gender), the heterogeneities across activity participation attributes (e.g.,frequency, duration) are still unknown. To resolve that question, firstly, this chapter calculates the total
number of social contacts and the mean number of social contacts for people who have performed their
daily social contacts before or during the pandemic (as see in Appendix 4 and Appendix 5; total number= sample* mean number) across the options of different individual/household attributes and key activity
participation attributes. The trend and heterogeneity of the number of individual’s social contact fordifferent groups are revealed. Secondly, this chapter calculates people’s changes of the number of social
contacts before (2019: during the Influenza season) and during the pandemic (2020 – 2021. May) basing
on that obtained total number and mean number of social contacts. In the end of this chapter, thecircumstances with high risk of infection via social contacts is stated and some recommendations fortarget circumstances are proposed. The contents of this chapter have provided a reference for furthersocial contact research and scientific evidence for policy making in the pandemic era.

.2 Data
Most precious studies used POLYMOD data as the baseline of individuals’ social contacts before the81Chapter Differences of social contacts by activity settings before and during the COVID-19
pandemic and associational analysis

pandemic, and mainly focus on the changes of the number of social contacts between the pre-pandemic
and during the lockdown. However, POLYMOD data only shows people’s social contacts in a normal
daily life (not during influenza season), people’s social contact is obviously different from the social
contact during an infectious disease period (such as the influenza). Hence, except the difference ofsocial contact between during normal daily life in pre-pandemic and during the pandemic, the differenceof that between during the influenza season in pre-pandemic and during the pandemic should not beignored as well. This chapter used the social contact data in comparative survey of activity-travel andsocial contact before-during COVID-19 to investigate how individuals’ number of social contacts
changed after the outbreak of the COVID-19 pandemic and what is the heterogeneity of the number ofsocial contacts across different individual/household attributes, activity participation attributes(frequency, duration, taken protecting measures) and contact modes.
In the comparative survey of activity-travel and social contact before-during COVID-19, people
responded their average number of contacted persons for performing daily activities each time in anordered sequence: (1) 1~2 persons, (2) 3~4 persons, (3) 5~6 persons, (4) 7~9 persons, (5) 10 persons ormore. It is assumed that people would only have one contact with one contacted persons each time inthis study. Thus, the number of social contacts is equal to the number of contacted persons at here. And
because the data of the number of social contacts is in an ordered sequence, this study transfers theordered options into numerical options with the average value of the ordered range. For example, theoption of “(4) 7~9 persons” is transferred into contacting average “8” persons.

.3 Result and discussion
Based on the social contact data introduced in previous section, this section will give a descriptive
analysis of the changes of the number of contacts between the influenza season in the pre-pandemic (in2019) and during-pandemic (since 2020). The changes of social contacts will be expressed acrossdifferent variables to show the heterogeneities among their options. In this analysis, it is assumed thatone person only has one contact with another person during performing the activities. Thus, the numberof contacted persons from participants’ responses is accounted as the number of social contacts for eachcontact setting.
Only the contacts with the people outside of the companion (such as family) is counted as thecontacts in this analysis because people are more likely to get infected by households at home insteadof in other contact settings, so it is meaningless to talk about the contacts between families outside home,
and we didn’t include the household contacts at here.
The total number of social contacts and the mean value of social contact for each person arecalculated to show the changes of social contact behaviors between pre-pandemic and during-pandemic
from the perspective of different demographic attributes (age, gender, country, occupation, household
size, education, income), activity participation attributes (frequency, duration, protecting measures) and82

Chapter Differences of social contacts by activity settings before and during the COVID-19
pandemic and associational analysis

contact mode. The total number of social contacts can indicate the total change of social contacts in the
population, while the mean number of social contacts can explore the average change of social contacts
only for these people who still perform the according activities.
The results in Figure 6.1 and Figure 6.2 show the changes of the total number and mean numberof social contacts for each contact setting, respectively. Readers can find more details of the number ofcontacts in different contact settings in appendix and calculate the results in Figure 6.1 and Figure 6.2
by themselves. For the total samples in targeted countries, although the restrict lockdown measures havebeen lifted and most indoor places were allowed to reopen gradually since 2021, we can find the total
number of social contact still maintained a low level during the pandemic compared with that in
influenza season of pre-pandemic (as highlighted by Wong et al., 2022; Liu et al., 2021; Latsuzbaia etal., 2020; Zhang, J. and Litvinova, M. et al., 2021; Tomori et al., 2021). Associated with common sense,
the mean number of social contacts is largest in workplace/school (pre-pandemic: mean: 8.85, 95%CI:
8.67-9.03; during-pandemic: mean: 7.42, 95%CI: 7.22-7.62). The mean numbers of social contact
happened in Party (pre-pandemic: mean: 7.21, 95%CI: 7.05-7.37; during-pandemic: mean: 5.68,
95%CI: 5.5-5.86), Cultural leisure (pre-pandemic: mean: 6.32, 95%CI: 6.16-6.48; during-pandemic:
mean: 5.29, 95%CI: 5.1-5.48), and Public transport (pre-pandemic: mean: 6.96, 95%CI: 6.77-7.15;
during-pandemic: mean: 5.96, 95%CI: 5.74-6.18) are also in a high level.

On the contrary, people

only contact less than 4 persons during the medical activities on average (pre-pandemic: mean: 3.66,
95%CI: 3.56-3.76; during-pandemic: mean: 3.34, 95%CI: 3.24-3.44). And the most obvious decreaseof number of social contacts is found for Party (change of total number of contacts: -57.6%; change ofmean number of contacts: -1.53) and Cultural leisure (change of total number of contacts: -58.4%;
change of mean number of contacts: -1.03). They are followed by the decrease of contacts in public
transport (change of total number of contacts: -48.2%; change of mean number of contacts: -1.00). The
reasons for these dramatic changes would be that some venues are still closed (including bankruptcy),
and less people want to do these activities for the benefit of safety or money. The total number of social
contacts decreased obviously for Eating out (-44.6%) and Physical exercise (-45.2%), but for thesepeople who still do these activities, the decrease of the mean number of social contacts is limited (Eatingout: -0.59; Physical exercise: -0.97). In contrast, it seems that most people have resumed their essentialsocial contacts in places of work/study (change of total number of contacts: -34.0%), shopping (changeof total number of contacts: -22.1%), medical activities (change of total number of contacts: -28.4%)
after the initial lockdown in pervious waves of pandemic, while the average social contacts at workplaceor school decreased more obviously (change of mean number of contacts: -1.43). The descriptive
analysis of the number of contacts for different variables are summarized in subsequent sections.

83

Chapter Differences of social contacts by activity settings before and during the COVID-19
pandemic and associational analysis.3.1 Social contacts across individual and household attributes and itschanges before and during the COVID-19 pandemic

1) Social contacts across age
More than 70% of the social contacts were made by the people aged between 20 ~ 60 years.
For the work/study setting, among the working ages (20~64 years), the mean number of social
contacts increased with people’s age. That is sensible because when people getting older, they are more
likely to get a higher position for the work and have more works to contact with others. The mostobvious decrease of the mean value of work contact between pre-pandemic and during-pandemic isfound for the people aged from 20 to 39 year (more than -1.5). The study contacts for the students lessthan 19 years show a dramatic decrease with the value of -40.7% compared to pre-pandemic, which
may because the offline lecture wasn’t recovered totally, some student can still study online.
The mean value of social contacts during shopping activities begin to decrease gradually whenpeople are over 60 years. More young people aged less than 19 years have decreased their total shopping
contacts during the pandemic (-31.4%). Among these people who still have shopping contacts, thepeople with a middle-age (40~49) seems to pay more attention to decreasing the mean value ofcontacting others when shopping (-0.73).
The mean number of social contacts in eating out setting is negatively associated with people’s
age both in pre-pandemic and during-pandemic. For people with the working ages (20~64 years), thechange of the total number of contacts increased with people’s age, indicating that older people areprone the decrease the number of social contacts for eating out. The total number of social contacts ineating out setting have decreased more than 50% for all people aged over 50 years. And people aged 50~ 59 years contacted less people for each eating out behavior than other age groups (change of mean: 0.8).
The mean number of social contacts during physical exercise is high for young people less than
19 years (pre-pandemic: mean: 6.01, 95%CI: 5.02-7; during-pandemic: mean: 5.69, 95%CI: 4.39-6.99).
A sharp decrease of the mean number is found between the age group of less than 19 years and 20~29
years, which may because more people begin to work since 20 years old and have less time to dophysical exercise with others. However, that mean number increased again with the raise of people’s
age after 30 years, and the mean number returns to the level of the group of “less than 19 years” whenpeople are over 70 years (pre-pandemic: mean: 6.45, 95%CI: 5.83-7.07; during-pandemic: mean: 5.78,
95%CI: 4.96-6.6). Social contact during physical exercise decreased obviously for all people aged over
40 years, especially for people aged between 60 and 69 years (change of total number of contacts: 59.8%; change of mean number of contacts: -0.98).
Young people less than 19 (pre-pandemic: mean: 8.06, 95%CI: 7-9.12; during-pandemic: mean:

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Chapter Differences of social contacts by activity settings before and during the COVID-19
pandemic and associational analysis

6.74, 95%CI: 5.39-8.09) are more likely to contact more people during the party than other age group.
Fortunately, the total number of social contacts have decreased obviously from -41.1% to -75.6% for
all age groups. The impact of the COVID-19 pandemic on the social contacts during party is especiallyobvious for people aged from 40 to 49 years with the decrease of mean number (-1.72).
The mean number of social contacts during cultural leisure didn’t show obvious heterogeneities
across the age. During the pandemic, the total number of social contacts have decreased from -46.7%
to -67.6%, although these entertainment venues have been reopened after the lockdown.
During the pandemic, the mean number of contacts during medical activities is negatively
associated with people’s age for the people aged between 20 to 70. While that mean number is highest
for the age groups between 20 to 40 years, which may because these people aged between 20 to 40
years are more likely to be the parents who need to take care of their children during the during medical
activities. Because of the bad health status of the elderly, it is difficult for them to decrease the social
contacts even during the pandemic, that phenomenon is especially obvious for the people over 70 years
with the smallest decrease of total number (-17.3%) and mean number (-0.08) of contacts.
The mean number of contacts in public transport setting during the pandemic is the issue ranking
only second to work/study setting with value ranging from 5.47(95%CI: 5.12-5.82) to 6.6 (95%CI: 5.28). If people are older, they are more likely to decrease more social contacts in public transport, which
may because they have more access to use a private car to avoid the risk of infection. These people aged
between 60 and 65 years have the biggest decrease of the total number of contacts in public transportbecause they are more rich and more dangerous to the virus. Conversely, it is difficult for the people
aged between 30 and 39 years to decrease the total number of contacts, but they could try to decrease
the average number of contact each time (-1.14), such as avoiding traveling during rush hour.
2) Social contacts across gender
The share of males’ social contacts increased during the pandemic for all contact settings. It seems thatfemales decreased more social contacts than males (both the total number and mean number), especiallyfor shopping setting, eating out setting and cultural leisure setting. As a result, the shares of females’
contacts were surpassed by males’ contacts for these settings during the pandemic.
Males have more contacts than females in physical exercise setting, party setting and public
transport setting whether in pre-pandemic or in during-pandemic. Oppositely, females always havemore contacts than males during the medical activities.
3) Social contacts across countries
We collected the data in six countries from March 2021 to May 2021, the data from these countries help
us to conduct a cross-countries comparison of the social contact behaviors before and during the
pandemic.

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Chapter Differences of social contacts by activity settings before and during the COVID-19
pandemic and associational analysis

In the survey period, only a few new daily cases occurred in AU and NZ and the strict lockdown
in AU and NZ had been lifted for a long time. The new daily cases were high in US, but the strict
lockdown measures had been lifted for a long time in US as well. Thus, the data from AU, NZ and US
belong to the same stage, which people had lived with the COVID-19 virus in a normal life for a longtime during the pandemic. UK and CA had just eased the intervention of strict lockdown for not a longtime during the survey period. The data from these two countries can indicate the social contact
behaviors under stronger intervention. JP hadn’t taken strict lockdown measure in 2021, for example,
the restaurants were allowed to open from 5 a.m. to 20 p.m. every day, so the data in JP can show thepeople’s social contacts behaviors with a persistent soft intervention to the COVID-19 pandemic.
The results show that the mean number of contacts for work/study setting, and shopping setting
fluctuated across six countries during the pandemic, but the mean number of contacts for leisure settings(including eating out, physical exercise, party, cultural leisure) in AU, US, NZ (almost no interventions)is obviously higher than CA and UK (strong intervention), especially higher for cultural leisure setting(AU: mean: 5.49, 95%CI: 5.09-5.89; US: mean: 5, 95%CI:4.6-5.4; NZ: mean: 6.06, 95%CI: 5.65-6.47;
CA: mean: 4.24, 95%CI: 3.77-4.71; UK: mean: 3.82, 95%CI: 3.44-4.2). Nevertheless, the mean number
and total number of contacts during medical activities in AU, US, NZ were much higher than othercountries as well, whether there is an increase in the contacts during medical activity led by the infectionof the coronavirus needs further discussion. It seems that the total number and mean number of social
contacts in JP (county with soft interventions) for different settings are at the same level with that in
AU, US, NZ (almost no intervention countries) during the COVID-19 pandemic.
As expected, the decrease of the total number and mean number of social contacts between prepandemic and during-pandemic is most obvious in CA and UK (countries with stronger interventions).
And most people hadn’t recovered their daily social contacts especially for party setting (CA: changeof total number: -81.2%, change of mean number: -2.93; UK: change of total number: -80.8, change ofmean number: -2.53).
) Social contacts across occupation
We try to investigate individuals’ risky social contact behaviors outside home and found that more than
40% of social contacts were conducted by company employee / company officer or self-employedpeople for all contact settings (excluding home).
The staff in government (pre-pandemic: mean: 9.97, 95%CI: 9.31-10.63; during-pandemic: mean:
8.49, 95%CI: 7.76-9.22) or educational institution (pre-pandemic: mean: 10.42, 95%CI: 9.61-11.23;
during-pandemic: mean: 8.82, 95%CI: 7.91-9.73) have a much higher mean number of work contacts
than other occupations revealed that they are more likely to contact more people than others during theirwork.
During the pandemic, the mean numbers of social contacts made by housewife/househusband andother unemployed people (including retired) were obviously lower than other occupations, although

86Chapter Differences of social contacts by activity settings before and during the COVID-19
pandemic and associational analysis

their mean numbers in pre-pandemic were at the same level with other occupations for some contact
settings. The unemployed people (including retired) have an especially lower mean contacts with others
during the medical activities than other occupations during the pandemic (mean: 2.51, 95%CI: 2.362.66).
Housewife/househusband (change of total number: -55.0%; change of mean number: -1.27) andother unemployed people (including retired) (change of total number: -61.9%; change of mean number:-1.01) have decreased their contacts in public transport most obviously between pre-pandemic andduring-pandemic. An obvious decline of contacts in shopping setting (change of total number: -33.3%;
change of mean number: -0.96) and eating out setting (change of total number: -49.4; change of mean
number: -1.21) is found for student exploring that the students have much lower resistance to the closeof shopping and eating out venues.
) Social contacts across household size
About 70% of the social contacts were made by the people with a household size ranging from 2 persons
to 4 persons (including themselves). And a two-person household (such a couple without children) havemuch lower life pressure and higher intention to enjoy life than others, their social contacts account for
about one third of the total social contacts for all people. Interestingly, the mean number of social
contacts (excluding work/study contacts) is obviously lower for these people who live alone, it seems
that the social network of single people living alone is much simpler, as a result, they have fewer social
contacts no matter in pre-pandemic or in during-pandemic.
The mean number of work/study contacts didn’t show obvious heterogeneities across different
household size. However, the mean number of contacts in other settings increased with rising household
size and got the peak point at the household size of 5 persons. The reason may be that if people livewith more persons, they are more likely to join the activities outside home, and the household size of 5
can guarantee traveling together without too many troubles.
The decrease of work/study contacts (whether for total number or for mean number) between prepandemic and during-pandemic is negatively associated with household size, which may because if
people have more economic pressures, they must earn more money and are difficulty to decreased work
contacts. People are prone to decrease more shopping contacts and medical activities contacts if they
have larger household size, which could avoid bringing the virus home from the activity venues.
) Social contacts across education background
The mean number of party contacts during the pandemic is obvious higher for low educated people(elementary school: mean: 6.49, 95%CI: 5.36-7.62; middle school/ junior high school: 6.22, 95%CI:
5.47-6.97) than high educated people (master’s degree: mean: 5.59, 95%CI: 5.17-6.01; doctor degree:
mean: 5.87, 95%CI: 4.68-7.06). The reason might be that most low educated people are young student,

87Chapter Differences of social contacts by activity settings before and during the COVID-19
pandemic and associational analysis

they have less risk to the virus and have plenty of time to join the party. The low educated people havea higher dependence on the use of public transport for daily travels, as a result their mean number ofsocial contacts in public transport have increased slightly during the pandemic (elementary school: 0.03;
middle school/ junior high school: 0.05). The work/study contacts have decreased more for the people
with the master’s degree (change of total number: -38.4%; change of mean number: -1.82), which may
because they have more access to conduct online work or study during the pandemic.
) Social contacts across income level
The mean number of social contacts occurred at work/study, shopping and leisure venues increased withpeople’s household income since 25,000 USD both in pre-pandemic and during-pandemic. It is easy tounderstand that if people have more money, they have better access to work/study, shopping and leisureactivities and contacts with others.
The people with low income (under 34,999 USD per year) have the smallest decrease of work
contacts between pre-pandemic and during-pandemic (under 24,999 USD: change of total number: 32.7%, change of mean number: -0.92; 25,000 to 34,999 USD: change of total number: -26.0%, changeof mean number: -0.79). That means the poor people’s work don’t allow them to work online, the riskof infection via work contacts would be still higher for them. Meanwhile, the poor people (under 34,999
USD per year) also show a smaller decrease of shopping contacts than others (under 24,999 USD:
change of total number: -11.0%, change of mean number: -0.13; 25,000 to 34,999 USD: change of total
number: -16.6%, change of mean number: -0.11). In fact, the total number of shopping contacts for thepeople with a low income is at the same level with that for people with more money. Fewer poor people
have shopping contacts, and each poor people have a higher average number of contacts, if they have
shopping activities, so they have a higher risk of infection than other people with higher income.

.3.2 Social contacts across activity participation attributes and its changes
before and during the COVID-19 pandemic

1) Social contacts across frequency
Consistent with common sense, the mean number of social contacts is positively associated with thefrequency of the performing the activities. However, during the pandemic, the contacts with thefrequency of daily or almost daily have decreased for all contact settings. For instance, more than 80%of the work/study contacts occurred with the frequency of daily or almost daily in pre-pandemic, while
the total number of work/study contacts decreased -44.7% accompanied by the decrease of the mean
number with the value of -1.23, which may because there were fewer work opportunities due to the88Chapter Differences of social contacts by activity settings before and during the COVID-19
pandemic and associational analysis

pandemic. Simultaneously, the work/study contacts with lower frequency increased, the total numberof work/study contacts for the frequency of “Once or twice a month” and “Once or twice a week” haveincreased with the value of 29.5% and 17.1% respectively. Nevertheless, the mean number ofwork/study contact with low-frequency decreased during the pandemic indicating that people would
contact fewer others for work/study in low frequency as well.
In a similar way, the low-frequency shopping contacts increased compared to pre-pandemic (Lessthan once a month: 72.7%, Once or twice a month: 19.6%). About 70% of the shopping contacts
occurred once or twice a week in pre-pandemic, while during the pandemic the shopping contacts withthe frequency of once or twice a week decreased 33.4%, and the mean number of contact decreased 0.44.
In addition, because of the dramatic decrease of the use of public transport, the social contact inpublic transport decreased without the impact of the frequency.
2) Social contacts across duration
The duration of social contact is assumed as the average performing time of the activities in venues inthese chapter. The results show that the mean number of social contacts would increase with the extendof duration. And comparing with the pre-pandemic, the contact with long duration decreased obviously
during the pandemic. In the case of work/study setting, about 70% of the work/study contacts have aduration more than 4 hours each time in pre-pandemic, however, the total number of that contacts
decreased -39.0%, and the mean value declined -1.25 during the pandemic.
More than half of the shopping contacts have the duration between 15 mins and 1 hour, their total
number and mean number decreased -14.0% and -0.47 respectively. Meanwhile, the number of
shopping contact with long duration (more than 1 hour) decreased more than -40%, whereas the numberof contacts with the duration less than 5 mins increased (change of total number: 23.8%, change ofmean number: 0.24). About 80% of social contacts for eating out setting will continue 15 mins - 4 hours
in the influenza season before the pandemic. However, after the outbreak of the COVID-19 pandemic,
the eating out contacts continuing 15 mins -1 hour decreased -27.5% (change of mean number: -0.48),
the contacts continuing 1 hour - 4 hours decreased -59.6% (change of mean number: -0.47), while more
people prefer eating out less than 5 mins (change of total number of contacts: 7.4%, change of mean
number of contacts: -0.38).
To avoid the infection from the COVID-19, the social contacts with short duration (less than 5mins) during medical activities increased 10.0%, and that mean number of contacts increased 0.23 as
well.
3) Social contacts across protecting measures
Three protecting measures of wearing mask, disinfecting hands, keeping a proper distance with others

89Chapter Differences of social contacts by activity settings before and during the COVID-19
pandemic and associational analysis

are included in this analysis. The results indicated that the total number of these contacts taking
protecting measures increased obviously compared to pre-pandemic, and the contacts without
protecting measures decreased more than -90%.
The total number of contacts wearing a mask increased most obviously when working/studying(138.9%), doing medical activities (127.8%) and travelling in public transport (122.3%). The social
contacts with disinfecting hands increased most obviously for the contact setting of shopping (108.3%),
while it only increased 20.7% in the public transport. The obvious increase of the social contacts withkeeping a proper distance with others is found for work/study setting (172.3%) and shopping setting(173.8%).
It is gratifying that people have taken more protecting measures (the total number with protecting
measures increased) and contact fewer others (the mean number decreased) for the contact setting of
shopping, leisure activities and medical activities during the pandemic. And it is reasonable for people
to contact more people for work/study setting and public transport setting during the pandemic if they
have taken enough protecting measures. However, it should be noted that some people haven’t taken
any protecting measures neither in pre-pandemic nor in during-pandemic, the mean number of contacts
without any protecting measures in the location of shopping, cultural leisure and medical activities had
increased during the pandemic as well.

.3.3 Social contacts across contact modes and its changes before and during
the COVID-19 pandemic

There are three contact modes defined in this chapter: (1) ONPC: Only non-physical contacts (in thephysical presence of another person in same space without face to face conversation, skin-to-skintouching or other indirect physical contacts); (2) OPC: Only physical contacts (having a face to faceconversation or, skin-to-skin touching or other indirect physical contacts); (3) PCNPC: both nonphysical and physical contacts. In addition, it is important to note that the virtual social contact without
the physical presence of social contact object in same space (e.g., online contact through SNS) is notincluded in the targeted non-physical contacts of this study. This classification contains not only close
contacts but also indirect contacts providing a comprehensive overview for the social contact analysis.
About the relationship between number of contacts and contact mode, the results show that themean number of social contacts increased with the risk level across the contact modes (risk level: ONPC
< OPC < PCNPC). The number of contacts for medical activities setting is minimal among all settings,
and the mean number didn’t show obvious heterogeneities across the contact modes. The decreasing
value of the total number of social contacts is associated with risk levels of contact modes, namely,
PCNPC has the larger decrease of the total number of social contacts than OPC, and OPC has larger
decrease than ONPC for all contact settings.
90

Chapter Differences of social contacts by activity settings before and during the COVID-19
pandemic and associational analysis

Obviously, people have more direct conversations with others when working/studying or joining
the party. Thus, most of the contacts occurred in work/study setting (55%) and party setting (45.2%)
have both non-physical and physical contacts. And that total number (work/study: -45.9%; party: 67.7%) and mean number of contacts (work/study: -1.07; party: -1.51) in PCNPC mode show an
obvious decrease during the pandemic.
When people are shopping or traveling in public transport, they are usually in the physical presenceof others but don’t need to talk with them. As a result, the main contact mode for shopping and public
transport is only non-physical contacts (shopping: 42.2%; public transport: 45.4%). The shopping
contacts only showed a slightly decrease for the total number (-1.1%) and mean number (-0.48) ofcontacts, while the decrease of ONPC in public transport is obvious (change of total number: -40.5%;change of mean number: -0.93)
The mean number of contacts is average across three contact modes for cultural leisure, eating out,
physical exercise, medical activities. Because if people doing these activities with companion, somepeople might only have conversations their companion. For these people they are more likely to havecontacts in ONPC mode.

91

Chapter Differences of social contacts by activity settings before and during the COVID-19
pandemic and associational analysis

Protecting Contact
measures mode

Duration

Frequency

IncomeEducation background

Household size

Occupation

Country

Gend
er

Age

-100.0%-50.0%

0.0%

50.0%

100.0%

150.0%

200.0%

Total
Less than 19
20~29
30~39
40~49
50~59
60~64
65~69
70 and above
Male
Female
AU
US
NZ
CA
UK
JP
Company employee / self-employed
Governmental officier
Faculty or staff of an educational institution
Housewife or househusband
Part-time job
Student
Other unemployed (including retired)
Other
1
2
3
4
5
6+
No formal qualification
Elementary school
Middle school/ Junior high school
High school/ Senior high school
College/ Vocational school
Undergraduate/ Bachelor
Master degree
Doctoral degree
Under 24,999USD
25,000 to 34,999USD
35,000 to 49,999USD
50,000 to 74,999USD
75,000 to 99,999USD
100,000 to 124,999USD
125,000 to 149,999USD
150,000 to 174,999USD
175,000 to 199,999USD
200,000USD and overA few times a year
Less than once a month
Once or twice a month
Once or twice a week
Daily or almost dailyLess than 5 mins
5 mins - 15 mins
15 mins - 1 hour
1 hour - 4 hours
4 hours or longer
Only non-physical contacts
Only physical contacts
Both non-physical and physical contacts
Wear a mask
Disinfect your hands before/ during/ after using it
Distancing with others
No measures are taken

Work/Study

Shopping

Eating out

Physcial exercise

Party

Cultural leisure

Medical activities Public transport

Figure . 1. The changes of total number of social contacts between pre-pandemic and duringpandemic

92

Chapter Differences of social contacts by activity settings before and during the COVID-19
pandemic and associational analysis

Protecting Contact
measures mode

Duration

Frequency

IncomeEducation background Household size

Occupation

Country

Gend
er

Age

-4.5

-3.5

-2.5

-1.5

-0.5

0.5

1.5

Total
Less than 19
20~29
30~39
40~49
50~59
60~64
65~69
70 and above
Male
Female
AU
US
NZ
CA
UK
JP
Company employee / self-employed
Governmental officier
Faculty or staff of an educational institution
Housewife or househusband
Part-time job
Student
Other unemployed (including retired)
Other
1
2
3
4
5
6+
No formal qualification
Elementary school
Middle school/ Junior high school
High school/ Senior high school
College/ Vocational school
Undergraduate/ Bachelor
Master degree
Doctoral degree
Under 24,999USD
25,000 to 34,999USD
35,000 to 49,999USD
50,000 to 74,999USD
75,000 to 99,999USD
100,000 to 124,999USD
125,000 to 149,999USD
150,000 to 174,999USD
175,000 to 199,999USD
200,000USD and overA few times a year
Less than once a month
Once or twice a month
Once or twice a week
Daily or almost dailyLess than 5 mins
5 mins - 15 mins
15 mins - 1 hour
1 hour - 4 hours
4 hours or longer
Only non-physical contacts
Only physical contacts
Both non-physical and physical contacts
Wear a mask
Disinfect your hands before/ during/ after using it
Distancing with others
No measures are taken

Work/Study

Shopping

Eating out

Physcial exercise

Party

Cultural leisure

Medical activities Public transport

Figure . 2. The changes of the mean number of social contacts between pre-pandemic and duringpandemic

93Chapter Differences of social contacts by activity settings before and during the COVID-19
pandemic and associational analysis. Conclusion
In this chapter, the descriptive analysis of the changes of people’s social contact behaviors between
during the influenza season in pre-pandemic and during the COVID-19 pandemic is conducted for eightcontact settings from the perspectives of different individual/household attributes, activity participation
attributes and contact modes. Not only the close contacts but also the indirect contacts are included inthis analysis expressed into three contact modes: only non-physical contacts, only physical contacts,
both of them. Two indicators were calculated to estimate the general changes of the total number ofsocial contacts and the average changes of social contacts for people who still continue daily social
contacts with others during the pandemic. The study findings are summarized as follows.
Obvious heterogeneities of social contacts were found across individual and household attributes
and contact settings. After the lockdown were lifted, essential social contacts in places of work/study,
shopping and medical activities would recover faster than the contacts for leisure activities, however, it
is difficult to return to the normal level before the pandemic (Wong et al., 2022; Latsuzbaia et al., 2020;
Zhang, J. and Litvinova, M. et al., 2021). The mean number of contacts is especially higher inwork/study setting and public setting.
During the pandemic, older people are prone to have more contacts during work and fewer contacts
during eating out. Young people are more likely to contact more people during the party. Older people(less than 70 years) had tried to decrease the mean number of contacts more if they have to perform
medical activities. But it is difficult for the elderly (over 70 years) to decrease the social contacts during
the medical activities. Females have decreased more social contacts than males during the pandemic.
In countries with the ease of interventions for a long time, the mean number of contacts for these people
who still perform leisure activities during the pandemic was obviously higher than that in other countries.
However, the social contact during medical activities were high which could be led by the infection of
the coronavirus. If country have taken soft interventions, the negative impact of pandemic on theeconomy could be likely to be smaller, but these interventions have only a slight impact on the decreaseof the mean contacts. The people work for school/university and government are more likely to contact
more people than other occupations when they are working. Housewife/househusband and otherunemployed people (including retired) had decreased their contacts in public transport most obviously.
Most of the social contact is made by the two-person household. These people who live alone havefewer intentions to make social contacts whether in pre-pandemic or during-pandemic. If people havea larger household size with more economic pressures, they are less likely to decrease the number ofcontacts when working. The low educated people have a higher dependence on the use of public
transport for daily travels during the pandemic. The people with low income have an obvious higher
social contact with others for work/study setting and shopping setting.
When people have contacts with others during the pandemic, their number of social contacts is
94

Chapter Differences of social contacts by activity settings before and during the COVID-19
pandemic and associational analysis

positively associated with the frequency and duration for performing the activities. The social contacts
occurred with high-frequency and long-duration had decreased significantly during the pandemic.
Meanwhile, the low-frequency contacts increased for work and shopping, and the short-duration
contacts increased for medical activities, shopping and eating out settings. The social contacts with thecontact mode of PCNPC (both physical contacts and non-physical contacts) show the most obvious
decrease during the pandemic, while the social contacts with the mode of ONPC (only indirect nonphysical contacts) have the smallest decrease. The social contacts for work/study setting and party
setting are most both non-physical and physical contacts. The social contacts for shopping setting andpublic transport setting are most indirect non-physical contacts. The social contacts without protecting
measures have decreased more -90%. The social contacts with wearing a mask increased most obviously
for work/study setting, medical activities setting and public transport setting. The social contacts withdisinfecting hands increased most obviously when shopping. The social contacts with social distancing
measures increased obviously for work/study setting and shopping setting.
This chapter provides a glimpse on the heterogeneity of the number of social contact and itschanges across the demographic attributes, activity participation attributes and contact modes. However,
the obtained results in this chapter need to be combined with statistically significant results by modelingapproach for persuasive evidence. And due to the limitation of the length of the chapter, the differences
across some psychological variables that included in the multiple behaviors analysis are not reflectedin this part. The modeling analysis of social contact behaviors would be conducted by the research innext chapter.

95

Chapter Investigation of multi-faceted social contact decisions under the impacts of COVID-19:
oint analysis of contact mode choices and the number of contacted persons

CHAPTER

Investigation of multi-facetedsocial contact decisions under the impacts of
COVID-19:

oint analysis of contact mode

choices and the number of contacted persons.1 Introduction
Individuals’ social contacts with others during activity participation and travelling are one of the main
channels of virus transmission (e.g., Mossong et al., 2008; Akakzia et al., 2007; Latsuzbaia et al., 2020;
Li et al., 2020; Colomer et al., 2021; Mikszewski et al., 2022; Zhang et al., 2021a). To restrain the spreadof COVID-19, various non-pharmaceutical interventions have been implemented. For example, strict
lockdowns (Abouk et al., 2021; Sypsa et al., 2020; Tomori et al., 2021; Latsuzbaia et al., 2020; Lv etal., 2022) dramatically reduced the reproduction number (Brooks-Pollock et al., 2020; Zhang et al.,2021b; Tizzani et al., 2022; Trentini et al., 2022). Physical distancing measures and infection prevention
measures (e.g., wearing a mask) have also been proven to be effective for the pandemic control (Hauget al., 2020; Islam et al., 2020; López et al., 2020; Thu et al., 2020; Huang et al., 2021). However, thesemeasures also caused various negative impacts on economic activities and people’s daily lives(involving various social contacts), which should be well addressed (Chakraborty and Maity, 2020;
Guan et al., 2020; Oraby et al., 2021; Li et al., 2022; Nelson et al., 2022).
Social contact includes direct and indirect contacts between people. Because of the experiences
during lockdowns, people have learned to change the way of social contact and activity-travel behaviors
as well as to behave protectively for avoiding virus transmission (Wong et al., 2022; Liu et al., 2021;
Latsuzbaia et al., 2020; Zhang et al., 2021b; Tomori et al., 2021) and mitigating the COVID-19 impacts
on mental health (e.g., Pouso et al., 2020; Melnyk et al., 2022). However, it is not always successful torely on changes in social contact behaviors, as evidenced by the repeated peaks of new infection cases.
Thus, it is necessary to have a better understanding of social contact patterns and provide more reliable
input data for assessing various policies. At the aggregated level, consequences from such efforts could
offer valuable insights for improving the total environment, e.g., the reduced number of trips andactivities because of social contacts may lead to less carbon emissions. However, existing knowledge
96Chapter Investigation of multi-faceted social contact decisions under the impacts of COVID-19:
oint analysis of contact mode choices and the number of contacted persons

regarding the changes in social contact behaviors and its associated influential factors is rare, leading
to various unknowns about social contacts and consequent misleading policymaking. As reviewed later,
we have identified three key research gaps.
[1 Close contacts have been mainly investigated, because of the popular 1.0m or 2.0m distancing
rule, as recommended by WHO and the US Centers for Disease Control and Prevention as well as manyother countries. Studies on early stages of the pandemic already showed that the maximum transmission
distance of SARS-CoV-2 aerosol might reach 4.0m (e.g., Guo et al. 2020) and modeling studies further
confirmed that even the 2 m rule is not adequate for protecting people, especially in an indoor
environment (e.g., Bazant and Bush, 2021). In other words, indirect social contacts may also transmit
the virus. However, existing studies have neglected the investigation of indirect social contacts.
[2 Existing studies have widely investigated the average number of contacted persons during the
COVID-19 pandemic but neglected the analysis of social contact mode choices (including physical (ordirect) and non-physical (or indirect) contacts). More importantly, no study has been done to examine
the association between social contact modes and the number of contacted persons, not mentioning
about heterogeneities of social contacts across activity settings.
[3 As for modeling approaches, regression-type models have been mainly applied, where
behavioral mechanisms have been beyond the scope of modeling framework. Concerning the two keysocial contact behaviors (i.e., choices of contact modes and the number of contacted persons), no
relevant modeling effort has been made. In other words, the representation of the dependence between
the two contact behaviors have been fully ignored.
This study aims to fill the above research gaps from the following aspects.
(1) A multi-country questionnaire survey was conducted in March-May 2021, from which valid
answers were collected from more than 7000 respondents in six developed countries of Australia,
Canada, Japan, New Zealand, the USA, and the UK. All data are pooled for this study for deriving
cross-country common findings.
(2) Social contact mode choices and the number of contacted persons are jointly represented bybuilding a copula-based model that combines a multinomial logit model and an ordered logit model,
where utility-based behavioral decision-making mechanisms are reflected.
(3) Influences of seven key activity settings (work/study, shopping, eating out, physical exercise,parties, cultural leisure, medical activities) are simultaneously incorporated into the utility functions of
the two contact behaviors. By doing so, observed heterogeneities of the influences of activity settings
can be revealed in a comparative way.
(4) In addition to respondents’ individual attributes, this study especially investigates theinfluences of factors related to social contact settings (both objective and subjective factors) and riskrelated psychological factors (e.g., reliability of risk information, risk perceptions) as well as triggers(e.g., from government and workplace) for behavioral changes.
The rest of this study is structured as follows. Section 2 briefly summarizes the findings of previous
studies related to social contacts. Section 3 describes the survey data. Section 4 discusses the copula-

97

Chapter Investigation of multi-faceted social contact decisions under the impacts of COVID-19:
oint analysis of contact mode choices and the number of contacted persons

based model used in this study. Section 5 shows the model results and discussion, and Section 6 provides
the conclusion.

.2 Survey and Data
7.2.1 Survey design
Based on the life-oriented approach (Zhang, 2017), a retrospective questionnaire survey was designed,
where social contact behaviors during the COVID-19 pandemic were investigated with respect to seven
representative social contact settings (work/study, shopping at supermarket/shopping mall, eating out
at restaurants, physical exercise at indoor places (e.g., gym), parties at indoor public places (e.g., bar),cultural leisure (e.g., visiting cinema, concert hall, etc.), medical activities (e.g., see a doctor, purchase
medicine)). Social contact behaviors refer to choices of four types of social contact modes and thenumber of contacted persons. The four contact modes are: (1) no contact (NC), (2) only physicalcontacts [OPC (e.g., having a face-to-face conversation or, skin-to-skin touching or other indirect
physical contacts), (3) only non-physical (or indirect) contacts (ONPC) (e.g., in the physical presenceof another person but without face to face conversation, skin-to-skin touching or other indirect physicalcontacts), and (4) both physical and non-physical contacts (PCNPC). The number of persons to becontacted was measured using an ordinal scale: (1) 1–2 persons, (2) 3–4 persons, (3) 5–6 persons, (4)
7–9 persons, and (5) ten persons or more. In the case of no contact, the number was reported to be zero.
Note that contacts between household members are excluded from this survey.
The survey further investigated potential determinants of social contact behaviors, including
factors related to social contact settings and risk-related psychological factors as well as triggers forbehavioral changes, in addition to individuals’ socio-demographic attributes. As for social contact
settings, both objective and subjective factors are incorporated into the modeling analysis. Objective
factors include activity frequency, distance from home to activity destination, travel party, andprotective measures taken for having social contacts (wear a mask, disinfect hands). Subjective factors
include respondents’ perceptions of contact situations at activity locations (i.e., closed spaces with poor
ventilation, crowded places, and close-contact settings), perceived safety levels and protective
capacities (or efficiency) (e.g., Witte and Allen, 2020) with respect to respondents themselves and theirhousehold members, and attitudes toward the necessity and publishment related to activities.
Concerning risk-related psychological factors, this study focuses on the reliability of risk information,risk perceptions at different spatial scales, and acceptance of changing the duration/timing of activity
participation. Triggers for behavioral changes (e.g., Sparkman and Walton, 2017) were captured fromthe perspectives of stakeholders and the number of infected cases.

98Chapter Investigation of multi-faceted social contact decisions under the impacts of COVID-19:
oint analysis of contact mode choices and the number of contacted persons

7.2.2 Survey implementation and samples
The survey was conducted online in six developed countries (Australia, United States, New Zealand,
Canada, the United Kingdom, and Japan) in March-May 2021. To roughly reflect the representativenessof the whole population in each country, the collected samples’ distributions in terms of age, gender andresidence region were set to be almost same as those in the whole population, based on official statisticsof these countries.7 Finally, more than 1000 samples were collected in each country: Australia (AU:1125), United States (US: 1297), New Zealand (NZ: 1193), Canada (CA: 1176), United Kingdom (UK:1305) and Japan (JP: 1169). In total, 7265 respondents provided valid information. The differences
between the samples the whole populations are within two percentage points, respectively. Table 7.1
only shows the differences across regions in each country.
Table 7.2 shows distributions of individual attributes by country/region. The share of males (49.0%)
is slightly lower than that of females (51.0%) for the whole data. These shares well matched those in
the whole populations (males: 49.3%, females: 50.7%). People aged 30-60 years old account for thebiggest share (49.6%). The share of old people (aged 60 or above) (29.6%) is higher than that of young
people (younger than 30 or below) (20.8%). Company employees/self-employed people have thebiggest share (39.4%), followed by other unemployed/retirement (24.6%). About one-third of the
respondents have an educational qualification of undergraduate/bachelor (32.9%), followed by thosewith college/vocational qualification (22.5%) and high school or senior high school (19.8%).

7

Australian Bureau of Statistics: https://www.abs.gov.au/ (2020); U.S. Census Bureau: https://www.census.gov/(2020); Stats NZ: https://www.stats.govt.nz/ (2018); Statistics Canada: https://www.statcan.gc.ca/en/start (2020);
Office for National Statistics - GOV.UK: https://www.gov.uk/government/organisations/office-for-nationalstatistics (2020); Statistics Bureau of Japan: https://www.stat.go.jp/english/ (2020)

99

Chapter Investigation of multi-faceted social contact decisions under the impacts of COVID-19:
oint analysis of contact mode choices and the number of contacted persons

Table 7. 1. Differences of regional distributions between samples and whole populations
Country

AU

US

NZ

CA

UK

JP

Shares insamples (%)(A)

Regions
New South Wales, Australian Capital Territory
Queensland
South Australia, Northern Territory
Victoria, Tasmania
Western Australia
Arizona, New Mexico, Colorado
Washington, Alaska, Hawaii, Oregon, Wyoming,
Montana, Idaho, Nevada, Utah
California
Texas
FloridaNorth Carolina, South Carolina, Georgia
Virginia, West Virginia, Maryland, Delaware,
Washington, D.C., Tennessee, Kentucky
Arkansas, Mississippi, Oklahoma, Louisiana, Alabama
New York, Connecticut, Rhode Island, Massachusetts,
Vermont, New Hampshire, Maine
New Jersey, Pennsylvania
Wisconsin, Illinois, Indiana
Michigan, Ohio
Minnesota, North Dakota, South Dakota, Missouri,
Kansas, Nebraska, Iowa
Auckland, Northland
Bay of Plenty, Gisborne
Canterbury
Manawatu-Wanganui, Taranaki, Hawke’s Bay
Marlborough, Nelson, Tasman, West Coast, Southland,
Otago
WaikatoWellington
Alberta, Northwest Territories
British Columbia, Yukon
Manitoba, Nunavut, Saskatchewan
New Brunswick, Nova Scotia, Prince Edward Island,
Newfoundland and Labrador
Ontario
QuebecScotlandNorthern Ireland, Wales
North West, North East
Yorkshire and The HumberWest Midlands
East Midlands
South WestSouth East
East of England
Greater London
Hokkaido
Tohoku
Kanto
Chubu
Kinki
Chugoku, Shikoku
Kyushu, Okinawa

100

32.8
19.9
8.9
26.5
11.9
6.5

Shares in whole
population (%)
(B)
33.7
20.1
7.9
27.9
10.4
5.7

Differences ofshares (%)(A – B)
-0.9
-0.2
1.0
-1.4
1.5
0.8

6.5

5.8

0.7

12.1
8.3
6.6
8.3

12.3
8.5
6.6
8.5

-0.2
-0.2
0.0
-0.2

8.5

8.8

-0.3

6.2

6.1

0.1

9.3

9.6

-0.3

6.9
7.0
7.2

6.9
8.2
6.7

0.0
-1.2
0.5

6.6

6.3

0.3

32.2
8.8
13.7
11.6

36.8
7.7
13.0
10.8

-4.6
1.1
0.7
0.8

12.6

11.6

1.0

9.9
11.2
12.3
14.9
8.1

9.4
10.7
11.3
14.6
6.3

0.5
0.5
1
0.3
1.8

7.8

6.2

1.6

35.5
21.4
8.7
9
12.8
9.2
8.8
8.5
9.4
11.7
9.7
12.2
4.5
7.7
34.1
16.6
18.6
7.3
11.2

39.0
22.6
8.5
7.5
15.0
8.4
8.9
7.1
8.5
13.9
9.1
13.1
4.4
7.4
34.5
16.9
18.2
6.7
11.9

-3.5
-1.2
0.2
1.5
-2.2
0.8
-0.1
1.4
0.9
-2.2
0.6
-0.9
0.1
0.3
-0.4
-0.3
0.4
0.6
-0.7

Chapter Investigation of multi-faceted social contact decisions under the impacts of COVID-19:
oint analysis of contact mode choices and the number of contacted persons

Table 7. 2. Distribution of individual attributes by country
Attributes
GenderAge

Occupation

Education

Definition
Male
Female< 30 years old
30–59 years old
60 years old or above
Company employee/self-employed
Civil servants / organizational employees
Faculty and staff of educational institutes such as
schools and universities
Housewife
Part-time job
Student
Other unemployed (including retirement)
Others
No formal qualification
Elementary school
Middle school/Junior high school/Secondary
schoolHigh school/ Senior high school
College/ Vocational school
Undergraduate/ Bachelor
Master’s degree
Doctoral degree

AU
(%)
49.0
51.0
23.6
49.7
26.7
38.3
6.5

US
(%)
49.0
51.0
20.7
50.1
29.2
36.1
3.6

NZ
(%)
48.5
51.5
22.4
50.4
27.2
39.3
6.5

CA
(%)
48.6
51.4
20.6
51.1
28.3
38.4
4.7

UK
(%)
49.9
50.1
19.3
52.7
28.0
43.8
6.5

JP
(%)
48.6
51.4
18.5
42.9
38.6
40.2
3.3

Total
(%)
49.0
51.0
20.8
49.6
29.6
39.4
5.2

3.0

4.0

4.4

3.6

3.1

0.7

3.1

7.9
12.4
3.3
23.6
5.0
0.2
2.5

6.5
8.3
4.8
27.8
8.9
0.5
0.5

6.4
10.0
4.4
23.9
5.1
2.8
1.9

5.7
8.0
5.5
28.7
5.4
0.1
0.7

5.9
7.0
3.9
23.6
6.2
1.4
0.5

18.4
10.5
3.2
19.8
3.9
0.1
0.0

8.3
9.3
4.2
24.6
5.9
0.8
1.0

13.6

0.5

13.1

1.4

20.2

2.2

8.6

14.6
26.2
26.7
14.1
2.1

20.4
27.8
26.0
19.9
4.4

14.4
20.1
35.2
10.6
1.9

20.2
31.0
33.7
11.3
1.6

20.2
12.1
30.4
13.0
2.2

29.0
18.3
46.1
3.3
1.0

19.8
22.5
32.9
12.1
2.3

7.2.3 Data screening
To better understand social contact decisions, here, data without out-of-home activities are excluded.
As a result, samples included in the seven social contact settings are 3963 for work/study (51.8% outof all the 7265 respondents), 7245 for shopping (99.7%), 4562 for eating out (62.8%), 3518 for physicalexercise (48.4%), 3044 for parties (41.9%), 3025 for cultural leisure (41.6%), and 5670 for medical
activities (78.0%). The largest shares of performing no out-of-home activities are observed with respect
to cultural leisure and parties, while only 0.3% did not go shopping. Stopping some out-of-homeactivities is probably for the consideration of protecting people from being infected (Zhang, 2020).
As shown in Table 7.3, males performed more daily activities than females during the COVID-19
pandemic, except shopping and medical activities. People aged 20–29 participated in more activities(especially for parties (28.1%) and cultural leisure (28.3%)) than other age groups. More than half of
respondents lived in a household with fewer members (three members or below) and about one-third of
respondents came from a two-member household. More than half of the respondents were married.
Respondents with the undergraduate/bachelor qualification ranged from 32.4% to 36.7%, followed by
the education qualification of college or vocational school. About 60% of respondents’ income levels
were between 35,000 USD and 149,999 USD.

101

Chapter Investigation of multi-faceted social contact decisions under the impacts of COVID-19:
oint analysis of contact mode choices and the number of contacted persons
Table 7. 3. Individual attributes by social contact setting
Work/
Study
(%)

GenderAge

Number ofhouseholds

Marriage

Education

IncomeFemale
Male
15~19
20~29
30~39
40~49
50~59
60~64
65~69
70 and above
1
2
3
4
5
6 and above
Married
Other (including
single, divorced,
widowed)No formal
qualification
Elementary school
Middle school/
Junior high school
High school/
Senior high school
College/
Vocational school
Undergraduate/
Bachelor
Master’s degree
Doctoral degree
under 24,999USD
25,000 to
34,999USD
35,000 to
49,999USD
50,000 to
74,999USD
75,000 to
99,999USD
100,000 to
124,999USD
125,000 to
149,999USD
150,000 to
174,999USD
175,000 to
199,999USD
200,000USD andover

Shopping(%)

Eating out(%)

Physical
exercise
(%)

46.2
53.8
2.8
27.0
22.1
20.2
16.5
4.6
3.3
3.5
15.9
28.6
22.7
21.8
7.3
3.7
55.1

51.0
49.0
2.1
18.8
16.7
16.2
16.7
7.1
8.2
14.2
19.1
36.6
19.8
16.1
5.4
3.0
55.8

48.6
51.4
2.3
23.3
19.1
16.9
14.9
5.3
6.4
11.8
16.3
34.1
20.9
19.0
6.1
3.6
58.2

45.3
54.7
2.7
26.4
21.3
17.0
13.3
4.5
5.6
9.2
15.4
31.0
21.8
20.9
6.6
4.3
57.8

44.9

44.2

41.8

0.5

0.8

0.9

Culturalleisure(%)

Medicalactivities
(%)

Total
(%)

44.6
55.4
2.5
28.1
22.2
18.1
13.1
3.7
4.5
7.8
14.1
31.0
22.3
21.4
6.7
4.5
59.0

44.7
55.3
2.5
28.3
22.6
17.7
13.1
3.8
4.1
7.9
15.1
29.1
22.9
21.6
6.8
4.5
57.0

50.8
49.2
1.9
19.2
16.6
15.5
15.7
6.8
8.5
15.8
18.1
36.8
19.8
16.7
5.4
3.2
58.1

48.1
51.9
2.3
23.3
19.4
17.1
15.1
5.5
6.3
11.0
16.8
33.3
21.1
19.0
6.1
3.7
57.2

42.2

41.0

43.0

41.9

42.8

0.8

0.7

0.9

0.9

0.9

0.8

1.0

1.2

1.4

1.7

1.6

1.3

1.2

6.0

8.6

6.9

7.0

7.2

7.3

8.3

7.6

17.5

19.7

18.2

16.5

16.2

16.0

18.7

18.0

20.0

22.5

22.5

20.6

20.7

20.6

22.8

21.6

36.7

32.9

34.2

34.7

34.0

34.5

32.4

33.9

15.6
2.8
9.4

12.2
2.3
13.3

13.6
2.6
11.7

16.2
2.9
10.3

16.6
2.7
9.7

16.5
2.6
10.5

13.2
2.4
12.8

14.3
2.6
11.5

7.9

11.1

10.2

9.2

8.4

9.1

10.8

9.8

11.2

13.7

11.5

11.0

9.8

10.1

13.1

11.9

16.5

17.0

16.8

16.9

17.3

16.2

17.1

16.9

15.6

14.4

15.3

15.2

16.4

16.2

14.3

15.1

13.7

11.1

11.7

13.0

13.6

13.4

11.6

12.3

10.0

7.9

8.9

9.5

9.7

9.5

8.3

8.9

7.5

5.2

6.4

6.8

6.4

6.4

5.2

6.1

3.7

2.7

3.3

3.5

3.9

3.8

3.0

3.3

4.5

3.6

4.2

4.6

4.8

4.8

3.8

4.2

102

Parties(%)

Chapter Investigation of multi-faceted social contact decisions under the impacts of COVID-19:
oint analysis of contact mode choices and the number of contacted persons.3 Features of Key Variables
7.3.1 Dependent variables: Social contact behaviors
As shown in Figure 7.1, more than 60% of respondents had a certain contact with others at all contact
settings, where parties have the largest contact share (75.7%), and shopping showed the smallest shareof 62.3%. Among the three modes with contact, the ONPC mode (i.e., only having indirect contact)accounted for the largest share in all settings (among which, the shares during shopping and eating out
were larger than 37.0%). Considering that the PCNPC mode (the shares range between 11.5%(shopping) and 25.7% (work/study)) also includes indirect contact, ignoring the indirect contact inexisting studies is obviously wrong. Smaller shares of the OPC mode than the ONPC mode at all settingssuggest that people surely tried to avoid close contact for safety as much as possible, but among all
settings, parties, cultural leisure, and medical activities had the largest shares of close contact.
Interestingly, the shares across modes during parties are similar to those during medical activities.
Figure 7.2 indicates that work/study, parties, and cultural leisure involved a larger number ofcontacts (at least 5 persons: 29.2%–40.3%) than in other settings, where the routine activity “work/study”
should be regarded as the riskiest setting for pandemic control policymaking. Shopping, eating out andmedical activities showed the largest share of 1–2 persons. The shares of 3–4 persons varied acrosssettings slightly (11.9% (shopping) – 20.6% (eating out)), in comparison to those of 1–2 persons (16.9%(work/study) – 43.8% (medical activities)).
Concerning the relationships between social contact modes and the number of contacted persons(see Figure 7.3), it is shown that the largest number of contacted persons (i.e., at least 5 persons) was
observed under both physical and non-physical contacts (i.e., PCNPC) at all settings. Thus, mixed
contact modes are associated with more persons to be contacted. Compared with the non-physicalcontact mode (ONPC), the physical contact mode (OPC) has a higher share in the largest number ofcontacted persons (i.e., at least 5 persons). This is consistent with the observation that the ONPC mode
is more likely to be linked with the smallest number of 1–2 persons than OPC and PCNPC. As for the
medium-size of contacts (i.e., 3–4 persons), its largest shares are not necessarily associated with either
PCNPC or OPC.

103

Chapter Investigation of multi-faceted social contact decisions under the impacts of COVID-19:
oint analysis of contact mode choices and the number of contacted persons
100%

11.5%
13.5%

25.7%

80%
60%

16.0%

40%

30.0%

20%

28.4%

37.7%

Work/Study

Shopping

17.1%

14.7%

19.4%

19.9%

37.3%

21.3%

17.5%

22.7%

21.0%

31.6%

35.0%

29.4%

24.3%

26.5%

25.7%

30.1%

Parties

CulturalleisureMedicalactivities

Total

29.5%

37.1%
26.5%

35.9%

Eating out

Physical
exercise0%

No contact

ONPC

OPC

18.2%

23.6%

18.5%

21.3%

33.2%

PCNPC

Figure 7. 1. Distributions of social contact modes by activity

100%
80%

17.4%
11.9%

40.3%

60%

20.6%

16.9%

28.4%

37.7%

Work/Study

Shopping

26.5%

35.9%

Eating out

Physical
exercise0

1–2 persons

16.3%

43.8%

29.6%

26.5%

25.7%

30.1%

CulturalleisureMedicalactivities

Total

20.5%

25.6%

24.3%

Parties

3–4 persons

24.0%

16.2%

18.8%

19.5%

0%

14.2%

29.2%

35.7%

23.6%

31.2%

16.9%

20%

23.6%

32.9%

14.4%

40%

21.7%

at least 5 persons

Figure 7. 2. Numbers of persons contacted by activity

Work/Study

Shopping

Eating out
1–2 persons

Physical
exercise
3–4 persons

Parties

CulturalleisureMedicalactivities

PCNPC

OPC

ONPC

PCNPC

OPC

ONPC

PCNPC

OPC

ONPC

PCNPC

OPC

ONPC

PCNPC

OPC

ONPC

PCNPC

OPC

ONPC

PCNPC

OPC

ONPC

PCNPC

OPC

16.3%
18.0%
23.1%
23.2%
24.5%
27.2%
27.7%
33.2%
33.6%36.7%
34.8%37.3%
36.3%
39.7%42.1%
44.1%
44.3%
44.8%44.8%
46.6%
51.4%
19.0%
58.6%
23.4%
59.4%
68.7%17.2%
25.2%
24.2%
22.0%
25.0%
24.2%
24.7%19.2%
26.8%
32.5%29.3%
26.8%22.0%
24.4%
23.6%
30.2%
27.2%26.2%
27.9%
64.8%
21.9%
59.6%
22.0%
58.6%
51.7%
51.4%
50.8%
14.0%
47.3%
44.1%
42.6%
41.8%
36.9%
33.5%35.9%
32.7%33.4%
31.6%
31.5%
29.4%
26.1%
25.0%
20.7%19.6%
18.6%17.3%

ONPC

100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%

Total

at least 5 persons

Figure 7. 3. Distributions of numbers of persons contacted under different modes by activity

104

Chapter Investigation of multi-faceted social contact decisions under the impacts of COVID-19:
oint analysis of contact mode choices and the number of contacted persons

7.3.2 Explanatory variables: Key candidates.3.2.1 Factors related to activity participation
1) erceptions of 3Cs at activity locations
An analysis was conducted with respect to the shares of the respondents who perceived their daily
activity locations to be closed spaces with poor ventilation, crowded places, or places with close contact(i.e., 3Cs). Pooling data from all social contact settings, it is found that the share perceiving closed
places is only 18.5%, the share of crowded places is 30.0% and the share of close-contact settings is
34.7%. This can be interpreted that people are willing to visit places without 3Cs, especially those who
hold a low tolerance to poor ventilation during the COVID-19 pandemic. Among the seven contact
settings, five of them showed the highest share of having places with close contact. Crowded places
were mostly perceived with respect to the settings of shopping, cultural leisure, and parties. People
suffered more from poor ventilation during work/study and parties than in other settings. Details referto the Figure 7.4 of Supplementary Information.

33.1%

36.8%
33.9%

38.0%
36.0%

36.0%
30.1%

32.6%

Work/Study

Shopping

Eating out

20.5%

19.5%

16.6%

18.5%

15.7%

Physical
exercise

Closed spaces with poor ventilation

34.7%
30.0%

20.9%
18.3%

34.8%

28.7%

22.0% 22.5%
18.6%

36.7%
35.6%

Parties
Crowded places

CulturalleisureMedicalactivities

Total

Close-contact settings

Figure 7. 4. Perceptions of 3Cs at activity locations

2) erceived safety during activity participation
Only about half of respondents perceived that participation in activities was safe. Performing medical
activities were perceived to be the safest among all social contact settings, followed by the setting ofwork/study. The above observations are about themselves and their companions. More or less,respondents perceived that the safety levels during parties and cultural leisure are lowest for themselves,
while they perceived that participating in shopping and eating out has the least safety level for theircompanions. Details refer to the Figure 7.5 of Supplementary Information.

105

companions' safety

self-safety

Chapter Investigation of multi-faceted social contact decisions under the impacts of COVID-19:
oint analysis of contact mode choices and the number of contacted persons

Work/Study
Shopping
Eating out
Physical exercise
Parties
Cultural leisure
Medical activities
Total

7.1%
8.7%
7.0%
8.3%
9.6%
8.3%
6.3%
7.8%

14.8%
19.8%
19.7%
16.6%
19.5%
19.2%
14.1%
17.7%

Work/Study
Shopping
Eating out
Physical exercise
Parties
Cultural leisure
Medical activities
Total

7.3%
8.5%
7.4%
8.2%
9.7%
8.0%
6.5%
7.8%

16.7%
22.6%
20.2%
18.6%
19.0%
19.4%
14.3%
18.9%

0%
Strongly disagree

10%

21.8%

33.0%

23.4%
24.7%
25.4%
24.0%
24.7%
20.7%

20%

23.4%
13.0%
14.7%
17.1%
14.8%
15.7%
24.4%
17.6%

35.1%
34.0%
32.5%
32.1%
32.1%
34.5%

23.3%

33.7%

24.8%

32.1%

24.4%
27.5%
26.3%
25.3%
25.8%
23.5%

33.9%
25.2%

30%

Somewhat disagree

19.1%
12.4%
14.2%
15.3%
16.1%
15.4%
21.7%
16.2%

32.1%
30.7%
31.5%
29.9%
31.3%
31.9%

40%

50%

Neither

60%

70%

80%

Somewhat agree

90%

100%

Strongly agree

Figure 7. 5. Perceived safety during activity participation

3) rotective measures taken during activity participation
Recognizing the above perceived safety level (relatively low, on average), the shares of wearing a mask
and disinfecting hands are about 60% by pooling data of all contact settings. Among all settings, theshares of taking such protective measures are highest for shopping and medical activities. This may be
due to the high frequency of shopping and concerns about infection risks in hospitals. As expected, theshares of taking protective measures when participating in parties are lowest, which are however not
significantly different from those for physical exercise and cultural leisure. It is further surprising thatthe shares for eating out are much lower than those for work/study, even though eating out is usuallyrisker in terms of infection. Details refer to the Figure 7.6 of Supplementary Information.

73.9%
69.0%
63.2%
58.5%

Work/Study

70.6%
67.2%

63.1%
56.0%

Shopping

Eating out

61.5% 61.8%
54.3%

53.6%
49.9%

46.1%

Physical
exercise

Parties

Wear a mask

54.3%
50.1%

CulturalleisureMedicalactivities

Disinfect hands

Figure 7. 6 Protective measures taken during activity participation

106

Total

Chapter Investigation of multi-faceted social contact decisions under the impacts of COVID-19:
oint analysis of contact mode choices and the number of contacted persons
4) elf-efficiency and others’ efficiency

In all contact settings, respondents’ belief in other people’s capacity to protect others (including
respondents) (i.e., others’ efficiency) is higher (shares of “high” and “very high”) than that in their own
capacity to protect themselves (i.e., self-efficiency). Respondents reported the highest capacity toprotect themselves during shopping and medical activities, while reported that other people’s capacity
to protect others (including respondents) during medical activities is the highest, which is probably
because of the higher belief in hospitals. Being aware of higher infection risks during parties and culturalleisure activities, the perceived self-efficiency and others’ efficiency were lower than in other contact

others‘ efficiency

self-efficiency

settings. Details refer to the Figure 7.7 of Supplementary Information.

Work/Study 5.7% 9.5%
Shopping 3.9% 9.6%
Eating out 3.8% 11.7%
Physical exercise 4.9% 11.6%
13.6%
Parties 5.0%
Cultural leisure 5.0% 12.6%
Medical activities 3.5% 8.6%
Total 4.4% 10.6%

22.5%
21.0%

10%

24.7%
24.1%

25.6%

39.9%

19.0%

26.0%

36.8%

20.7%

25.8%

36.0%

19.6%

26.2%

37.4%

21.7%

18.9%

39.3%

23.5%

18.2%
Work/Study 3.3% 7.3%
14.4%
Shopping 3.9% 8.3%
18.2%
Eating out 2.5% 7.7%
21.7%
Physical exercise 3.0% 7.8%
21.6%
Parties 3.6% 9.2%
21.4%
Cultural leisure 3.4% 7.4%
Medical activities 1.9%4.4% 12.3%
17.3%
Total 3.1% 7.3%

0%

37.6%
41.5%

27.0%

38.9%

22.6%

38.9%

32.3%

45.5%

27.9%

44.4%

27.1%

41.5%

26.0%

42.2%

23.3%

43.0%

24.8%

34.2%

47.2%

41.4%

30.9%

20%

30%

40%

50%

60%

70%

Very low

Low

NeitherHigh

Very high

80%

90%

100%

Figure 7. 7. Self-efficiency and others’ efficiency
5) Attitudes toward necessity and punishment related to activity participation

An analysis was conducted about people’s attitudes toward whether they think performing a certain
activity during the pandemic should be published or not and whether they think performing the activity
is necessary even at the infection risk or not. In the case of total data, the shares of supporting
punishment and necessity are almost equally larger than 40% (“somewhat agree” and “totally agree”),
which are larger than the unsupported shares. The largest support share of activity necessity is
work/study (very close to 50%), while the support share of punishment to work/study is less than 40%.
The second largest support share of activity necessity is participation in medical activities, for which
only about 32% of respondents supported the punishment of medical activities. The largest more than
52% of respondents supported the punishment to party participation, while its necessity-support share
was about 37%. But the smallest necessity-support share does not necessarily correspond to the largest
punishment-support share, as shown by the shares for eating out. Details refer to the Figure 7.8 of107

Chapter Investigation of multi-faceted social contact decisions under the impacts of COVID-19:
oint analysis of contact mode choices and the number of contacted persons

Doing activity should bepunished
Doing activity is necessary

Supplementary Information.
Work/Study
Shopping
Eating out
Physical exercise
Parties
Cultural leisure
Medical activities
Total

15.8%

15.4%

19.1%

20.3%

17.1%

25.4%

13.8%
20.3%

12.3%

18.3%

21.6%

22.5%

14.4%

25.1%

17.2%

20.5%

22.1%

15.1%

24.7%

18.6%

20.0%

22.8%

14.5%

22.4%

13.9%

28.1%

19.3%

20.6%

17.2%

21.1%

25.5%

15.6%

21.9%

15.8%

23.9%

23.0%

15.4%

16.0%

17.3%

23.6%

14.5%

15.1%

11.6%

25.7%

20%

22.1%

29.4%
18.1%

24.3%

30%

40%

Somewhat disagree

19.9%

23.2%

14.2%

10%

18.0%

30.1%

14.1%

20.7%

16.4%

27.3%

24.3%

30.5%

14.1%

26.7%

25.8%

10.9%

13.4%

22.4%

25.0%

13.9%

12.5%

Totally disagree

21.4%

23.3%

23.9%

0%

20.0%
29.3%

20.6%

15.6%

Work/Study
Shopping
Eating out
Physical exercise
Parties
Cultural leisure
Medical activities
Total

28.6%

20.6%

50%

Neither

14.1%

24.3%

60%

70%

Somewhat agree

16.4%

80%

90%

100%

Totally agree

Figure 7. 8. Perceptions of activity-related necessity and punishment
6) Activity attributes

Even under the COVID-19 pandemic, about 60% of respondents had to work/study in a daily or almost
daily basis. Going to shopping once or twice a week accounted for the largest share (55.7%), among all
shopping frequency categories. Participation less than once a month was dominant for parties, culturalleisure, and medical activities as well as eating out. For physical exercise, less than once a month andonce or twice a week are the two main activity frequencies. Details refer to the Figure 7.9 of
Supplementary Information.
Work/Study 3.2%

10.5%

Shopping 2.7%

12.3%

9.1%

18.6%
22.5%

55.7%

47.3%

27.3%

Eating out 3.4%
Physical exercise

4.2%

Parties

6.1%

Cultural leisure

6.3%

Medical activities

5.2%

Total

4.2%

0%

58.6%

31.3%

19.4%

6.7%
18.3%

27.3%

53.4%

17.8%

19.9%

57.6%

16.9%

65.6%
37.6%

10%

20%

19.3%
19.8%

30%

40%

50%

25.3%

60%

70%

80%

Less than once a month Once or twice a month

Once or twice a week

Daily or almost daily

108

15.3%

5.3%

13.1%

6.1%
7.1% 2.8%
13.2%

A few times a year

Figure 7. 9. Activity frequencies

3.8%

90%

100%

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Comparing with the travel distance for home to work/study locations that may be largely associatedwith people’s daily action space, medical activities had a similar share of shorter distance (< 2km),
while culture leisure’s longer travel distance (5km or longer) is similar to that of work/study. The travel
distances for shopping and physical exercise are similarly shorter than 2km. Respondents participatingin cultural leisure prefer a longer distance over a shorter distance. More or less, eating out and parties
showed similar distance distributions. In the case of medical activities, the shares of shorter and longer
distances are very similar. Details refer to the Figure 7.10 of Supplementary Information.

Work/Study

23.5%

Shopping

19.5%

Eating out

11.5%
8.8%
13.8%

Total

16.0%

0.0%

10.0%

22.8%

17.5%
21.9%

29.2%

20.4%

18.7%

29.6%

23.6%

27.7%
20.6%
20.7%

20.0%

25.1%

30.9%

18.1%

Medical activities

18.7%
30.1%

22.1%

18.8%

Parties

19.8%

24.2%

12.4%

Physical exercise

Cultural leisure

13.0%

30.0%

40.0%

8.7%
12.7%

10.5%
15.0%

24.9%

20.5%

30.4%

20.2%

15.0%

28.6%

20.2%

14.5%

50.0%

60.0%

70.0%

80.0%

Less than 1 km

1 km or longer, but less than 2 km

2 km or longer, but less than 5 km

5 km or longer, but less than 10 km

90.0%

100.0%

Longer than 10 km

Figure 7. 10. Travel distance from home to activity locations

Concerning the shares of whether people performed a certain activity with acquaintances(including friends, but except their household members), expectedly, participating in a party had thelargest share (33.7%), followed by cultural leisure, while the shares for shopping and medical activities
were the lowest. Eating out and physical exercise showed similar shares (about 20%). Details refer tothe Figure 7.11 of Supplementary Information.

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33.7%

25.4%
20.9%

19.2%
13.7%
6.5%

6.1%
Shopping

Eating out

Physical
exercise

Parties

Cultural leisure

Medicalactivities

Total

Figure 7. 11. Shares of activity participation with acquaintances.3.2.2 Risk-related psychological factors
1) Risk perception and reliability of information
The shares of people perceiving a higher risk in crowded trains and buses are between 60% and 70%(“somewhat agree” and “strongly agree”), which were much higher than the shares of a higher risk
perceived in residence regions (more than 40%) and residence country (between 30% and 40%). Thus,
it seems that people’s risk perception is negatively associated with spatial scales; however, the sharesof “disagree” and “agree” are almost equal for the risk perception at the level of residence region, whilethe “disagree” shares are larger than the “agree” shares at the level of the residence country. Concerningsocial contact settings, the shares of higher risk in crowded public transport are especially higher in thecases of participating in medical activities, shopping, and work/study, while there are differences acrossthe settings, although the differences are not remarkable. For information reliability (provided by the
local government), more than 65% of the respondents trusted the information, and only about 10% did
not trust it, where the shares of reliability assessment are very similar across social contact settings.
Details refer to the Figure 7.12 of Supplementary Information.

110

The infection risk in
The COVID-19
The infection risk in your residence region outbreak is severe in
Reliability of thecrowded train or bus is (prefecture, province, your residence country information from local
highstate) is highthan other countries
government

Chapter Investigation of multi-faceted social contact decisions under the impacts of COVID-19:
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22.1%
48.0%
Work/Study 3.0% 8.7%
23.1%
49.5%
Shopping 2.9% 8.2%
22.6%
48.5%
Eating out 3.2% 8.5%
22.1%
47.6%
Physical exercise 3.1% 8.8%
21.3%
48.4%
Parties 3.0% 8.2%
21.7%
46.9%
Cultural leisure 3.0% 8.3%
22.1%
49.6%
Medical activities 2.7% 8.2%
22.3%
48.6%
Total 3.0% 8.4%
18.7%
21.9%
22.7%
Work/Study
20.4%
23.8%
23.1%
Shopping
23.4%
23.8%
21.7%
Eating out
20.4%
20.7%
22.6%
Physical exercise
23.5%
18.8%
21.2%
Parties
24.0%
20.0%
20.9%
Cultural leisure
21.5%
23.7%
22.0%
Medical activities
21.5%
22.3%
22.2%
Total
14.1%
18.3%
21.7%
Work/Study
15.7%
20.8%
20.5%
Shopping
18.2%
21.9%
19.7%
Eating out
15.9%
20.0%
20.3%
Physical exercise
18.3%
20.9%
19.3%
Parties
18.8%
20.3%
19.0%
Cultural leisure
16.2%
21.4%
19.7%
Medical activities
16.5%
20.6%
20.1%
Total
19.6%
38.7%
Work/Study 6.4% 8.5%
18.4%
37.6%
Shopping 6.5% 8.4%
20.6%
37.5%
Eating out 7.9% 9.8%
20.3%
37.8%
Physical exercise 7.3% 9.0%
21.4%
37.4%
Parties 8.3% 10.5%
8.8%
10.2%
21.9%
37.0%
Cultural leisure
18.4%
37.7%
Medical activities 6.5% 8.4%
19.7%
37.7%
Total 7.2% 9.1%

0%strongly disagree

10%

20%

somewhat disagree

30%

40%

50%

neither agree nor disagree

60%

18.2%
16.3%
17.1%
18.4%
19.2%
20.1%
17.5%
17.8%
25.0%
11.7%
22.0%
10.8%
21.5%
9.6%
24.6%
11.6%
25.2%
11.2%
24.1%
11.1%
21.7%
11.0%
23.1%
10.9%
31.0%
14.9%
30.2%
12.8%
27.8%
12.3%
29.4%
14.3%
27.1%
14.4%
27.7%
14.2%
29.3%
13.4%
29.2%
13.6%
26.8%
29.1%
24.2%
25.6%
22.5%
22.2%
29.0%
26.3%

70%

80%

somewhat agree

90%

100%

strongly agree

Figure 7. 12. Risk perception and reliability of risk information2) riggers for behavioral changes
Regarding the triggers for behavioral changes, more than 60% of respondents answered that theychanged their behaviors due to the influences of multiple triggers, irrespective of social contact settings.
In contrast, only less than 15% of respondents disagreed with the influences of multiple triggers. Amongthe three triggers investigated in the survey, people were mostly triggered by the formal rules from thegovernment in the sense that the corresponding “agree” shares are much higher than the other two
triggers: i.e., the rules from the workplace/organization and the number of infected cases. This isprobably because the rules from the government are more mandatory and authoritative. The rules fromthe government and the number of infected cases had larger impacts on the behavioral changes of thosepeople who had shopping and medical activities during the pandemic. The rules from theworkplace/organization/affiliation especially affected the behavioral changes related to work/study. The“agree” shares for leisure activities (eating out, physical exercise, parties, cultural leisure) were not aslarge as the shares for mandatory/maintenance activities (work/study, shopping, medical activities).
Details refer to the Figure 7.13 of Supplementary Information.

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The formal rules fromworkplace/organization/affilia
The number of infected casestion

The formal rules from thegovernment

Chapter Investigation of multi-faceted social contact decisions under the impacts of COVID-19:
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Work/Study

3.2% 6.4%

16.2%

Shopping 2.9% 5.9%

42.7%

14.0%

31.4%

42.4%

34.8%

Eating out

3.2% 6.5%

15.8%

44.8%

29.7%

Physical exercise

3.2% 6.2%

16.6%

44.2%

29.8%

Parties

3.2% 6.0%

16.8%

45.5%

28.5%

Cultural leisure3.4% 6.3%

17.2%

Medical activities 2.6%5.9%

13.7%

Total 3.0% 6.1%

15.4%

Work/Study

4.9% 7.0%

45.4%

27.6%

43.2%

34.6%

43.7%

20.0%

31.7%
42.3%

25.8%

Shopping

7.0%

7.9%

26.9%

35.5%

22.6%

Eating out

6.5%

8.0%

25.6%

38.3%

21.6%

Physical exercise

5.5%

Parties
Cultural leisure

7.5%

23.5%

40.2%

23.4%

4.8% 7.1%

24.1%

41.1%

22.9%

7.8%

23.8%

40.9%

22.0%

5.4%

Medical activities

6.6%

7.9%

Total

6.0%

7.7%

26.5%
24.8%

Work/Study

4.9% 7.0%

18.7%

Shopping

5.1% 7.0%

17.1%

Eating out
Physical exercise

36.1%

22.9%

38.5%

23.0%

40.1%

29.3%

38.9%

31.9%

8.1%

19.0%

41.0%

26.2%

5.2% 7.2%

19.7%

40.5%

27.5%

5.8%

Parties

5.2%

8.1%

21.0%

40.8%

Cultural leisure

5.6%

8.1%

21.2%

40.4%

Medical activities
Total

4.7% 7.1%
5.2%

0%strongly disagree

7.4%

10%

16.7%

somewhat disagree

24.8%

39.5%

18.6%

20%

24.9%

32.0%

40.0%

30%

40%

50%

neither agree nor disagree

28.8%

60%

70%

80%

somewhat agree

90%

100%

strongly agree

Figure 7. 13. Triggers for behavioral changes

3) Acceptance to change the duration/timing for activity participation
More than 70% of people were willing to change the duration/timing of performing activities (i.e., theshares of “somewhat agree” and “agree”). The “disagree” shares were just about 10%. Among the sevencontact settings, the willingness was especially higher for medical activities and shopping, while thewillingness for other settings was almost similar. Details refer to the Figure 7.14 of Supplementary
Information.

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Work/Study

3.3% 6.3%

Shopping

3.2% 5.7%

Eating out

3.8% 6.7%

18.4%

43.1%

28.0%

Physical exercise

3.4% 6.7%

18.7%

42.6%

28.6%

Parties

3.7% 7.1%

18.9%

43.1%

27.2%

7.3%

19.2%

42.4%

27.3%

Cultural leisure

3.8%

Medical activities

3.0% 5.7%

Total

3.4% 6.3%

0%strongly disagree

19.0%

42.7%

16.5%

41.5%

15.9%

20%

33.0%

42.3%

17.7%

10%

28.8%

33.1%

42.4%

30%

somewhat disagree

40%

50%

30.1%

60%

neither agree nor disagree

70%

80%

somewhat agree

90%

100%

strongly agree

Figure 7. 14. Acceptance to change the duration/timing for activity participation. Integrated copula-based social contact decision modeling
As revealed from the above aggregate analyses, choices of social contact modes and the number ofcontacted persons seem not to be independent of each other. Therefore, it is worth exploring how tomodel these two decision variables together for a better understanding of people’s decisions on social
contact. Considering the features of the two variables, choices of social contact modes are presented bya multinomial logit model and the number of contacted persons based on an ordered logit model. The
joint modeling is done based on a copula approach.

. .1 Modeling of social contact mode choices
Let n (n=1, 2, …, N) be the index for individuals, v (v=1, 2, …, V) be the index for explanatory variables
and i (i=1, 2, …, I) be the index for social contact modes. To incorporate the heterogeneity of social
contact decisions across activity settings for the same person, αg is a parameter introduced to capture
explanatory variables’ influencing magnitudes that may be sensitive to or heterogeneous across activity
settings (g=1,2, …, G). Thus, αg can be called a sensitivity parameter. Let hnig indicate the utility
obtained by individual n who chooses contact mode i for activity setting g.
ℎ𝑛𝑖𝑔 = ∑𝑣 𝛼𝑔 𝛽𝑖𝑣 𝑥𝑛𝑖𝑣 + 𝜀𝑛𝑖𝑔

(7-1)

Here, xniv is the explanatory variable for mode i, βiv is the corresponding parameter to be estimated,and εnig is the random error term following an independent and identical Gumbel distribution. Based on
the random utility maximization principle, individual n chooses alternative i only when the utility of113

Chapter Investigation of multi-faceted social contact decisions under the impacts of COVID-19:
oint analysis of contact mode choices and the number of contacted persons

alternative i is larger than that of other alternatives:(7-2)ℎ𝑛𝑖𝑔 > 𝑚𝑎𝑥𝑖≠𝑗 ℎ𝑛𝑗𝑔
Then, equations (7-1) and (7-2) can be rewritten as:(7-3)∑𝑣 𝛼𝑔 𝛽𝑖𝑣 𝑥𝑛𝑖𝑣 > 𝑚𝑎𝑥𝑖≠𝑗 ℎ𝑛𝑗𝑔 − 𝜀𝑛𝑖𝑔

Let rnig be a dummy variable indicating whether individual n chooses contact mode i (rnig=1) ornot (rnig=0) for activity setting g. It is equal to 1 only if the equation (7-3) is true, otherwise it is zero.
If we define:(7-4)

𝜑𝑖𝑔 = {𝑚𝑎𝑥𝑖≠𝑗 ℎ𝑛𝑗𝑔 } − 𝜀𝑛𝑖𝑔

Then the implied marginal distribution of 𝐹(∑𝑣 𝛼𝑔 𝛽𝑖𝑣 𝑥𝑛𝑖𝑣 ) (i.e., choice probability) can beformed as follows:exp(∑ 𝛼 𝛽 𝑥𝐹(∑𝑣 𝛼𝑔 𝛽𝑖𝑣 𝑥𝑛𝑖𝑣 ) = 𝑃(𝜑𝑖𝑔 < ∑𝑣 𝛼𝑔 𝛽𝑖𝑣 𝑥𝑛𝑖𝑣 ) = ∑ exp(∑𝑣 𝛼𝑔 𝛽𝑖𝑣 𝑛𝑖𝑣
𝑥
𝑗

𝑣

)

𝑔 𝑗𝑣 𝑛𝑗𝑣 ), 𝑗 = 1,2, … , 𝐽, 𝑔 = 1,2, … , 𝐺(7-5). .2 Number of contacted persons
In this study, the number of contacted persons was measured using an ordinal scale. Thus, an orderedlogit model is selected to explain the number of contacted persons during performing an activity. Let k(k=1, 2, …, K) be the index for the number of contacted persons and 𝑠𝑛𝑖𝑔 be the number of contacted
persons corresponding to contact mode i for activity setting g. 𝑠𝑛𝑖𝑔 is observed only if rnig is equal to
1. The underlying propensity of the number of contacted persons with respect to contact mode i is shownas follows:∗∗𝑠𝑛𝑖𝑔= ∑𝑣 𝛼𝑔 𝛾𝑣 𝑧𝑛𝑖𝑣 + 𝜂𝑛𝑖𝑔 , 𝑠𝑛𝑖𝑔 = 𝑘 𝑖𝑓 𝜏𝑘−1 < 𝑠𝑛𝑖𝑔
< 𝜏𝑘

(7-6)

Here, 𝑧𝑛𝑖𝑣 is the explanatory variable for the number of contacted persons, and 𝛾𝑔 is theunknown parameter. 𝜂𝑞𝑖𝑔 represents the error term following the Gumbel distribution. 𝜏𝑘 terms∗( 𝜏0 = −∞, 𝜏𝐾 = +∞ ) indicate the thresholds of 𝑠𝑛𝑖𝑔
. Let 𝐺(𝑥) be the cumulative distributionfunction of a standard logistic distribution. Then the marginal distribution of 𝐺(𝜏𝑘 − ∑𝑣 𝛼𝑔 𝛾𝑣 𝑧𝑛𝑖𝑣 ) is
formed as follows.

exp (𝜏 −∑ 𝛼 𝛾 𝑧)

𝐺(𝜏𝑘 − ∑𝑣 𝛼𝑔 𝛾𝑣 𝑧𝑛𝑖𝑣 ) = 1+exp (𝜏𝑘 −∑𝑣 𝛼𝑔 𝑣𝛾 𝑛𝑖𝑣
𝑧 )
𝑘

𝑣

𝑔 𝑣 𝑛𝑖𝑣

114

(7-7)

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The probability that the number of contacted persons being k is observed is written as follows.
𝑃(𝑠𝑛𝑖𝑔 = 𝑘) = 𝑃(𝜂𝑘𝑔 < 𝜏𝑘 − ∑𝑣 𝛼𝑔 𝛾𝑣 𝑧𝑛𝑖𝑣 ) − 𝑃(𝜂𝑘𝑔 < 𝜏𝑘−1 − ∑𝑣 𝛼𝑔 𝛾𝑣 𝑧𝑛𝑖𝑣 ) = 𝐺(𝜏𝑘 − ∑𝑣 𝛼𝑔 𝛾𝑣 𝑧𝑛𝑖𝑣 ) −𝐺(𝜏𝑘−1 − ∑𝑣 𝛼𝑔 𝛾𝑣 𝑧𝑛𝑖𝑣 )(7-8)
In equation (7-8), because 𝜏𝑘 follows the ordering condition (−∞ < 𝜏1 < 𝜏2 < ⋯ < 𝜏𝐾−1 <+∞), 𝑃(𝜂𝑘𝑔 < 𝜏0 − ∑𝑣 𝛼𝑔 𝛾𝑣 𝑧𝑛𝑖𝑣 ) is equal to 0 and 𝑃(𝜂𝑘𝑔 < 𝜏𝐾 − ∑𝑣 𝛼𝑔 𝛾𝑣 𝑧𝑛𝑖𝑣 ) is equal to 1.

. .3 oint copula model of social contact
Here, the notion that contact mode choices and the number of contacted persons means that the above
two error terms (εnig and ηnig) are not independent. In other words, they should follow a joint distribution(or a bivariate distribution). It is not easy to estimate models with such a joint distribution. To overcome
this difficulty, the copula approach has been widely applied, which can use marginal distributions anddependence parameters to represent the joint distribution (Sklar, 1973; Nelsen, 2007; Schmidt, 2007;
Trivedi et al., 2007; Meloni et al., 2011; Irannezhad et al., 2017). In other words, with the copula
approach, it is not necessary to directly estimate the joint distribution, making the estimation tasks mucheasier. Moreover, compared with other multivariate correlation methods (e.g., Pearson, Kendall’s tau,
Spearman’s rho), copulas can obtain nonlinear central dependence and tail dependence in both
symmetric and asymmetric forms (Frey et al., 2001; Trivedi et al., 2007). Because of the flexibility ofchoosing marginal distributions, the copula-based approach allows complex dependency structures
between discrete variables (Huang et al., 2022; Seyedabrishami et al., 2019; Rashidi and Mohammadian,2016), continuous variables (Sener et al, 2010; Wali et al., 2022; Zilko et al., 2016; Kuwano et al., 2011),
and ordered variables (Eluru et al., 2010; Laman et al., 2018; Wang et al., 2015). Only the copula
approach allows such combinations with different types of marginal distributions (Bhat et al., 2009;
Zhang et al., 2012; Habib et al., 2009; Spissu et al., 2009; Irannezhad et al., 2017; Nguyen et al., 2017;
Rith et al., 2019; Shabanpour et al., 2017).
To the best knowledge of the authors, it is the first time in the social contact research to apply the
copula approach. By referring to previous studies (Meloni et al., 2011; Jafari Shahdani et al., 2021;
Keya et al., 2019), the joint representation of social contact mode choices and the number of contacted
persons is described below.
According to Sklar’s theorem (Sklar, 1973), any joint distribution of multiple variables can be
represented

in

terms

of

a

uniquecopula function,

in

which

the continuous

marginal

probability distribution of each variable is uniform over the interval [0, 1 . For the bivariate case in thisstudy:

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𝐶𝜃 (𝑢1g , 𝑢2g ) = 𝑃(𝑈1g < 𝑢1g , 𝑈2g < 𝑢2g )Here, 𝐶(. , . ) is a copula function, 𝜃 is dependence parameter vector of copula. 𝑈1g , 𝑈2g are
uniformly distributed random variables with support contained in [0,1 . In this study, copula approach
is applied to generate the joint multivariate distribution function with given marginals of contact modes
and the number of contacted persons, where 𝑢1g = 𝐹(∑𝑣 𝛼𝑔 𝛽𝑖𝑣 𝑥𝑛𝑖𝑣 ) and 𝑢2g = 𝐺(𝜏𝑘 −∑𝑣 𝛼𝑔 𝛾𝑣 𝑧𝑛𝑖𝑣 ). The joint distribution of random variables 𝜑𝑖𝑔 and 𝜂𝑘𝑔 can be expressed as:𝐶𝜃 (𝐹 (∑ 𝛼𝑔 𝛽𝑖𝑣 𝑥𝑛𝑖𝑣 ) , 𝐺 (𝜏𝑘 − ∑ 𝛼𝑔 𝛾𝑣 𝑧𝑛𝑖𝑣 ))𝑣𝑣= 𝑃 (𝑈1g < 𝐹 (∑ 𝛼𝑔 𝛽𝑖𝑣 𝑥𝑛𝑖𝑣 ) , 𝑈2g < 𝐺 (𝜏𝑘 − ∑ 𝛼𝑔 𝛾𝑣 𝑧𝑛𝑖𝑣 ))
𝑣

= 𝑃(𝐹

−1

𝑣

(𝑈1g ) < ∑𝑣 𝛼𝑔 𝛽𝑖𝑣 𝑥𝑛𝑖𝑣 , 𝐹−1(𝑈2g ) < 𝜏𝑘 − ∑𝑣 𝛼𝑔 𝛾𝑣 𝑧𝑛𝑖𝑣 )= 𝑃(𝜑𝑖𝑔 < ∑𝑣 𝛼𝑔 𝛽𝑖𝑣 𝑥𝑛𝑖𝑣 , 𝜂𝑘𝑔 < 𝜏𝑘 − ∑𝑣 𝛼𝑔 𝛾𝑣 𝑧𝑛𝑖𝑣 )(7-10)

where 𝐹 −1 (. ) is the inverse univariate cumulative distribution function.
Let “Cg = i” be the indication of “𝑟𝑛𝑖𝑔 =1” for each contact mode (i=1,2,3,4 represent the choiceof “no contact”, “ONPC”, “OPC”, “PCNPC”, respectively). If individual n has no contact with others,
the number of contacted persons by him/her is zero. Because there are three ordered alternatives of thenumber of contacted persons, two thresholds are included in the model (k=1, 2, 3). Then the bivariate
joint probability of social contact decisions is expressed as follows.

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Chapter Investigation of multi-faceted social contact decisions under the impacts of COVID-19: oint analysis of contact mode choices andthe number of contacted persons

𝐹 (∑ 𝛼𝑔 𝛽𝑖𝑣 𝑥𝑛𝑖𝑣 )𝑖𝑓 𝐶𝑀𝑔 = 1𝑣

𝑃 (𝜑𝑖𝑔 < ∑ 𝛼𝑔 𝛽𝑖𝑣 𝑥𝑛𝑖𝑣 , 𝜂𝑘𝑔 < 𝜏1 − ∑ 𝛼𝑔 𝛾𝑣 𝑧𝑛𝑖𝑣 )𝑃(𝐶𝑀𝑔 = 𝑖, 𝑠𝑛𝑖𝑔 = 𝑘) =𝑣

𝑖𝑓𝐶𝑀𝑔 = 𝑖, 𝑖 = 2,3,4; 𝑠𝑛𝑖𝑔 = 1𝑣

𝑃 (𝜑𝑖𝑔 < ∑ 𝛼𝑔 𝛽𝑖𝑣 𝑥𝑛𝑖𝑣 , 𝜏1 − ∑ 𝛼𝑔 𝛾𝑣 𝑧𝑛𝑖𝑣 < 𝜂𝑘𝑔 < 𝜏2 − ∑ 𝛼𝑔 𝛾𝑣 𝑧𝑛𝑖𝑣 ) 𝑖𝑓𝐶𝑀 = 𝑖, 𝑖 = 2,3,4; 𝑠𝑔𝑛𝑖𝑔 = 2𝑣
𝑣
𝑣
𝑖𝑓𝐶𝑀𝑔 = 𝑖, 𝑖 = 2,3,4; 𝑠𝑛𝑖𝑔 = 3
𝑃 (𝜑𝑖𝑔 < ∑ 𝛼𝑔 𝛽𝑖𝑣 𝑥𝑛𝑖𝑣 , 𝜂𝑘𝑔 > 𝜏2 − ∑ 𝛼𝑔 𝛾𝑣 𝑧𝑛𝑖𝑣 ){𝑣
𝑣
𝐹 (∑ 𝛼𝑔 𝛽𝑖𝑣 𝑥𝑛𝑖𝑣 )𝑖𝑓 𝐶𝑀𝑔 = 1

𝑣

𝐶𝜃 (𝐹 (∑ 𝛼𝑔 𝛽𝑖𝑣 𝑥𝑛𝑖𝑣 ) , 𝐺 (𝜏1 − ∑ 𝛼𝑔 𝛾𝑣 𝑧𝑛𝑖𝑣 ))𝑣=

𝑖𝑓𝐶𝑀𝑔 = 𝑖, 𝑖 = 2,3,4; 𝑠𝑛𝑖𝑔 = 1𝑣

𝑃 (𝜑𝑖𝑔 < ∑ 𝛼𝑔 𝛽𝑖𝑣 𝑥𝑛𝑖𝑣 , 𝜂𝑘𝑔 < 𝜏2 − ∑ 𝛼𝑔 𝛾𝑣 𝑧𝑛𝑖𝑣 ) − 𝑃(𝜑𝑖𝑔 < ∑ 𝛼𝑔 𝛽𝑖𝑣 𝑥𝑛𝑖𝑣 , 𝜂𝑘𝑔 < 𝜏1 − ∑ 𝛼𝑔 𝛾𝑣 𝑧𝑛𝑖𝑣 ) 𝑖𝑓𝐶𝑀 = 𝑖, 𝑖 = 2,3,4; 𝑠𝑔𝑛𝑖𝑔 = 2𝑣𝑣
𝑣
𝑣
𝑖𝑓𝐶𝑀𝑔 = 𝑖, 𝑖 = 2,3,4; 𝑠𝑛𝑖𝑔 = 3
𝑃 (𝜑𝑖𝑔 < ∑ 𝛼𝑔 𝛽𝑖𝑣 𝑥𝑛𝑖𝑣 ) − 𝑃(𝜑𝑖𝑔 < ∑ 𝛼𝑔 𝛽𝑖𝑣 𝑥𝑛𝑖𝑣 , 𝜂𝑘𝑔 < 𝜏2 − ∑ 𝛼𝑔 𝛾𝑣 𝑧𝑛𝑖𝑣 ){𝑣𝑣𝑣
𝐹 (∑ 𝛼𝑔 𝛽𝑖𝑣 𝑥𝑛𝑖𝑣 )𝑖𝑓 𝐶𝑀𝑔 = 1

𝑣

𝐶𝜃 (𝐹 (∑ 𝛼𝑔 𝛽𝑖𝑣 𝑥𝑛𝑖𝑣 ) , 𝐺 (𝜏1 − ∑ 𝛼𝑔 𝛾𝑣 𝑧𝑛𝑖𝑣 ))𝑣=

𝑖𝑓𝐶𝑀𝑔 = 𝑖, 𝑖 = 2,3,4; 𝑠𝑛𝑖𝑔 = 1

𝑣

𝐶𝜃 (𝐹 (∑ 𝛼𝑔 𝛽𝑖𝑣 𝑥𝑛𝑖𝑣 ) , 𝐺 (𝜏2 − ∑ 𝛼𝑔 𝛾𝑣 𝑧𝑛𝑖𝑣 )) − 𝐶𝜃 (𝐹 (∑ 𝛼𝑔 𝛽𝑖𝑣 𝑥𝑛𝑖𝑣 ) , 𝐺 (𝜏1 − ∑ 𝛼𝑔 𝛾𝑣 𝑧𝑛𝑖𝑣 ))
𝑣

{

𝑣

𝑣

𝑣

𝐹 (∑ 𝛼𝑔 𝛽𝑖𝑣 𝑥𝑛𝑖𝑣 ) − 𝐶𝜃 (𝐹 (∑ 𝛼𝑔 𝛽𝑖𝑣 𝑥𝑛𝑖𝑣 ) , 𝐺 (𝜏2 − ∑ 𝛼𝑔 𝛾𝑣 𝑧𝑛𝑖𝑣 ))𝑣𝑣

𝑖𝑓𝐶𝑀𝑔 = 𝑖, 𝑖 = 2,3,4; 𝑠𝑛𝑖𝑔 = 2
𝑖𝑓𝐶𝑀𝑔 = 𝑖, 𝑖 = 2,3,4; 𝑠𝑛𝑖𝑔 = 3𝑣(7-11)

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There is no general agreement about which copula approach among the copula families should be
used for a specific research target (Nelsen, 2007; Trivedi et al., 2007; Schmidt, 2007). Generally
speaking, if people are more likely to have contact in normal times, no matter what kinds of contact
modes are involved, it is reasonable to speculate that they would contact more persons. Thus, thesetypes of contact decisions would be positively dependent. For such situations, adopting Gumbel,
Clayton, or Joe copulas is obviously suitable. However, such positive associations may not always beobserved in the context of COVID-19, because people may want to reduce the number of contacted
persons for avoiding the infection in the case of, for example, having physical contact with others. For
accommodating such possibilities, copulas allowing for negative dependence between these two typesof contact decisions should also be an option. Therefore, in this study, five major copulas functions(FGM, Frank, Gumbel, Clayton, and Joe) are estimated and compared with a model assuming
independence between the two social contact decision variables to find out the most suitable model.
These copulas are introduced below.
F

copulaThe FGM (Farlie–Gumbel–Morgenstern) copula was proposed by Morgenstern (1956) and further
discussed by Gumbel (1960) and Farlie (1960). In the FGM copula function, the dependence parameter𝜃 must range between [-1, 1 , and the correlation between variables is independent when 𝜃 = 0. The
FGM copula allows either negative or positive dependence for the correlation between the uniform
marginals of u1g and u2g with the assumptions of asymptotic independence and radial symmetry in
dependence structure. In this bivariate case, the FGM copula is formed as:𝐶𝜃 (𝑢1g , 𝑢2g ) = 𝑢1g 𝑢2g (1 + 𝜃(1 − 𝑢1g )(1 − 𝑢2g ))(7-12)

Frank copula

The Frank copula is the only comprehensive Archimedean copula approach allowing for both positive
and negative dependence, which is proposed by Frank (1979). It imposes the assumption of asymptotic
independence and is suited for strong central dependency with weak tail dependency. The 𝜃 of Frankcopula has the threshold value of (−∞, +∞), but the Frank copula is equal to independent structure
when 𝜃 → 0. The Frank copula function for the bivariate case is defined as:1𝜃𝐶𝜃 (𝑢1g , 𝑢2g ) = − ln (1 +(𝑒 −𝜃𝑢1g −1)(𝑒 −𝜃𝑢2g −1))
𝑒 −𝜃 −1(7-13)

umbel copula
The Gumbel copula (also called as the Gumbel–Hougaard copula) is proposed by Gumbel (1960). The
Gumbel copula is an Archimedean copula suited for cases with strong right-tail dependence (strong118

Chapter Investigation of multi-faceted social contact decisions under the impacts of COVID-19:
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correlation at high values) and weak left-tail dependence (weak correlation at low values). The
dependence parameter 𝜃 of Gumbel copula must be [1,+∞), and the structure is independent when
𝜃 = 1. The function of Gumbel copula for the bivariate case is provided as:𝜃𝜃𝐶𝜃 (𝑢1g , 𝑢2g ) = exp (−((−𝑙𝑛𝑢1g ) + (−𝑙𝑛𝑢2𝑔 ) )1/𝜃 )(7-14)

Clayton copula
The Clayton copula is an Archimedean copula proposed by Clayton (1978) which only indicatesasymmetric positive dependence (𝜃𝜖(0, +∞)). It can achieve the Fréchet upper bound when 𝜃 → +∞.
The dependence parameter 𝜃 of Clayton copula should be larger than 0 and show independence when
𝜃 → 0. The Clayton copula is suited for strong left-tail dependence and weak right-tail dependence. The
form of Clayton copula for the bivariate case is expressed as𝐶𝜃 (𝑢1g , 𝑢2𝑔 ) = (𝑢1g −𝜃 + 𝑢2g −𝜃 − 1)−1/𝜃(7-15)

Joe copula
The Joe copula (Joe, 1993; Joe, 1997) is another copula function that only accounts for positive
dependence. Just like for Gumbel copula, the dependence parameter 𝜃 of the Joe copula should satisfy
the restricted condition of 𝜃𝜖[1, +∞) , and indicates the dependence when 𝜃 = 1 . The Joe copula
cannot achieve the Fréchet lower bound but achieves the Fréchet upper bound when 𝜃 → +∞ as well.
Some scholars think the Joe copula is more like to be the reverse of the Clayton copula instead of thatof the Gumbel copula (Bhat et al., 2009). The Joe copula function for the bivariate case is formed as:

𝜃

𝜃

𝜃

𝜃

𝐶𝜃 (𝑢1g , 𝑢2g ) = 1 − ((1 − 𝑢1g ) + (1 − 𝑢2g ) − (1 − 𝑢1g ) (1 − 𝑢2g ) )1/𝜃

(7-16). . Estimation and goodness of fit
In this study, the derived copula-based integrated model of social contact decisions is estimated by the
maximum likelihood method, where the log-likelihood function is expressed as follows:

𝐿𝐿 = ∑𝑁 ∑𝐼 ∑𝐾 ∑𝐺 𝑀𝑛𝑖𝑘𝑔 𝑙𝑛(𝑃(𝐶𝑀𝑔 = 𝑖, 𝑠𝑛𝑖𝑔 = 𝑘))𝑀𝑛𝑖𝑘𝑔 = 1[𝑟𝑛𝑖𝑔 = 1] × 1[𝑠𝑛𝑖𝑔 = 𝑘]

(7-17)(7-18)

where, 𝑀𝑛𝑖𝑘𝑔 is an indicator of choice result (1 or 0), which is decided by the function 1[. , 𝑀𝑛𝑖𝑘𝑔 isequal to 1 only when individual n chooses i and k simultaneously for contact setting g; otherwise, it is119Chapter Investigation of multi-faceted social contact decisions under the impacts of COVID-19:
oint analysis of contact mode choices and the number of contacted persons

0.
R programming language and the R package “maklik” (Henningsen et al., 2011) were used toevaluate the maximal value of the log-likelihood function. In this study, Newton-Raphson maximization
was applied for the unconstrained optimization problems of Frank copula and independent copula. The
Nelder-Mead maximization was applied for the optimization problems of FGM copula, Clayton copula,
Joe copula and Gumbel copula, because there are some constraints for their dependence parameters of𝜃.
To compare the goodness-of-fit for different copula models, the Bayesian Information Criterion(BIC) and Akaike Information Criterion (AIC) are used. The BIC can control the complexity of themodel and avoid overfitting (Aho et al., 2014). The lower the BIC value, the higher goodness-of-fit.

. Results and discussion
As shown in Table 7.4, six models were estimated, including the model assuming independence betweensocial contact mode choices and the number of contacted persons, where the “no contact” mode ischosen as the reference for MNL-based contact mode choice model.

7.5.1 Choosing the best copula
As evidenced by AIC and BIC, the Frank copula-based model generates the smallest values, suggesting
it is the best model. In other words, the Frank copula-based integrated social contact decision model
also outperforms the independence model. Other copula models unfortunately even perform worse than
the independence model. This indicates that the other non-Frank copula-based models do not work well
to represent the current social contact decisions. Looking at the range of dependence parameters, which
are compared in Table 7.4, the Frank copula accommodates the largest range of dependence parameters,
which can be understood to be the theoretical foundation that supports the Frank copula to be used for
capturing people’s joint decisions on social contact modes and the number of contacted persons.
Hereafter, the Frank-based model (estimation results are shown in Table 7.5) will be explained anddiscussed.

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Chapter Investigation of multi-faceted social contact decisions under the impacts of COVID-19:
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Table 7. 4. The model fit for six copula-based models
Range of dependence

Number ofparameterparameters–

FGM

Copula function

Log-likelihood

AIC

BIC

123

-60814.91

121875.8

122182.3[-1, 1

126-61376.02

123004.0

123318.0

Gumbel[1,+∞)

126-61550.8123353.6

123667.6

Joe[1, +∞)

126-61821.76

123895.5124209.5

Clayton(0, +∞)

126-62092.45

124436.9124750.9

Frank(−∞, +∞)

126-60667.03

121586.1

121900.1

Independence

The Frank-based modeling estimation results show that the dependency parameter is statisticallysignificant for the ONPC mode and the number of contacted persons (estimated value: 1.06, t-test
against 0: 3.80). The positive dependency parameter implies that the higher the probability of having
only non-physical contacts with others, the more the number of contacted persons during activity
participation. In other words, because an individual chooses to keep away from others with physicalcontact, contacting more people becomes less risky and consequently he/she contacts more people. The
insignificance of the dependency parameters for OPC and PCNPC may suggest that once an individual
decides to have physical contacts with others, he/she is less likely to care about the number of contacted
persons during activity participation. Allowing different dependency parameters with respect todifferent contact modes makes the representation of the above heterogeneous responses possible.

Table 7. 5. Estimation results of copula-based social contact behavior model
Ordered Logit
Multinomial Logit (MNL) Model(OL) Model
VariablesNo

Non-physicalPhysical contact

contact

contact (ONPC)(OPC)

Both physical andNumber of

non-physicalcontacted

contact (PCNPC)

persons

Est.

t-val(0)Est.

t-val(0)Est.

t-val(0)Est.

t-val(0)Threshold 1

-

-

-

-

-

-

-

1.12

7.91

Threshold 2

-

-

-

-

-

-

-

2.23

15.22

Constant term

-

-1.04

-4.31

-3.11

-9.40

-4.50

-11.44--

Gender

-

0.10

2.88

0.18

4.00

0.02

0.40

0.20

5.73

Age

-

-0.05

-4.91

-0.12

-9.15

-0.12

-8.79

0.01

0.08

Household size

-

-0.01

-0.47

0.02

1.18

-0.01

-0.30

0.09

5.44

Individual attributes

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Chapter Investigation of multi-faceted social contact decisions under the impacts of COVID-19:
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Marital status

-

0.12

3.02

0.29

6.05

0.12

2.55

-0.14

-2.68

Education qualification

-

0.05

3.69

0.09

4.78

0.11

5.71

0.03

2.08

Income

-

0.02

2.67

0.02

2.25

0.06

5.70

0.06

8.33

Activity frequency

-

0.02

1.35

0.12

5.20

0.26

9.80

0.26

14.00

Travel distance

-

0.18

12.34

0.28

14.65

0.38

18.95

0.18

12.93

Companion of acquaintances

-

0.76

8.50

1.20

11.95

0.93

9.99

0.71

12.51

Wearing a mask

-

-0.51

-11.70-0.91-17.28-0.59-11.42-0.57-12.76

Disinfecting hands

-

0.40

10.02

0.07

1.48

0.18

3.92

-0.21

-5.82

-

0.12

2.57

-0.01

-0.23

0.36

6.69

-0.10

-2.44

Crowded places

-

0.37

8.66

0.53

9.96

0.42

7.63

0.45

12.84

Close-contact settings

-

0.32

8.59

0.65

14.88

0.68

14.87

0.46

13.20

Self-efficiency

-

-0.11

-5.83

-0.12

-5.35

-0.02

-1.03

0.07

3.71

Other's efficiency

-

0.01

0.69

0.04

1.76

0.09

3.44

-0.05

-2.86

Self-safety

-

0.04

1.90

0.06

2.12

0.13

4.63

0.03

1.85

Household member' safety

-

0.10

4.79

0.17

6.14

0.09

3.45

-0.05

-2.88

Necessity of activity

-

0.08

5.60

0.22

11.38

0.23

12.32

0.05

4.06

Punishment to activity

-

-0.08

-6.03

-0.04

-2.34

-0.10

-5.91

-0.04

-2.16

-

0.12

5.79

0.17

6.67

0.19

7.20

-0.01

-0.60

-

-0.06

-3.02

0.03

1.20

-0.11

-4.73

-0.07

-3.61

-

0.06

2.84

0.18

7.20

0.10

4.09

0.10

6.69

Risk perception in public transport

-

-0.03

-1.59

-0.09

-3.79

-0.11

-4.54

-0.03

-2.10

Trigger of government

-

-0.05

-2.18

-0.19

-6.97

-0.10

-3.53

-0.07

-3.51

-

0.01

0.41

0.03

1.17

0.06

2.40

0.12

6.99

-

-0.04

-1.76

0.00

0.10

-0.04

-1.34

-0.07

-3.93

-

-0.01

-0.60

-0.12

-4.47

-0.10

-3.75

0.01

-0.23

-

1.06

3.80

-0.31

-1.54

-0.33

-1.35

-

-

Factors related to activity participation

Closed spaces with poor
ventilation

Risk-related psychological factors
Reliability of the information fromlocal government
Risk perception in residence
country
Risk perception in residence
regionTrigger ofworkplace/organization/affiliation
Trigger of the number of infectedcases
Acceptance of changing theduration/timing of activity
participation
Dependency

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Sensitivity parameters of social
Est.

t-val(0)t-val(1)Work/study

1.02

26.71

0.46

Shopping---

Eating out

0.69

24.44

-11.18

Physical exercise

0.90

22.02

-2.45

Parties

0.85

21.32

-3.71

Cultural leisure

0.82

23.03

-5.01

Medical activities

0.42

17.83

-24.79contact settings

7.5.2 Factors related to contact settings
Activity frequency
People are prone to have physical contact (fully or partially (i.e., mixed with non-physical contact))
when performing activities with high frequency, and the number of contacted persons increases with
the increase in frequency. This observation may be due to psychological fatigue (Shearston et al., 2021)
from following social distancing measures accumulated during the long COVID-19 pandemic. For
those highly frequent activities (e.g., shopping, work/study), people may become less concerned about
the exposure to the virus via social contacts and as a result, they are more likely to have OPC/PCNPC
contacts, potentially leading to more and more infections.
ravel distance
The travel distance between home and destination shows positive influences on both social contact
mode choices and the number of contacted persons, indicating that if people go to a distant place, they
are more likely to have contacts with others in all possible contact forms (modes of OPC, ONPC, and
PCNPC) and contact more people. This is probably because during the pandemic, many activity venues
nearby people’s homes were closed and as a result, these people had to visit activity venues that are far
from their homes, making such venues gathering points that result in social contacts with more people.
Companion of acquaintances
If an individual performs an activity together with his/her acquaintances, he/she is more likely to have
all kinds of social contacts with others and contact more people. In line with common sense, if people
are going to perform daily activities excluding work/study with acquaintances, that daily activity itself
has a social nature of communication with people, so it is easier to have more social contacts around
them.

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rotective measures
Wearing a mask does not prompt people to have contact with strangers except their household members
or acquaintances. If people wear a mask, they are prone to contact fewer strangers, indicating that they
already reduced their social contact consciously when realizing the necessity of wearing masks to keep
themselves safe during the pandemic.
Oppositely, if people already disinfected their hands before/during/after performing an activity(e.g., shopping, eating out, parties), they do not mind contacting others in a physical or non-physicalway. However, although people do not mind participating in activities with the physical presence ofothers after disinfecting their hands, they are still prone to contact fewer persons during the pandemic.
This finding is in line with Chen et al. (2021) who showed that disinfection measure does not lead to ahigher probability to use public transport.
rotective capability and perceived safety
Here, the following protective capability and perceived safety are analyzed: i.e., self-efficiency (anindividual’s belief in his/her capability to protect him/herself from the infection), others' efficiency (anindividual’s belief in other persons’ capability to protect themselves from the infection), self-safety(whether an individual thinks he/she is safe from the infection when performing an activity), household
members' safety (whether an individual thinks his/her household members are safe from the infection
when performing an activity).
It is estimated that if people think they are highly capable of protecting themselves from theinfection of the COVID-19 virus, they tend to contact more people. People would reduce their social
contact with others in the cases of only non-physical contacts and only physical contacts: i.e., people
with higher self-efficiency do not prefer to contact with others under these two contact modes. If people
cannot avoid social contact with both physical and non-physical contacts (e.g., in the case of work/study
and parties), they do not mind contacting more people, under the condition of high self-efficiency (e.g.,
being able to take sufficient protective measures). On the contrary, if people believe in others’ capabilityof taking protective measures, they do not mind making social contact even when neither physical nor
non-physical contact could not be avoided, but they would consciously reduce the number of contacts.
For the risk perception about whether people think themselves and their household members aresafe from the infection, expectedly, when people think they or their household members are safer duringperforming daily activities, they are more likely to have social contact with others in different modes.
However, if people think that their household members are safer, the number of contacted persons would
decrease. This indicates that individuals would not try to contact more people during performing daily
activities for the safety of household members.
erception of 3Cs in activity locations
As for the 3Cs (i.e., closed spaces with poor ventilation, crowded places, close contacts), if activity
venues have closed spaces with poor ventilation, people are more likely to contact fewer persons

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oint analysis of contact mode choices and the number of contacted persons(parameter: -0.10, t value: -2.44) for reducing the probability of being infected from others.
Unexpectedly, people would not avoid contacting others and would not reduce the number of social
contacts even when indoor activity venues are crowded places with close contact. This phenomenon
reveals that although people can choose to go to locations without a crowd or close contact, it is difficultfor individuals to avoid social contact when they have to do daily activities in crowded places or places
with close contact (e.g., work/study, shopping, medical activities). Thus, there is still a lot of
possibilities to improve crowded places with close contact.
ecessity and punishment
The perception of activity necessity (whether doing an activity is necessary or not) and punishment(whether doing a certain activity should be punished or not) have significant effects on people’s social
contact behaviors. People are more likely to have social contact with others in all contact modes andcontact more people when they think that performing daily activities (e.g., work/study, shopping,
medical activities) is necessary even with a high risk of infection. Conversely, if people think that
performing daily activities during the pandemic should be punished (e.g., gathering in a concert), thenumber of persons they contact decreases, and they are prone to make no contact with others. It seems
that most people have a clear understanding of the balance between necessity and punishment (orresponsibility) when performing activities, which indirectly indicates the effectiveness of governments’policy measures and public opinion guidance related to COVID-19.

7.5.3 Risk-related psychological factors
Reliability of information

The more reliable the information announced by the local government, the higher the probability of
having contact with others, while such a phenomenon is not confirmed with respect to the number ofcontacted persons. This may be due to the fact that people in these target countries have been willing tolive with the virus. Hence, people are prone to have social contact with others if they trust the infection
information (e.g., the Omicron variant only presents a low fatality rate) announced by the local
government. However, although people trust such information, they tend to not take the risk of infection
by contacting more people.
Risk perception at different spatial scales

The impacts of risk perception on people’s social contact behaviors show spatial differences. To be
specific, when people perceive a higher risk of infection in their residence country, they are prone to
reduce the number of contacted persons and the social contacts through the modes of ONPC and PCNPC.
If people’s risk perception about public transport increases, they would reduce social contacts throughthe modes of OPC and PCNPC and the number of contacted persons for daily activities as well.
Conversely, social contact behaviors are positively associated with the risk perception observed in

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people’s residence regions (province/prefecture/state), and people would have more contact with others
although they perceive that the risk is increasing in their residence regions. This is probably because ahigh-risk perception at the national level makes people realize how serious the risks are, and the highrisk perception at specific locations (e.g., in public transport vehicles) reveals that the risk of infection
has been increasing nearby their daily action spaces. Thus, people feel they need to reduce social contact
instantly for safety, while the risks in residential regions are not treated as an obvious threat to people’s
daily lives.
riggers of behavior changes

If people think they are more likely to change their behaviors because of formal governmental rules,
they tend to make no contact with others and decrease the number of contacted persons. Meanwhile,
people are prone to reduce the number of contacted persons if they attach more importance to thenumber of infection cases. However, for those people who are easier to be affected by the formal rules
from their workplaces/organizations/affiliations, they tend to have social contact, through the mode ofboth non-physical and physical contact, and contact more people. This may be because the share of thecontact mode “PCNPC” is highest for work/study, and the rules of workplaces/organizations/affiliations
only have a strong binding force on people’s contact behaviors for work/study. The rules of
workplace/organization/affiliation may tend to request people to take protective measures rather than
reducing work/study contacts.
Acceptance of changing the duration/timing of activity participation
The modeling results show that if people accept to change the duration/timing (or activity schedule) for
performing activities, even leading to various inconveniencies, they will reduce the probability ofcontact through the modes of OPC and PCNPC simultaneously. That makes sense because if people canreschedule their activities, they could avoid the peak hours of daily activities and avoid social contact
if there are fewer other people around.

7.5.4 Individual attributes
Males are more likely to have social contact with others through the forms of ONPC and OPC, and they
are further prone to contact more people, than females. For the contact mode “PCNPC”, gender does
not matter. This observation may be because males usually have more social opportunities (e.g.,working contacts or eating out after work) than females. But for activity settings with more participants(e.g., parties, and cultural leisure), both males and females are easier to have ONPC and OPC
simultaneously, leading to a slight gender difference for the contact mode “PCNPC”. Older people areless likely to contact with others through all contact modes, in comparison with no contact. However,age is not influential to the number of contacted persons. This may be because older people wellrecognize the higher infection risk (infection rate and severity of illness) of their age group. As a result,on one hand, they consciously try to avoid any contact with others for daily activities as far as possible.
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On the other hand, if social contact with others cannot be avoided, they would have no way or no ability
to reduce the number of persons they contact. People with higher education qualifications and income
are more likely to contact others and contact more other people. This may be because such people havemore opportunities or better access to perform various daily activities and consequently make moresocial contacts with others.
Married people are more likely to have social contact with others, probably because they have toperform more maintenance/mandatory (e.g., shopping, work) activities to support their family lives. On
the other hand, married people try to contact fewer people. This may be because they are more worried
about household members’ infections through out-of-home social contact. Household size does notmatter to people’s contact mode choices, but it surely does to the number of contacted persons. This
indicates that if people live with more household members, they are prone to contact more people during
daily activities. This observation is intuitively understandable because larger households have more
opportunities to perform maintenance/leisure activities (e.g., shopping, gathering) together with theirmembers, resulting in contacting more people during activity participation.

7.5.5 Heterogeneous influences across activity settings
Larger (smaller) sensitivity parameter means that the people’s choice probabilities of the social contact
behaviors for corresponding contact setting are more (less) sensitive to external variables and easier(harder) to change under the impact of these variables when they are compared with the choiceprobabilities of contact behaviors for shopping setting. All the above observations are further
heterogeneous across activity settings because all the activity-specific sensitivity parameters (except
the work/study setting) are statistically significant against both 0 and 1. The t-test against 0 is for testingwhether the obtained sensitivity is meaningful or not, and the t-test against 1 is for testing whether theobtained sensitivity is the same as that of the reference activity (i.e., shopping) or not. All the sensitivity
parameters are positive, while the sensitivity for work/study is statistically indifferent from that for
shopping. The sensitivity parameters for other activity settings are all smaller than 1, indicating that
individuals’ social contact behaviors for these activities are less sensitive (in terms of influencing
magnitudes) to those significant factors identified from the modeling analysis, relative to shopping. Thisphenomenon is especially obvious for eating out (0.69) and medical activities (0.42).

. Conclusion
In this study, a unique social contact survey concerning seven main daily contact settings (work/study,
shopping, eating out, physical exercise, parties, cultural leisure, and medical activities) was conductedin six developed countries from March to May 2021 and more than 7000 respondents provided valid
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answers. A copula approach was used to capture the dependence between social contact mode choices
and the number of contacted persons which were represented by a multinomial logit model and anordered logit model, respectively. Various factors affecting the social contact behaviors were
investigated.
Findings
First, five types of copula functions, including FGM, Gumbel, Joe, Clayton, and Frank, were estimatedand compared with a model without dependence on the two social contact behaviors. Results
empirically confirmed that the Frank copula outperforms other copulas, where the theoretical featuresof the Frank copula also support this empirical observation. As for the dependence related to social
contacts, indirect contacts were estimated to be dependent on the number of contacted persons.
Considering that existing studies have neglected the role of indirect contact in COVID-19 transmission,
the above finding is especially important to implementing more effective pandemic policies.
Second, frequency of activity participation is positively associated with more physical contacts
and having contacts with more people during performing an activity. Various social contacts wereobserved at places far from home, while the presence of acquaintances will increase the number ofsocial contacts in all modes and the number of contacted persons.
Third, people tried to perform activities in spaces with poor ventilation, but unexpectedly, crowded
places with close contact would not encourage people to perform safer social contact. Wearing a mask
and disinfecting hands discouraged people to contact more people. Disinfecting hands does notnecessarily encourage people to avoid unsafe contact, but wearing a mask does.
Fourth, various influences of psychological factors on social contact are found. People feelstruggling between performing necessary activities and worrying to be punished. Trust in thegovernmental risk information leads to more contact in all modes but does not affect the number ofcontacted persons. People’s risk perceptions at different spatial scales (the highest risk was perceived
within public transport vehicles) present inconsistent influences on the choices of social contact modes
and the number of contacted persons: this observation seems to be in line with the fact of many
infections even under strict restrictions, which obviously brings difficulties and complexities into
pandemic policymaking.
Fifth, government rules are more effective in to encourage safer social contacts than the rules fromworkplaces/organizations/affiliations. Higher acceptance of changing the duration/timing for activity
participation is positively associated with safer social contacts.
Sixth, all the above observations show their sensitivity to the specific social contact settings, interms of the magnitudes of the effects, in the sense that the parameters supporting the above
observations vary significantly across different settings.
The revealed influences of various factors on social contact behaviors in different contact settingssuggest that pandemic control policies should be carefully designed and timely adjusted in response tovarious changes. In this regard, technology-supported scientific monitoring of people's behaviors and128

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the risk-protection conditions of activity-travel environments and the resulting context-sensitive
warnings should be implemented in a seamless manner.
Academic contributions
Both survey and modeling approaches have unique and attractive features. The social contact survey
implemented in this study differs from existing social contact surveys in several ways: (1) leisureactivities are detailed; (2) psychological factors are investigated with respect to different social contact
settings; (3) altruism is reflected by asking respondents’ perceptions of their household members’
capacity of managing social contacts under risky environments. The copula modeling approach is
flexible and logical to represent the dependence between social contact mode choices and the numberof contacted persons and especially, it has the potentials to represent more complicated social contact
decisions even with larger dimensions.
imitations and future challenges
The social contact survey of this study only targets average situations during the COVID-19 pandemic,
which ignores changes and variations in social contact decisions themselves and their influential factors.
It is therefore worth investigating a panel survey in the future, which is especially useful to capture theeffects of social-distancing fatigue, even though the panel survey is costly and time-consuming. The
way of investigating social contacts could also be improved, for example, by conducting an activitytravel diary survey, even though this will increase respondents’ answering burdens. For deriving generalfindings, this study pooled all data collected from six developed countries. In the future, it may be worth
exploring how the findings from this study may vary across countries.

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Chapter Conclusions

CHAPTER

Conclusions.1 Introduction
This dissertation gives a seamless exploration of individuals’ behaviors mechanisms of the activitytravel behaviors with social contacts and provides important policy implications for pandemic
policymaking. In the beginning of this study, the necessity of pandemic policymaking based on analysis
of behavioral mechanism is stated and the key research questions of pandemic behaviors is introduced(Chapter 1). Then, an overview of the research status about individual’s pandemic behaviors is reviewed
for the behavioral and psychological adaptations to COVID-19, travel mode choice and social contact(Chapter 2). The methodology applied in this study is introduced from the perspectives of survey data
and modeling approaches (Chapter 3). Individuals’ behaviors mechanisms are explored in the multiple
behaviors case of dynamic activity-travel behaviors with social contacts (Chapter 4) and the single
behavior cases of travel mode choice behaviors (Chapter 5) and social contact behaviors (Chapter 6 and
7) respectively. To answer the research questions in this study, this Chapter 8 would first give a summaryof the research conclusion. Then it is followed by a discussion of policy implications. Finally, thelimitations and future research challenges of this study are described.

.2 Conclusion
The COVID-19 pandemic has made a tremendous impact on people’ behaviors for whether multiple
activity-travel behaviors with social contact or a certain single behavior in it during the process oftravelling and doing activity. Exploring the individuals’ behaviors mechanisms of the activity-travel
behaviors with social contacts and obtaining a sufficient knowledge about it are the basic of conducting
a sensible pandemic policymaking. A reasonable modelling approach and survey design contributes to
exploring individuals’ behavioral mechanisms. And individuals’ demographic attributes, psychologicalfactors and activity participation attributes play an important role in affecting people’ behavior changes.
To realize the research purposes for exploring pandemic-related behavioral mechanism, thisdissertation first attempts to develop a dynamic structural equation model for clarifying the dynamic
associations between psychological factors of policymaking and temporal behavior changes with thedata of Japan. After that, this study makes two single behavior analyses for travel mode choice behaviors

130Chapter Conclusions

and social contact behaviors respectively with the case study in six developed countries (Australia,
United States, New Zealand, Canada, United Kingdom, Japan). The impact of psychological/culturalrisk factors and pre-pandemic preparedness on individuals’ travel mode choices across travel purposes
during the COVID-19 pandemic is revealed by estimating a mixed hybrid choice model with correlated
latent variables. The changes of the number of social contacts between influenza period in 2019 andpandemic period since 2020 and its heterogeneity across variables and contact settings are revealed by
the calculation of total number and mean number. Meanwhile, the joint social contact behavioral
mechanism of the contact modes and the number of contacted persons are revealed by a copula-based
approach. The analysis results of the dynamic activity-travel and social contact behavior changes, travel
mode choice and social contact behavior in this study have shown an evident consistency with eachother.
Some findings are summarized to show individuals’ behaviors mechanisms of the activity-travel
behaviors with social contacts under the background of the COVID-19 pandemic as follows.
First (answer Q-1), it is significantly confirmed that people’s accumulated behavior changes in thepast can affect all psychological factors of the reliability of information sources, the risk perceptions,the attitudes toward COVID-19 policymaking capability, the attitudes toward PASS-LASTING based
policies. And these psychological factors will further influence the most recent behavior changes. That
influence is especially obvious for the relationship between risk perception and recent/accumulated
behavior changes. People have an obvious different share of the high-risk perceptions in different spatial
scale. Meanwhile, the recent behavior changes are most affected by accumulated behavior changes in
the past in an indirect way. People’s behavior changes are mostly characterized by avoidance behaviors
for the direct contacts or talking with others (avoid social contacts). The attitudes toward policymaking
capacity (e.g., government) may be more important than the attitudes toward specific policy on
influencing people’s behavior changes. The positive attitudes (e.g., specialized, enthusiastic, competent,
can be trusted) toward policymaking capacity can prompt people to follow PASS-LASTING based
policy measures. People’s perception of information reliability is most decided by the information from
authorities (i.e., central/local governments, experts, medical institutes)
Second (answer Q-2, Q-3, Q-4, Q-8), people’s travel mode choice behavior shows a diversity
across the travel purposes. Although the use of public transport decreased significantly during the
COVID-19 pandemic, people still rely on public transport for shopping and medical activities. People
are less likely to change travel mode choices for commuting than other travel purposes. The impact oflatent psychological/cultural factors on travel mode choice show an obvious diversity across different
travel purposes. The trust is positively influential to risk perception, and the significant associations
between latent factors indicate a direct and indirect effect of them on the travel mode choice behaviors.
Risk perception is found to be more remarkable at larger spatial scales than inside crowded public
transport vehicles. The effect of the cultural orientations on travel mode choices can be divide into twodimensions: hierarchism-egalitarianism and individualism-fatalism because of the higher similarities ineach pair. Hierarchists and egalitarians are mainly reflected in travel mode choices for shopping and131

Chapter Conclusions

physical exercise, while individualists and fatalists are dominating in travel mode choices for othertravel purposes. Individualists and fatalists show a completely opposite preference to Hierarchists andegalitarians for all trip purposes except work/study. The preparedness focusing on risk-concerned travel
habits formed before the COVID-19 pandemic shows a significant impact on travel mode choices, but
the influences are not consistent across travel purposes.
Third (answer Q-5, Q-6, Q-8), there is an obvious heterogeneity of the number of social contacts
and its changes across the targeted attributes and contact settings, the pandemic policymaking could
focus on the protection of specific group with higher danger level of social contacts. For instance, people
especially contact more people in work/study setting and public transport setting. Older people are moredangerous to the virus, but it is difficult for the elderly (over 70 years) to decrease the social contacts
during the medical activities. If a country (e.g., US, AU) has eased the lockdown interventions for along time, people in that country would contact more people for leisure activities averagely than othercountries. However, the number of social contacts in that country were high as well for medical
activities which could be led by the infection of the coronavirus. There is an obvious inequity of theaccess to safety from infection by decreasing social contacts for the people with low educated level andlow income. People’s number of social contacts is positively associated with the frequency and duration
for performing the activities, but the social contacts occurred with high-frequency (e.g., shopping) andlong-duration had decreased significantly after the pandemic. People’s contact modes and the taken
protecting measures are associated with the number of social contacts during the pandemic and show
different features across the contact settings. On one hand, most PCNPC contacts occurred inwork/study setting and party setting, while the most ONPC contact occurred in shopping setting andpublic transport setting. On the other hand, protecting measures are well implemented for work/study
setting and shopping settings during the pandemic.
Fourth (answer Q-6, Q-7, Q-8), the joint estimation of people’s different indexes of social contact
behaviors is possible via the copula approach. Thus, a significant joint correlation between the ONPC
mode and the number of social contacts is found indicating that people trend to simultaneously increase
the number of contacted persons and the probability of having ONPC contacts with others under the
influence of common unobserved factors during the COVID-19 pandemic, but that significant
dependency is not found for OPC/ PCNPC contacts. Not only individuals’ demographic attributes but
also the psychological factors and activity participation attributes have a diversified influence on
people’s joint choice of social contact behaviors. Some factors may have a significant influence on theprobability of a certain contact mode, while they may not be significant for other modes. Moreover,having contacts with others doesn’t means people would contact more people, for instance, theperception of higher self-efficiency and lower other's efficiency would prompt people not to contact
with others, but when they can’t avoid the social contacts, they don’t mind contacting more people.
People’s safe feelings (safe from infection) of their household members can decrease the probability of
having no contacts with others, but it will decrease the number of contacted persons at the same time.
Some attributes show the same impact on social contact choices that consistent with the findings in132

Chapter Conclusions

descriptive analysis of the number of social contacts in chapter 6, which proves the consistency of model
analysis and description analysis of social contacts. The behavior changes of social contact during the
COVID-19 pandemic are found in the forms of dramatic decrease of the number of social contacts andavoid contacts with others. The additional subjective psychological variables in copula analysis
highlight that people’ risk-related psychological factors (e.g., reliability of information source, risk
perception in different spatial scale) surely have the significant impact on a certain behavior change too.
And it is reasonable to include risk perception with different spatial scales in the behavior analysis
because of the obvious difference among them. The joint analysis of social contact also indicates thatthe difference of behavioral mechanisms for different contact settings can be reflected not only in the
difference of positive-negative effects but also in different sensitivity to influencing factors.

.3 Policy implication
Based on the result obtained in the case study of target countries, this dissertation has found five key
prerequisites for the pandemic policymaking:1) The pandemic policymaking for multiple activity-travel behaviors in daily life and thepolicymaking for a certain single behavior aren’t separated process from each other. On one hand, thepolicymaking of multiple behaviors can comprehensively regulate people's daily behaviors and provide
the guidelines for the policymaking of single behavior. Some influences of variables in the process ofmultiple behaviors policymaking are also applicable to that of single behavior policymaking, which canbe used as a reference. On the other hand, the single behavior policymaking adds more operational
details of conducting measures for multiple behaviors policymaking by including the influences of more
external variables. And if the details in single behavior policymaking are contrary to the multiple
behaviors policymaking, the feedback and error correction from single behavior policymaking can bemade to the multiple behaviors policymaking reversely. The policymaking for multiple behaviors andsingle behavior is complementary to each other.
2) The dynamic associations between policymaking and behavior changes over time are especially
important for behavior policymaking. Previously conducted policies (e.g., strict lockdown in initial
phase of pandemic) may not be effective and practical anymore as the COVID-19 pandemic continues.
And avoiding people’s policy fatigue and prompting preparedness for the pandemic should beconsidered for pandemic policymaking as well.
3) Individuals’ psychological factors are crucial for the pandemic policymaking. Especially therisk perception in different spatial scales can provide an overall view on the behavioral mechanisms
when people facing the risk of a pandemic like the COVID-19, hence, it shouldn’t be ignored in thepolicymaking process of activity-travel behavior and social contact.
4) There exists a significant logical relation between different psychological factors, policymaker133

Chapter Conclusions

should pay attention to that, because it is very likely that changing one policy related to one aspect ofindividuals’ psychological factors will have a knock-on effect on the implementation of other policies.
5) The pandemic policymaking should be tailored to specific situations of activity-travel behavior
with social contact because people’s behaviors have obvious differences not only across the groups of
attributes but also across the contact/travel settings.
From the perspective of the five mentioned prerequisites, the specific recommended policies ofactivity-travel behavior with social contact are summarized as follows.
olicy implications of behavioral and psychological adaptations to COVID-19

1) All categories of the PASS-LASTING based policy measures should be taken to effectively
control the COVID-19 pandemic. The PASS-LASTING based policy measures appear to be widely
accepted and followed by individuals during the COVID-19 pandemic, proving that PASS-LASTING
based policy measures (Zhang, 2020; Zhang, 2021) are rational and effective. In particular, the “Avoid”
measures show a significant impact on people’s behavior changes. Some measures such as avoiding
inconsistent information, avoiding crowded platforms and vehicles of public transport systems, andavoiding activities/trips involving close physical distance should be strongly recommended. Suchmeasures are not only feasible but also less costly. The importance of such avoidance measures is also
consistent with the observations by Shimizu and Negita (2020), who studied Japanese people’s
responses to the first wave of the COVID-19 pandemic.
2) Governments should ensure that the information they provide is reliable. The more authoritative
the government is, the more likely it is that people will cooperate in making behavior changes which
will prevent the spread of the virus. Communications between governments and the general publicshould be better designed by reflecting the up-to-date insights from social psychological research.
Governments also need to build a channel to receive feedback from the public about perceptions of thereliability of their information and address any concerns in a timely way. The above policy
recommendations are in line with the argument by Hyland-Wood et al. (2021), who stated thatgovernment policymakers should promote widespread public participation by involving diverse
communities and using digital technologies in communication and engagement activities.
3) Government (especially local governments) should pay more attention to the public’s attitudes
to them and take some feedback to improve people’s feelings about governments’ expertise, enthusiasm,
competence and trust. In this regard, Kim et al. (2020) showed a similar observation that effective risk
communications can improve the public’s attitudes to governments. Both central and local governments
should ensure the consistency of policymaking. After the central government takes policy measures,
local governments should take subsequent measures, accordingly. Ren (2020) compared COVID-19
policymaking in China, Italy, and the US to examine how crisis responses in each country are shapedby its central-local government relations and concluded that central governments should play the key

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Chapter Conclusions

role in controlling the current pandemic. To guarantee such policymaking consistency, the COVID-19
policymaking headquarters in the central government should involve key members from local
governments. As behavioral interventions have to be made with respect to various behaviors across life
domains, a cross-sectoral and interdisciplinary policymaking framework should be established as themost fundamental framework to support effective policymaking. It is already very obvious that publichealth authorities cannot stop the pandemic by themselves. The importance of such a cross-sectoral
policymaking system is strongly supported by Mei’s (2020) research.
4) COVID-19 policymakers should pay more attention to people’s time-varying behaviors byencouraging behavior changes that are helpful in controlling the pandemic, while at the same time
restricting behavior changes which worsen the pandemic. Acuña-Zegarra et al. (2020) also revealed that
individuals’ intentions to follow preventive measures change over time. Co-changes in various
behaviors and fatigue of behavior changes caused by continuous interventions should be addressed. It
seems important to figure out several key behavior changes that may trigger other changes automaticallywithout further interventions. In other words, intervention policymaking should have clear targets inorder to avoid unnecessary pressures on people and to enhance policymaking efficiency. This is because
when a policy is implemented for a long time, people will inevitably show policy fatigue, which may
weaken the effects of the policy (Rahmandad et al., 2021; Petherick et al., 2021).
olicy implications of travel mode choice during the COVID-19 pandemic

1) The traffic restriction of public transport can control the infection, but it is worth discussing
because people rely on the use of public transport for essential activities, such as shopping and medical
activities travels. Guaranteeing a safer environment for the use of public transport during the COVID19 pandemic and rebuilding confidence in using public transport after the pandemic would be crucial
questions, even when people are inside public transport vehicles for a long time. Pandemic-resilienttransport policies (e.g., social distancing) should be sensitive to locations. These previous measures
could ensure the objective and subjective safety of people.
2) Trip makers’ cultural risk factors should be properly reflected in making pandemic-resilienttransport policies that can well distinguish between the four types of cultural orientations: i.e.,hierarchism, egalitarianism, individualism, and fatalism, depending on different travel purposes. For
instance, individualists and fatalists prefer to choose risky public transport for unessential activities,
they are not interested in following social rules, thus some virtual personal benefits (e.g., free parking)for choosing a car or active transport would be more effective to attract individualists and fatalists. If
policymakers want the use of public transport to restore to the level of pre-pandemic, building a social
consensus that the use of public transport is recommended and safe for high-frequency daily travels(e.g., for shopping travel) would be more useful.
3) Governments and health/medical agencies should make efforts to encourage people to form awell-prepared distance-sensitive lifestyle that is useful to survive from pandemics. For instance, it is135Chapter Conclusionsdifficult and uncomfortable for people to change their habit of travel mode for commuting suddenly,
hence, consciously changing the travel habit of mode choice for commuting (e.g., from high-risk mode
to low-risk mode) in the early pandemic would be helpful for people to prepare for or adapt to the life
in a post-pandemic era. More types of preparedness should be made with respect to various stakeholders,as suggested by the PASS approach, from which various “prepare-protect-provide”, “avoid-adjust”,“shift-share”, and “substitute-stop” policy measures have been summarized/proposed (Zhang, 2020).
4) As the during-pandemic tentative measures, reasonably sufficient parking places should be
prepared at areas of shopping and physical exercise as well as medical activities, for the use by car, bus,
shared mobility and taxi. At areas with medical facilities, bus stops should be further installed, which
could contribute to the use of bus. Higher dependence on car caused by COVID-19 should be restrictedby both effective transport and non-transport policy measures after the pandemic, where non-transport
policies should be given a higher priority considering that travel demand is derived from activity
participation.
5) Even though up to the survey period in this study, the governments of the targeted six countries
had taken various protective measures (e.g., requirements of wearing masks, disinfection, and keepingsocial distance) for all types of travel modes, our analysis results revealed trip makers’ diverse,
heterogeneous and complicated responses to the pandemic, where some responses are even seemingly
contradictory from each other. Both logical and illogical perceptions about the infection risk, which issignificantly associated with trust in governments and medical agencies, are detected. The above
observations re-confirm the importance of differentiated risk communication with respect to different
activities, where different stakeholders should play different roles in the communication, as evidenced
by Zhang (2021).
olicy implications of non-household social contact during the COVID-19 pandemic

1) The pandemic policymaking on social contacts should focus on the target population with highrisk of infection through the daily social contacts. For instance, the people aging over 60 years havehigher risk of the COVID-19 virus, however, they have contacted more people during the work. It issensible to increase the percentage of telework and vaccination for old working people during the
pandemic. The older people are harder to avoid social contact with others. And the elderly over 70 years
is most dangerous under the exposure of the COVID-19 virus, however, they have to perform medical
activities usually during the pandemic due to their health status. It is difficult for them to decrease thesocial contacts. But the remote medical services can resolve these problems easily. The government can
provide some financial supports for the private or communal remote medical services. There are moresocial contacts for the staffs in government and educational institution, such as the contacts with thepeople coming to government for formalities or students in school. To ensure the normal operation of
the government and the safety in reopened school during the pandemic, the staffs in these organization
should take more protecting measures and regular nucleic acid amplification test could be conducted in

136

Chapter Conclusions

government and school if someone want to have a test. If people have a larger household size, they areless likely to decrease the work contacts, this phenomenon is also applicable to other contact settings
as well. That is caused by many complicated reasons, such as the responsibility of earning money andsupport the family. But the government could provide more subsidy to these people who have higher
economic pressures with a larger household size. The low educated people and people with low income
have less ability to decrease their daily social contacts especially for public transport, work, andshopping during the pandemic. They are suffering from the higher risk of getting infection. The
government could provide some free medical supplies for pandemic prevention (such as mask or
disinfectant) to the people with low education background or low income.
2) The pandemic policymaking should especially focus on the protection in the locations ofpeople’s high-frequency activity (e.g., workplace, school, shopping venue), because it is easy for people
to form a policy fatigue when performing high-frequency activities and relax their vigilance of theinfection via social contacts. Closing the venues of daily activity (such as restaurants) near people’s
residences can reduce people's social contact, but it also encourages people to travel farther and meet
more people, which in turn increases the risk of infection. Taking protecting measures could prompt
people to decrease the social contact behaviors consciously. But the measures of disinfecting hands inpublic transport is very limited. It is necessary to provide more hand disinfection facilities in public
transport and disinfect the public transport vehicle regularly. The social contacts without any protecting
measures decreased significantly during the pandemic, however, the contacts without any protecting
measures still increased slightly for shopping, cultural leisure, and medical activities settings, whichmeans there are some people who never taking protecting measures and contacted more people during
the pandemic. The policymaker should pay attention to these parts of people in the next wave of thepandemic. People are sensitive to the 3Cs features in activity locations, hence, the policy avoiding 3Cs
should continue.
3) Pandemic policymaking should be made from the perspective of ensuring personal safety andfrom the perspective of ensuring the safety of people’ household members. Because people are more
willing to reduce social contact for the safety of their families even than the safety of themselves.
Clarifying the necessity and possible punishments for performing activities during the pandemic canfurther improve the effectiveness of policies. In addition, the importance of the differentiated risk
communication pathways (e.g., reliability of information source, trigger of formal rules introduced by
the government) with respect to different activities for policymaking is confirmed again.
4) In fact, most people have a clear understanding of the high risk of infection for the close contacts,that is why previous studies and policymaking were focusing on close contacts. However, the thesignificant dependency between the number of social contact and the choice probability of having only
indirect non-physical contacts has revealed that the policymaking for indirect non-physical contacts is
also indispensable. When the policy affects the number of contacts, it will also affect the probability of
having non-physical contacts. That increases the complexity of the policymaking for non-physicalcontacts. But this does not mean that ignoring policymaking for non-physical contacts is reasonable.

137Chapter Conclusions

On the contrary, considering non-physical contacts is crucial for a comprehensive pandemic
policymaking.
5) Non-mandatory policies have different effectiveness under different contact settings. A certain
policy that is effective for work/study and shopping setting may not be so effective for other contact
settings. For instance, people especially show less intention to change their contact mode and thenumber of contacted persons for medical activities setting. So, the pandemic policymaking needs to beflexible and depend on the specific circumstances.

. Future research challenges
This dissertation has focus on the pandemic policymaking based on the exploration of people’s
behavioral mechanism of activity-travel behaviors with social contacts. However, there are someresearch challenges need to be discussed in future research.
First, for the research data, this study only applied the RP survey data for the behavior
policymaking analysis, the Big Data and Open Data of people’s mobility behaviors and social contact
behavior could well expand the scope of macroscopic behavior analysis. And because the data iscollected in a few target countries, the findings may not be generalizable, it is necessary to conduct
further studies in other countries in order to make international comparisons. It is difficult to obtain the
dynamic psychological data precisely in our conducted surveys, only individuals’ psychological statusduring the conducted periods of surveys are collected. A longitudinal panel survey with dynamic
psychological data at different points in time would be helpful for further dynamic analysis forbehaviors.
Second, for the research framework, a comprehensive framework is built for the analysis ofactivity-travel and social contact. However, there are some elements illustrated by dotted arrows need
to be resolved in future studies. Pre-pandemic preparedness is an important factor indicating the
dynamic effect of people’s behaviors before and during the pandemic, but the detailed behavior analysis
of pre-pandemic preparedness hasn’t been conducted with the correlations between different factor
clusters in this study. And it is only be adopted to the analysis of travel mode choice behavior. For the
behavioral analysis of activity-travel and social contact in this dissertation, only the analysis of social
contact has considered about the joint relationship between different behaviors (contact mode andnumber of contacted persons), that kind of correlations across other behaviors could be further studied.
In the multi-behavior dynamic analysis of activity-travel and social contact, I only analyzed theinfluence of accumulated experience during the pandemic on current behavior and ignored the dynamic
effect between the behaviors at different time points within accumulated behavior.
Third, for the research contents, this dissertation explores the behavioral mechanism and conducts
the pandemic policymaking with the help of psychological factors reflecting personal feelings about138

Chapter Conclusions

policymaking processes. However, some unique policy measures made for all individuals in different
countries haven’t been examined directly. Only the travel mode choice behaviors and social contact
behaviors outside home are further investigated in the scale of single behavior. Further research may
focus on the single behavior research in other aspects of people’s activity-travel behaviors during the
pandemic. And the impact of individuals’ influencing factors (e.g., psychological factors) on their
behaviors is explored, however, the daily behaviors could also significantly affect people’s
psychological factors in reverse, that kinds of explorations haven’t been investigated in the single
behavior analysis of this study. Including the bidirectional relationship between behaviors andinfluencing factors in research is an important challenge for a comprehensive policymaking. The
dynamic effect of behaviors is only investigated in the analysis in chapter 4 and 5, further dynamic
analysis for people’s social contact behaviors is needed.
Fourth, for the modeling analysis, the social contact behaviors in public transport vehicles setting
are only explained by descriptive analysis due to the limitation of data. The modeling analysis for social
contact behaviors in public transport vehicles is need for solid evidence of pandemic policymaking. The
contact time for different contact setting is another important index of people’s social contact behaviors,
while it is only explored by the descriptive analysis in this study. Further study may investigate thesocial contact behaviors in the three dimensions of contact mode, number of contacts and contact time
simultaneously via the Copula approach or other joint modeling approaches. The applied copula-based
approach couldn’t resolve the sample selection bias caused by individuals' random responses during theprocess of survey, a mixed copula-based model with randomness could be developed for further
research.

139

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PUBLICATIONS
ournal articles and book chapters:
1) Chapter . Ding, H.,

Zhang, J. (2021). Dynamic associations between temporal behavior

changes caused by the COVID-19 pandemic and subjective assessments of policymaking: A
case study in Japan. Transport Policy, 110, 58-70. (Published) (IF=6.173)
2) Chapter . Ding, H., Zhang, J.

Zhang, L. (2022). Psychological/cultural risk factors andinfluenza-induced preparedness associated the travel mode choices under the impacts of
COVID-19: Analysis based on a mixed hybrid choice model. Transport Policy. (passed the firstround of review: major revision - resubmitted) (IF=6.173)
3) Chapter . Ding, H., Zhang, J. (2023). Book chapter for Research Handbook on Transport and
COVID-19. Edward Elgar Publishing. (Accepted)
4) Chapter . Ding, H., Zhang, J. (2023). Dependence analysis of social contact behaviors under
the impacts of COVID-19 based on a copula approach. (Submitted)
5) Zhang, J., Zhang, R., Ding, H., Li, S., Liu, R., Ma, S., ...

Hayashi, Y. (2021). Effects oftransport-related COVID-19 policy measures: a case study of six developed countries.
Transport Policy, 110, 37-57. (Published) (IF=6.173)

Conference presentations:

1) Hongxiang Ding, Junyi Zhang, Saori Kashima, Runsen Zhang. (2020). The role of risk
communication in mitigating the spread of COVID-19 in Japan: An analysis by building a
dynamic Scobit model. International e-Conference on Pandemics and Transport Policy(ICPT2020), WCTRS COVID-19 Task Force, December 7-11, 2020.
2) Hongxiang Ding, Junyi Zhang. (2020). Examining the effects of PASS-based and LAST-based
COVID-19 policy measures on changes in intercity and intracity behavioral choices in Japanby building a dynamic structural equation model with panel data. International e-Conference
on Pandemics and Transport Policy (ICPT2020), WCTRS COVID-19 Task Force, December
7-11, 2020.

157

Appendices

APPENDICES

Appendix 1: Questionnaire of life-oriented panel survey of
behavioral and psychological adaptations to COVID-19 inapaneseパートＩ 個人と世帯に関する質問
SC1
あなたの性別を教えて下さい。
1.男性 2.女性

SC2
あなたの年齢を教えて下さい。
1.１４歳以下
2.１５～１９歳
3.２０代
4.３０代
5.４０代
6.５０代
7.６０～６４歳
8.６５～６９歳
9.７０代以上

SC3
あなたのお住まいの都道府県をお答えください。

158

Appendices1.北海道 11.埼玉県 21.岐阜県 31.鳥取県 41.佐賀県
2.青森県 12.千葉県 22.静岡県 32.島根県 42.長崎県
3.岩手県 13.東京都 23.愛知県 33.岡山県 43.熊本県
4.宮城県 14.神奈川県 24.三重県 34.広島県 44.大分県
5.秋田県 15.新潟県 25.滋賀県 35.山口県 45.宮崎県
6.山形県 16.富山県 26.京都府 36.徳島県 46.鹿児島県
7.福島県 17.石川県 27.大阪府 37.香川県 47.沖縄県
8.茨城県 18.福井県 28.兵庫県 38.愛媛県
9.栃木県 19.山梨県 29.奈良県 39.高知県
10.群馬県 20.長野県 30.和歌山県 40.福岡県

SC4
あなたのお住まいをお答えください。
1.東京都
2.横浜市
3.川崎市
4.さいたま市
5.千葉市
6.相模原市
7.名古屋市
8.京都市
9.堺市
10.大阪市
11.神戸市
12.札幌市
13.仙台市
14.新潟市
15.静岡市
16.浜松市
17.岡山市
18.広島市19.北九州市
20.福岡市
21.熊本市22.その他の地域

SC5
あなたがお住まいの地域の郵便番号を７桁の数字（ハイフン無し）でお教えください。／※ハイフンなし___________

159

AppendicesSC6
あなたの職業をお答えください。
1.会社員／会社役員または自営業
2.公務員・団体職員
3.学校や大学などの教育機関の教職員
4.専業主婦5.パート・アルバイト
6.学生
7.その他の無職（年金生活者を含む）
8.その他

SC7
あなたの業種をお答えください。
1.農林漁業
2.鉱業、建設業
3.製造業
4.電気・ガス・熱供給・水道業
5.情報通信業
6.運輸業
7.卸売・小売業
8.金融・保険業
9.不動産業
10.飲食店・宿泊業
11.医療・福祉
12.教育・学習支援業
13.その他のサービス業（法務、学術、宗教、複合サービスなど）
14.その他SC8
あなたを含めて同居されているご家族の人数は何人ですか。
総人数___人
小学生以下の人数__人
中学生の人数__人
６５歳以上の高齢者の人数__人

SC9-SQ
あなたの婚姻状況をお知らせください。
1.未婚
2.既婚
3.離別・死別

160

AppendicesSC9
あなたと同居しているご家族の方を教えて下さい。
1.配偶者
2.自身の子ども
3.孫
4.自身の親・配偶者の親
5.その他
6.同居していない（一人暮らし）

SC10
あなたのお住まいについて当てはまるものをお選びください。
1.持ち家：一戸建て
2.持ち家：マンション等の集合住宅
3.賃貸：一戸建て
4.賃貸：マンション等の集合住宅
5.その他

SC11
あなたの最終学歴を教えて下さい。
1.中学（卒業済）2.高校（卒業済）
3.専修・専門学校（卒業済）
4.短大・高専（卒業済）
5.大学（卒業済）6.大学院（卒業済）
7.中学（在学中）
8.高校（在学中）
9.専修・専門学校（在学中）
10.短大・高専（在学中）
11.大学（在学中）
12.大学院（在学中）

SC12
あなたの世帯の年収（税込み）は次のどれに当てはまりますか。もし収入がある方が２名以上いる場合は、すべての方の収入を合算してください。

161

Appendices1.３００万円未満
2.３００万円～４００万円未満
3.４００万円～５００万円未満
4.５００万円～６００万円未満
5.６００万円～７００万円未満
6.７００万円～８００万円未満
7.８００万円～９００万円未満
8.９００万円～１０００万円未満
9.１０００万円～１５００万円未満
10.１５００万円～２０００万円未満
11.２０００万円以上

パート II 新型コロナウイルス感染症に関する質問
Q1
あなたは、新型コロナウイルスの感染を予防するため、以下のことを行なわれていますか。
Q1_1.２０秒以上の手洗いを頻繁に行う
Q1_2.水がない場合、消毒液で手を清潔にする
Q1_3.目、鼻や口を、洗っていない手で触らない
Q1_4.咳エチケットをよく行う
Q1_5.マスクをよく着用するQ1_6.体調が不良のときに、自宅にいるようにする
1.まったくあてはまらない
2.あまりあてはまらない
3.どちらとも言えない
4.まあまああてはまる
5.非常にあてはまる

Q2
あなたは、この１週間くらいの間に、新型コロナウイルスの感染情報や対策などについて、情報を入手されましたか。情報を入手されている場合、一日で最大どれくらいの時間（分）を情報入手に費やされたかをご記入ください。もし、その手段からは情報入手されていない場合は、「０」とご記入ください。
Q2_1.テレビ: __分
Q2_2.ＳＮＳ（ソーシャル・ネットワーキング・サービス）から入手：Ｆａｃｅｂｏｏｋ、ＬＩＮＥ、Ｔｗｉ
ｔｔｅｒ、ＷｈａｔｓＡｐｐなど: __分
Q2_3.紙媒体の情報源（新聞、政府の広報誌など）: __分
Q2_4.ラジオ: __分
Q2_5.ご自身のソーシャルネットワーク（周囲の人：知人、知人や同僚など）との情報交流: __分

Q3
新型コロナウイルスに関する情報源の「信頼性」として、あなたご自身の評価をお答えください。

162

AppendicesQ3_1.日本政府の発表情報（ホームページ情報、ニュース、広報誌など）
Q3_2.地方自治体政府の発表情報（ホームページ情報、ニュース、広報誌など）
Q3_3.専門家の見解（様々なチャンネルを通じて）
Q3_4.国内ニュース
Q3_5.海外ニュース
Q3_6.医療機関
Q3_7.職場・学校
Q3_8.ＳＮＳ（ソーシャル・ネットワーキング・サービス）：Ｆａｃｅｂｏｏｋ、ＬＩＮＥ、Ｔｗｉｔｔｅｒ、ＷｈａｔｓＡｐｐなど
Q3_9.検索エンジン：グーグル、ヤフーなど
Q3_10.ご自身のソーシャルネットワーク：周囲の人（知人、知人や同僚など）
1.最も低い2.低い
3.どちらとも言えない
4.高い
5.最も高いQ4
以下の新型コロナウイルス感染のことについて、どのように感じていますか。
Q4_1.日本全体の感染リスクが高まっているQ4_2.ご自身が居住する都道府県での感染リスクが高まっているQ4_3.ご自身が居住する市町村での感染リスクが高まっている
Q4_4.ご自身がよく行く場所（職場や学校、スーパー、レストラン、スポーツジムなど）での感染リスクが高まっている
Q4_5.混んでいる電車やバスのなかで感染する可能性が高い
Q4_6.新型コロナウイルスはとても怖い病気だ
Q4_7.ご自身が新型コロナウイルスにかかる可能性は非常に高い
Q4_8.ご自身は季節性インフルエンザにかかりやすいほうだ
1.まったく思っていない
2.あまり思っていない
3.どちらとも言えない
4.まあまあ思っている
5.非常に思っている

Q5
Q5_1. ４月は163

AppendicesQ5_1_1.国内観光旅行
Q5_1_2.国内出張
Q5_1_3.海外観光旅行
Q5_1_4.海外出張
Q5_1_5.フライト（国内）Q5_1_6.鉄道（国内）Q5_1_7.ほかの国内移動
Q5_1_8.コンサート
Q5_1_9.スポーツ観戦
1.変更せずに実施した
2.延期した3.キャンセルした
4.該当しない（この活動を行う予定はなかった）Q5_2. 5 月はQ5_2_1.国内観光旅行
Q5_2_2.国内出張
Q5_2_3.海外観光旅行
Q5_2_4.海外出張
Q5_2_5.フライト（国内）Q5_2_6.鉄道（国内）Q5_2_7.ほかの国内移動
Q5_2_8.コンサート
Q5_2_9.スポーツ観戦
1.変更せずに実施した
2.延期した3.キャンセルした
4.該当しない（この活動を行う予定はなかった）Q5_3. 6 月はQ5_3_1.国内観光旅行
Q5_3_2.国内出張
Q5_3_3.海外観光旅行
Q5_3_4.海外出張
Q5_3_5.フライト（国内）Q5_3_6.鉄道（国内）Q5_3_7.ほかの国内移動
Q5_3_8.コンサート
Q5_3_9.スポーツ観戦
1.変更せずに実施した
2.延期した3.キャンセルした
4.該当しない（この活動を行う予定はなかった）

164

AppendicesQ5_4. 7 月はQ5_4_1.国内観光旅行
Q5_4_2.国内出張
Q5_4_3.海外観光旅行
Q5_4_4.海外出張
Q5_4_5.フライト（国内）Q5_4_6.鉄道（国内）Q5_4_7.ほかの国内移動
Q5_4_8.コンサート
Q5_4_9.スポーツ観戦
1.変更せずに実施した
2.延期した3.キャンセルした
4.該当しない（この活動を行う予定はなかった）Q5_5. 8 月はQ5_5_1.国内観光旅行
Q5_5_2.国内出張
Q5_5_3.海外観光旅行
Q5_5_4.海外出張
Q5_5_5.フライト（国内）Q5_5_6.鉄道（国内）Q5_5_7.ほかの国内移動
Q5_5_8.コンサート
Q5_5_9.スポーツ観戦
1.変更せずに実施した
2.延期した3.キャンセルした
4.該当しない（この活動を行う予定はなかった）Q5_6. 9 月はQ5_6_1.国内観光旅行
Q5_6_2.国内出張
Q5_6_3.海外観光旅行
Q5_6_4.海外出張
Q5_6_5.フライト（国内）Q5_6_6.鉄道（国内）Q5_6_7.ほかの国内移動
Q5_6_8.コンサート
Q5_6_9.スポーツ観戦

165

Appendices1.変更せずに実施した
2.延期した3.キャンセルした
4.該当しない（この活動を行う予定はなかった）

Q6
Q6_1. 一年前の「４月」と 今年の「４月」 を比較すると・・・・・

Q6_1_1.出勤・退社時刻（通学・帰宅時刻）はもっと柔軟になったQ6_1_2.混雑時時以外の出勤・退社（通学・帰宅）をするようにした
Q6_1_3.勤務先や学校を出た後の寄り道行動が減った
Q6_1_4.自宅でのテレワークや勉強が増えた
Q6_1_5.仕事の量が減った
Q6_1_6.全体の外出回数が減った
Q6_1_7.オンラインショッピングやテレショッピングは増えた
Q6_1_8.食事のオンライン注文や電話注文が増えた
Q6_1_9.店に行く買物が減った
Q6_1_10.外食は減った
Q6_1_11.人混みの多い場所への外出を避けるようにした
Q6_1_12.人との接触のある密閉空間での活動を避けるようにした
Q6_1_13.近い距離で人と話すことを避けるようにした
Q6_1_14.感染症状がなくてもご家族への訪問を避けるようにした
Q6_1_15.ご家族に、ご自身への訪問をしないように依頼した
Q6_1_16.感染状況が悪い場所からの来訪客に会わないようにした
Q6_1_17.公共交通での移動が減 Q6
Q6_1_18.車での移動が増えた
Q6_1_19.徒歩や自転車での移動が増えた
Q6_1_20.国内旅行が減った
Q6_1_21.海外旅行が減った
Q6_1_22.予定なしの国内旅行をした：日帰り
Q6_1_23.予定なしの国内旅行をした：宿泊
Q6_1_24.運動頻度・時間が増えた
Q6_1_25.自分の家事の時間が増えた
Q6_1_26.家族の家事の時間が増えた
Q6_1_27.家族との会話が増えた
Q6_1_28.自宅でのＳＮＳが増えた
Q6_1_29.友人・知人との付き合いが減った
Q6_1_30.睡眠時間が増えた

166

AppendicesQ6_1_31.自分の趣味に費やす時間が増えた
Q6_1_32.世帯エネルギー消費（ガソリン、光熱、電力など）が増えた
Q6_1_33.飲酒の回数・量が増えた
Q6_1_34.体重が増えた
Q6_1_35.医療費の出費が増えた
Q6_1_36.消費全体を控えるようになったQ6_1_37.家にこもる時間が長くなり、心身的に疲れてきている
Q6_1_38.ご自身が周りの人の行動変容を勧めるようにした
1.はい
2.いいえ
3.該当しないQ6_2. 一年前の「5 月」と 今年の「5 月」

を比較すると・・・・・

Q6_2_1.出勤・退社時刻（通学・帰宅時刻）はもっと柔軟になったQ6_2_2.混雑時時以外の出勤・退社（通学・帰宅）をするようにした
Q6_2_3.勤務先や学校を出た後の寄り道行動が減った
Q6_2_4.自宅でのテレワークや勉強が増えた
Q6_2_5.仕事の量が減った
Q6_2_6.全体の外出回数が減った
Q6_2_7.オンラインショッピングやテレショッピングは増えた
Q6_2_8.食事のオンライン注文や電話注文が増えた
Q6_2_9.店に行く買物が減った
Q6_2_10.外食は減った
Q6_2_11.人混みの多い場所への外出を避けるようにした
Q6_2_12.人との接触のある密閉空間での活動を避けるようにした
Q6_2_13.近い距離で人と話すことを避けるようにした
Q6_2_14.感染症状がなくてもご家族への訪問を避けるようにした
Q6_2_15.ご家族に、ご自身への訪問をしないように依頼した
Q6_2_16.感染状況が悪い場所からの来訪客に会わないようにした
Q6_2_17.公共交通での移動が減 Q6Q6_2_18.車での移動が増えた
Q6_2_19.徒歩や自転車での移動が増えた
Q6_2_20.国内旅行が減った
Q6_2_21.海外旅行が減った
Q6_2_22.予定なしの国内旅行をした：日帰り
Q6_2_23.予定なしの国内旅行をした：宿泊

167

AppendicesQ6_2_24.運動頻度・時間が増えた
Q6_2_25.自分の家事の時間が増えた
Q6_2_26.家族の家事の時間が増えた
Q6_2_27.家族との会話が増えた
Q6_2_28.自宅でのＳＮＳが増えた
Q6_2_29.友人・知人との付き合いが減った
Q6_2_30.睡眠時間が増えた
Q6_2_31.自分の趣味に費やす時間が増えた
Q6_2_32.世帯エネルギー消費（ガソリン、光熱、電力など）が増えた
Q6_2_33.飲酒の回数・量が増えた
Q6_2_34.体重が増えた
Q6_2_35.医療費の出費が増えた
Q6_2_36.消費全体を控えるようになったQ6_2_37.家にこもる時間が長くなり、心身的に疲れてきている
Q6_2_38.ご自身が周りの人の行動変容を勧めるようにした
1.はい
2.いいえ
3.該当しないQ6_3. 一年前の「6 月」と 今年の「6 月」

を比較すると・・・・・

Q6_3_1.出勤・退社時刻（通学・帰宅時刻）はもっと柔軟になったQ6_3_2.混雑時時以外の出勤・退社（通学・帰宅）をするようにした
Q6_3_3.勤務先や学校を出た後の寄り道行動が減った
Q6_3_4.自宅でのテレワークや勉強が増えた
Q6_3_5.仕事の量が減った
Q6_3_6.全体の外出回数が減った
Q6_3_7.オンラインショッピングやテレショッピングは増えた
Q6_3_8.食事のオンライン注文や電話注文が増えた
Q6_3_9.店に行く買物が減った
Q6_3_10.外食は減った
Q6_3_11.人混みの多い場所への外出を避けるようにした
Q6_3_12.人との接触のある密閉空間での活動を避けるようにした
Q6_3_13.近い距離で人と話すことを避けるようにした
Q6_3_14.感染症状がなくてもご家族への訪問を避けるようにした
Q6_3_15.ご家族に、ご自身への訪問をしないように依頼した
Q6_3_16.感染状況が悪い場所からの来訪客に会わないようにした

168

AppendicesQ6_3_17.公共交通での移動が減 Q6Q6_3_18.車での移動が増えた
Q6_3_19.徒歩や自転車での移動が増えた
Q6_3_20.国内旅行が減った
Q6_3_21.海外旅行が減った
Q6_3_22.予定なしの国内旅行をした：日帰り
Q6_3_23.予定なしの国内旅行をした：宿泊
Q6_3_24.運動頻度・時間が増えた
Q6_3_25.自分の家事の時間が増えた
Q6_3_26.家族の家事の時間が増えた
Q6_3_27.家族との会話が増えた
Q6_3_28.自宅でのＳＮＳが増えた
Q6_3_29.友人・知人との付き合いが減った
Q6_3_30.睡眠時間が増えた
Q6_3_31.自分の趣味に費やす時間が増えた
Q6_3_32.世帯エネルギー消費（ガソリン、光熱、電力など）が増えた
Q6_3_33.飲酒の回数・量が増えた
Q6_3_34.体重が増えた
Q6_3_35.医療費の出費が増えた
Q6_3_36.消費全体を控えるようになったQ6_3_37.家にこもる時間が長くなり、心身的に疲れてきている
Q6_3_38.ご自身が周りの人の行動変容を勧めるようにした
1.はい
2.いいえ
3.該当しないQ6_4. 一年前の「7 月」と 今年の「7 月」

を比較すると・・・・・

Q6_4_1.出勤・退社時刻（通学・帰宅時刻）はもっと柔軟になったQ6_4_2.混雑時時以外の出勤・退社（通学・帰宅）をするようにした
Q6_4_3.勤務先や学校を出た後の寄り道行動が減った
Q6_4_4.自宅でのテレワークや勉強が増えた
Q6_4_5.仕事の量が減った
Q6_4_6.全体の外出回数が減った
Q6_4_7.オンラインショッピングやテレショッピングは増えた
Q6_4_8.食事のオンライン注文や電話注文が増えた
Q6_4_9.店に行く買物が減った

169

AppendicesQ6_4_10.外食は減った
Q6_4_11.人混みの多い場所への外出を避けるようにした
Q6_4_12.人との接触のある密閉空間での活動を避けるようにした
Q6_4_13.近い距離で人と話すことを避けるようにした
Q6_4_14.感染症状がなくてもご家族への訪問を避けるようにした
Q6_4_15.ご家族に、ご自身への訪問をしないように依頼した
Q6_4_16.感染状況が悪い場所からの来訪客に会わないようにした
Q6_4_17.公共交通での移動が減 Q6Q6_4_18.車での移動が増えた
Q6_4_19.徒歩や自転車での移動が増えた
Q6_4_20.国内旅行が減った
Q6_4_21.海外旅行が減った
Q6_4_22.予定なしの国内旅行をした：日帰り
Q6_4_23.予定なしの国内旅行をした：宿泊
Q6_4_24.運動頻度・時間が増えた
Q6_4_25.自分の家事の時間が増えた
Q6_4_26.家族の家事の時間が増えた
Q6_4_27.家族との会話が増えた
Q6_4_28.自宅でのＳＮＳが増えた
Q6_4_29.友人・知人との付き合いが減った
Q6_4_30.睡眠時間が増えた
Q6_4_31.自分の趣味に費やす時間が増えた
Q6_4_32.世帯エネルギー消費（ガソリン、光熱、電力など）が増えた
Q6_4_33.飲酒の回数・量が増えた
Q6_4_34.体重が増えた
Q6_4_35.医療費の出費が増えた
Q6_4_36.消費全体を控えるようになったQ6_4_37.家にこもる時間が長くなり、心身的に疲れてきている
Q6_4_38.ご自身が周りの人の行動変容を勧めるようにした
1.はい
2.いいえ
3.該当しないQ6_5. 一年前の「8 月」と 今年の「8 月」

を比較すると・・・・・

Q6_5_1.出勤・退社時刻（通学・帰宅時刻）はもっと柔軟になったQ6_5_2.混雑時時以外の出勤・退社（通学・帰宅）をするようにした

170

AppendicesQ6_5_3.勤務先や学校を出た後の寄り道行動が減った
Q6_5_4.自宅でのテレワークや勉強が増えた
Q6_5_5.仕事の量が減った
Q6_5_6.全体の外出回数が減った
Q6_5_7.オンラインショッピングやテレショッピングは増えた
Q6_5_8.食事のオンライン注文や電話注文が増えた
Q6_5_9.店に行く買物が減った
Q6_5_10.外食は減った
Q6_5_11.人混みの多い場所への外出を避けるようにした
Q6_5_12.人との接触のある密閉空間での活動を避けるようにした
Q6_5_13.近い距離で人と話すことを避けるようにした
Q6_5_14.感染症状がなくてもご家族への訪問を避けるようにした
Q6_5_15.ご家族に、ご自身への訪問をしないように依頼した
Q6_5_16.感染状況が悪い場所からの来訪客に会わないようにした
Q6_5_17.公共交通での移動が減 Q6Q6_5_18.車での移動が増えた
Q6_5_19.徒歩や自転車での移動が増えた
Q6_5_20.国内旅行が減った
Q6_5_21.海外旅行が減った
Q6_5_22.予定なしの国内旅行をした：日帰り
Q6_5_23.予定なしの国内旅行をした：宿泊
Q6_5_24.運動頻度・時間が増えた
Q6_5_25.自分の家事の時間が増えた
Q6_5_26.家族の家事の時間が増えた
Q6_5_27.家族との会話が増えた
Q6_5_28.自宅でのＳＮＳが増えた
Q6_5_29.友人・知人との付き合いが減った
Q6_5_30.睡眠時間が増えた
Q6_5_31.自分の趣味に費やす時間が増えた
Q6_5_32.世帯エネルギー消費（ガソリン、光熱、電力など）が増えた
Q6_5_33.飲酒の回数・量が増えた
Q6_5_34.体重が増えた
Q6_5_35.医療費の出費が増えた
Q6_5_36.消費全体を控えるようになったQ6_5_37.家にこもる時間が長くなり、心身的に疲れてきている
Q6_5_38.ご自身が周りの人の行動変容を勧めるようにした

171

Appendices

1.はい
2.いいえ
3.該当しないQ6_6. 一年前の「9 月」と 今年の「9 月」

を比較すると・・・・・

Q6_6_1.出勤・退社時刻（通学・帰宅時刻）はもっと柔軟になったQ6_6_2.混雑時時以外の出勤・退社（通学・帰宅）をするようにした
Q6_6_3.勤務先や学校を出た後の寄り道行動が減った
Q6_6_4.自宅でのテレワークや勉強が増えた
Q6_6_5.仕事の量が減った
Q6_6_6.全体の外出回数が減った
Q6_6_7.オンラインショッピングやテレショッピングは増えた
Q6_6_8.食事のオンライン注文や電話注文が増えた
Q6_6_9.店に行く買物が減った
Q6_6_10.外食は減った
Q6_6_11.人混みの多い場所への外出を避けるようにした
Q6_6_12.人との接触のある密閉空間での活動を避けるようにした
Q6_6_13.近い距離で人と話すことを避けるようにした
Q6_6_14.感染症状がなくてもご家族への訪問を避けるようにした
Q6_6_15.ご家族に、ご自身への訪問をしないように依頼した
Q6_6_16.感染状況が悪い場所からの来訪客に会わないようにした
Q6_6_17.公共交通での移動が減 Q6
Q6_6_18.車での移動が増えた
Q6_6_19.徒歩や自転車での移動が増えた
Q6_6_20.国内旅行が減った
Q6_6_21.海外旅行が減った
Q6_6_22.予定なしの国内旅行をした：日帰り
Q6_6_23.予定なしの国内旅行をした：宿泊
Q6_6_24.運動頻度・時間が増えた
Q6_6_25.自分の家事の時間が増えた
Q6_6_26.家族の家事の時間が増えた
Q6_6_27.家族との会話が増えた
Q6_6_28.自宅でのＳＮＳが増えた
Q6_6_29.友人・知人との付き合いが減った
Q6_6_30.睡眠時間が増えた
Q6_6_31.自分の趣味に費やす時間が増えた

172

AppendicesQ6_6_32.世帯エネルギー消費（ガソリン、光熱、電力など）が増えた
Q6_6_33.飲酒の回数・量が増えた
Q6_6_34.体重が増えた
Q6_6_35.医療費の出費が増えた
Q6_6_36.消費全体を控えるようになったQ6_6_37.家にこもる時間が長くなり、心身的に疲れてきている
Q6_6_38.ご自身が周りの人の行動変容を勧めるようにした
1.はい
2.いいえ
3.該当しない

Q7
先ほどお答えいただいたような日常生活における様々な変化は、色々なキッカケによって引き起こされたと考えられますが、以下のような事柄は、どの程度きっかけになったと思われますか。
Q7_1.自分の感染状況に対する理解によって、上記の各種変化が生じた
Q7_2.政府の要請によって、上記の各種変化が生じた
Q7_3.家族からの勧めによって、上記の各種変化が生じた
Q7_4.同僚、知人や友人などからの勧めによって、上記の各種変化が生じた
Q7_5.所属団体からの要請によって、上記の各種変化が生じた
Q7_6.感染者数の状況を見て、上記の各種変化が生じた
Q7_7.周りの目や批判を気にして、上記の各種変化が生じた
1.まったく思っていない
2.あまり思っていない
3.どちらとも言えない
4.まあまあ思っている
5.非常に思っている

Q8
新型コロナウイルスの感染拡大を防ぐ各種取り組みについて、あなたはどう思いますか。
Q8_1.新型コロナウイルスについて、日本の保健・医療機関は十分な専門的知識を持っている
Q8_2.新型コロナウイルスについて、日本政府は十分な専門的知識を持っている
Q8_3.新型コロナウイルスについて、ご自身が居住している地方自治体は十分な専門的知識を持っている
Q8_4.新型コロナウイルス感染拡大を防ぐにあたり、日本政府の熱意が感じられる
Q8_5.新型コロナウイルス感染拡大を防ぐにあたり、ご自身が居住している地方自治体の熱意が感じられる
Q8_6.新型コロナウイルス感染拡大を防ぐにあたり、日本政府の管理体制や対策ができている
Q8_7.新型コロナウイルス感染拡大を防ぐにあたり、ご自身が居住している地方自治体の管理体制や対策ができている
Q8_8.感染拡大を防ぐにあたり、日本の保健・医療機関を信頼できる

173

AppendicesQ8_9.新型コロナウイルスの感染拡大を防ぐにあたり、日本政府を信頼できる
Q8_10.新型コロナウイルスの感染拡大を防ぐにあたり、ご自身が居住している地方自治体を信頼できる
Q8_11.日本政府は、説明責任をもっと果たすべきQ8_12.ご自身が居住している地方自治体は、説明責任をもっと果たすべきQ8_13.日本政府として、全国規模の対策をもっと関連徹底的に講じるべき
Q8_14.ご自身が居住している地方自治体は関連対策をもっと徹底的に講じるべき
Q8_15.感染者の行動履歴を通信会社などから入手し、個人情報を保護の上、ホームページの地図上に公開すべきQ8_16.感染者の行動履歴（個人情報を保護の上）を通信会社などから入手し、それをもとに、感染可能性の高い場所を特定し、場所限定の対策を講じるべき
Q8_17.政府が講じている現在の対策・対応をみると、万が一、新型コロナウイルスが日本全体に流行する場
合（パンデミックの状態）、大変心配だ
Q8_18.新型コロナウイルスの感染拡大を防ぐ政府の現在の対策は、日本全体の感染リスクを下げるのに大きな効果があるQ8_19.新型コロナウイルスの感染拡大を防ぐ政府の現在の対策は、ご自身が居住する都道府県での感染リス
クを下げるのに大きな効果がある
Q8_20.新型コロナウイルスの感染拡大を防ぐ政府の現在の対策は、日本経済の短期的な景気停滞をもたらす
恐れが高い
Q8_21.感染しない、または他人に感染させないようにするために、ご自身は非常に不便だと感じても、自分の生活ニーズ、ライフタイルを見直すことにまったく抵抗がないほうだ
Q8_22.感染しない、または他人に感染させないようにするために、ご自身は非常に不便だと感じても、毎日の活動内容、活動場所、活動時間を見直すことにまったく抵抗がないほうだ
Q8_23.ご自身がこのようなパンデミックのような非常事態によく備えるほうだ
Q8_24.感染しない、または他人に感染させないようにするために、非常に不便だと感じても、ご自身はフィジカルディスタンシングをよく実践しているほうだ
Q8_25.感染しない、または他人に感染させないようにするために、ご自身の行動履歴の詳細を医療機関・政府機関に提供することにまったく抵抗がないほうだ
Q8_26.感染しない、または他人に感染させないようにするために、ご自身は非常に不便だと感じても、３密（密集、密接、密閉）のある活動・行動を常に避けるようにするほうだ
Q8_27.感染しない、または他人に感染させないようにするために、非常に不便だと感じても、ご自身の活動・行動スケジュールや計画を常に調整するほうだ
Q8_28.感染しない、または他人に感染させないようにするために、ご自身は非常に不便だと感じても、仕事の勤務形態をテレワークにシフトしたり、公共交通以外の交通手段の利用にシフトしたりするほうだ
Q8_29.感染しない、または他人に感染させないようにするために、ご自身の行動履歴、健康状況の情報を他人に共用することにまったく抵抗がないほうだ
Q8_30.感染しない、または他人に感染させないようにするために、ご自身は非常に不便だと感じても、オンラインでの活動に切り換えたり、旅行などを延期したり、移動回数や距離を減らしたり、不要不急の活動を止めたりするほうだQ8_31.感染しない、または他人に感染させないようにするために、ご自身は非常に不便だと感じても、移動を伴う活動を中止し、家でパーティーしないようにするほうだ
Q8_32.日本では新型コロナウイルスワクチンの予防接種が無料化となれば、予防接種を受けたいQ8_33.新型コロナウイルスのワクチンを接種すれば、感染しないと思う
Q8_34.コロナ禍であるが、自分の生活の質が非常にいい

174

AppendicesQ8_35.今後の生活に非常に不安を感じている
Q8_36.一般論として、ご自身は今日のために生きていて、明日のことを考えないほうだ
Q8_37.今回の新型コロナウイルス感染の状況をみると、ご自身の人生観が大きく変わったQ8_38.重要な意思決定を専門家や政府に任せて、そのような社会的な秩序を支持するほうだ
Q8_39.だれもが平等で、多数の人々の利益がどの個人の利益よりも優先されるべき、そして、重大なリスクに関する意思決定は少数のエリートや権威ではなく、すべての人々で行うべきだと思う
Q8_40.個人の自由が絶対に重要であり、個人の選択が社会や他の人々によって制約されるべきではないと思う
Q8_41.自分自身が周りの環境を左右することができないので、運命に身を任せ、その運命を知ろうとしな
い、心配しないように努めるほうだ
1.まったく思っていない
2.あまり思っていない
3.どちらとも言えない
4.まあまあ思っている
5.非常に思っている

Q9
あなたは、Ｇｏ Ｔｏトラベル

キャンペーンを利用しましたか。

1.既に利用した
2.まだ利用していないが、利用する予定
3.利用する予定はない

Q10
あなたは、今年のゴールデンウイーク期間中に、帰省や旅行をしましたか。
1.はい
2.いいえ

Q11
あなたは、今年のシルバーウィーク期間中（９／１８～９／２２）に、どこかを旅行しましたか。
1.はい
2.いいえ

Q12
あなたは今年、インフルエンザの予防接種をしましたか。
1.既に接種した
2.まだ接種していないが、接種する予定
3.接種する予定はない

Q13

175

Appendices

あなたは、ＣＯＶＩＤ－１９の重症化リスクが高くなる可能性がある基礎疾患をお持ちですか。当てはまるものがあればいくつでもお選びください。
1.糖尿病
2.心不全
3.呼吸器疾患（喘息、ＣＯＰＤ等）
4.肥満（ＢＭＩ ３０以上）

5.慢性腎疾患
6.免疫抑制剤や抗がん剤などを用いている
7.この中にあてはまるものはない

Q14
あなたの世帯で保有している自動車の総台数を教えてください。
__台Q15
あなたの世帯で保有している自動車のうち、ご自身が自由に使える車の有無について教えてください。
1.ほぼ自分専用の自動車はある
2.家族共用の自動車はある
3.自分が使える自動車はない

176

Appendices

Appendix 2: Questionnaire of comparative survey of activity-travel and social contact before-during
COVID-19 in English
Intro

The current COVID-19 pandemic has imposed various impacts on people’s lives. This questionnaire survey aims to investigatechanges in your daily life, social contacts, and protective measures against infection from viruses, etc., during the current pandemic(2020 and 2021) and before the pandemic (i.e., during influenza seasons in and before 2019). Your perceptions and attitudes related
to the control of the current pandemic are also asked. All data collected will be treated statistically and used only for scientificresearch on how to control the current pandemic and prevent future pandemics. Any privacy information that can identify a
specific person will not be included in the data. We would like to express our sincere thanks to your kind cooperation.

Part 1 Individual Household Attributes
A1

A2

Please tell us your gender.
1

Male

2

Female1

14 and below

2

15~19

3

20~29

4

30~39

5

40~49

6

50~59

Please tell us your age.

177

Appendices

A3

7

60~64

8

65~69

9

70 and above

Please tell us region/state, city/town and Postal
code of your residence.
Province/State/Region
City/town
Zip code/postal code

A4

A5

Please tell us your occupation.
1

Company employee / company officer or self-employed2

Governmental officier/staff / organization employee

3

Faculty or staff of an educational institution: e.g., school or university

4

Housewife

5

Part-time job

6

Student

7

Other unemployed (including pensioners)

8

Other

Which industry does your occupation belong
to?
12

or househusband

Agriculture, forestry, or fishery
Mining, or construction

178

Appendices

A6

A7

3

Manufacturing industry

4Electricity, gas, heat supply, or water supply

5

Information and communication industry

6

Transportation industry

7

Wholesale / retail8

Finance / insurance business

9

Real estate business10

Restaurant / accommodation business

11

Medical / welfare

12

Education / learning support business

13

Other service industries (legal, academic, religion, complex services, etc.)

14

Others

How many household members (including
yourself) live together?
1

Total number of household members living together (including yourself)2

Number of children under 11

3

Number of children 11-18

4

Number of people aged 65 and over

Please tell us your marital status.
1

Unmarried

2

Married

3

Divorce / bereavement179

Appendices
4

A8

A9

A10

Please tell us the family members with whom
you live?
1

Civil Partnership (not marriage but a legal status for couples)

Spouse/Couple

2Your child/children

3Grandchild / Grandchildren

4Parents/grandparents of you / Parents/grandparents of your spouse

5

Other

6

None

What is the type of your house?
1

Rental: Detached2Your own: Detached3

Rental: Semi-Detached4Your own: Semi-Detached5

Rental: Terraced

6

Your own: Terraced

7

Rental: Flat/Apartment

8Your own: Flat/Apartment

9

Other

What is the highest level of educational
qualification you have achieved?

180

Appendices(Allows one selection)

A11

1

No formal qualification2

Primary school/Elementary school

3

Secondary school/Middle school/ Junior high school

4

High school/Senior high school

5

Vocational school/College

6

Undergraduate/ Bachelor

7

Master degree8Doctoral degree

Which of the following represents the total ANNUAL income of your household BEFORE covid-19 PANDEMIC from all sources,
before tax?
(Allows one selection)

AUD

USD

NZD

CAD

GBP

1

under 15,000AUD

under 15,000USD

under 15,000NZD

under 15,000CAD

under 10,000GBP

2

15,000AUD to 24,999AUD

15,000USD to 24,999USD

15,000NZD to 24,999NZD

15,000CAD to 24,999CAD

10,000GBP to 19,999GBP

3

25,000AUD to 34,999AUD

25,000USD to 34,999USD

25,000NZD to 34,999NZD

25,000CAD to 34,999CAD

20,000GBP to 29,999GBP

4

35,000AUD to 49,999AUD

35,000USD to 49,999USD

35,000NZD to 49,999NZD

35,000CAD to 49,999CAD

30,000GBP to 39,999GBP

5

50,000AUD to 74,999AUD

50,000USD to 74,999USD

50,000NZD to 74,999NZD

50,000CAD to 74,999CAD

40,000GBP to 49,999GBP

6

75,000AUD to 99,999AUD

75,000USD to 99,999USD

75,000NZD to 99,999NZD

75,000CAD to 99,999CAD

50,000GBP to 74,999GBP

7

100,000AUD to 124,999AUD

100,000USD to 124,999USD

100,000NZD to 124,999NZD

100,000CAD to 124,999CAD

75,000GBP to 99,999GBP

8

125,000AUD to 149,999AUD

125,000USD to 149,999USD

125,000NZD to 149,999NZD

125,000CAD to 149,999CAD

100,000GBP to 124,999GBP

9150,000AUD to 174,999AUD

150,000USD to 174,999USD

150,000NZD to 174,999NZD

150,000CAD to 174,999CAD

125,000GBP to 149,999GBP

10

175,000AUD to 199,999AUD

175,000USD to 199,999USD

175,000NZD to 199,999NZD

175,000CAD to 199,999CAD

150,000GBP and over

11

200,000AUD and over

200,000USD and over

200,000NZD and over

200,000CAD and over181

AppendicesPart 2 Social Contacts During Activity
activity category of social
contact

activity location of social contact

Target periods

Q1. How often do you perform the following activity at the following
location?
[1] Never[2] The first time[3] Less than once a month[4] Once or twice a month[5] Once or twice a week
[6] Daily or almost daily
Before the COVID-19 pandemic: in During the pandemic: from 2020 to2019 (during the Influenza season)present

work/study

company/ school[1] [2] [3][4] [5] [6][1] [2] [3][4] [5] [6]shoppingsupermarket/shopping mall[1] [2] [3][4] [5] [6][1] [2] [3][4] [5] [6]eating out

restaurant[1] [2] [3][4] [5] [6][1] [2] [3][4] [5] [6]exercise/sports

indoor places (e.g., gym)

[1] [2] [3][4][5] [6][1] [2] [3][4] [5] [6]partyindoor public places(e.g., bar)[1] [2] [3][4] [5] [6][1] [2] [3][4] [5] [6]culture leisurecinema, concert hall, etc.

[1] [2] [3][4] [5] [6][1] [2] [3][4] [5] [6]see adoctor/purchase
medicine

hospital, clinic, etc.

[1] [2] [3][4] [5] [6][1] [2] [3][4] [5] [6]leisure/recreation

medicalactivities

category

location [Multiple choice]Target periods

182

Q2. How long is the distance between the following activity location andyour home?
[1] Less than 1 km
[2] 1 km or longer, but less than 2 km
[3] 2 km or longer, but less than 5 km
[4] 5 km or longer, but less than 10 km[5] Longer than 10 km
Before the COVID-19 pandemic: in During the pandemic: from 2020 to2019 (during the Influenza season)present

Appendiceswork/study

company/ school

[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

shoppingsupermarket/shopping mall

[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

eating out

restaurant

[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

exercise/sports

indoor places (e.g., gym)

[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

party

indoor public places(e.g., bar)

[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

culture leisurecinema, concert hall, etc.

[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

see a
doctor/purchase
medicine

hospital, clinic, etc.

[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

leisure/recreation

medicalactivities

activity category of social
contact

activity location of social contact

Target periods

Q3. How much is your average time to perform the following activity at
the following location each time?
[1] less than 5 mins
[2] 5 mins or longer, but less than 15 mins
[3] 15 mins or longer, but less than 30 mins
[4] 30 mins or longer, but less than 1 hour[5] 1 hour or longer, but less than 4 hours[6] 4 hours or longer
Before the COVID-19 pandemic: in During the pandemic: from 2020 to2019 (during the Influenza season)present

work/study

company/ school[1] [2] [3][4] [5] [6][1] [2] [3][4] [5] [6]shoppingsupermarket/shopping mall[1] [2] [3][4] [5] [6][1] [2] [3][4] [5] [6]eating out

restaurant[1] [2] [3][4] [5] [6][1] [2] [3][4] [5] [6]exercise/sports

indoor places (e.g., gym)

[1] [2] [3][4] [5] [6][1] [2] [3][4] [5] [6]partyindoor public places(e.g., bar)[1] [2] [3][4] [5] [6][1] [2] [3][4] [5] [6]culture leisurecinema, concert hall, etc.

[1] [2] [3][4] [5] [6][1] [2] [3][4] [5] [6]see adoctor/purchase
medicine

hospital, clinic, etc.

[1] [2] [3][4] [5] [6][1] [2] [3][4] [5] [6]leisure/recreation

medicalactivities

183

Appendicesactivity category of social
contact

activity location of social contact

Target periods

Q4. What is your major transportation to reach the location of thefollowing activity?
[1] Shared mobility: Uber/DiDi/Grab, etc.
[2] Taxi[3] Bus[4] Rail: Train, metro, street car, etc.
[5] Private car[6] Active transportation (walk, bicycle)[7] Others
Before the COVID-19 pandemic: in During the pandemic: from 2020 to2019 (during the Influenza season)present

work/study

company/ school[1] [2] [3] [4] [5] [6] [7][1] [2] [3] [4] [5] [6] [7]shoppingsupermarket/shopping mall[1] [2] [3] [4] [5] [6] [7][1] [2] [3] [4] [5] [6] [7]eating out

restaurant[1] [2] [3] [4] [5] [6] [7][1] [2] [3] [4] [5] [6] [7]exercise/sports

indoor places (e.g., gym)

[1][2] [3] [4] [5] [6] [7][1] [2] [3] [4] [5] [6] [7]party

indoor public places(e.g., bar)[1] [2] [3] [4] [5] [6] [7][1] [2] [3] [4] [5] [6] [7]culture leisurecinema, concert hall, etc.

[1] [2] [3] [4] [5] [6] [7]

[1]

see a
doctor/purchase
medicine

hospital, clinic, etc.

[1] [2] [3] [4] [5] [6] [7][1] [2] [3] [4] [5] [6] [7]activity location of social contact

Q5. How much is your average travel time (one-way) to reach thefollowing location of performing the following activity?
[1] Less than 10 mins
[2] 10 mins or longer, but less than 30 mins
[3] 30 mins or longer, but less than 1 hour[4] 1 hour or longer
Before the COVID-19 pandemic: in During the pandemic: from 2020 to2019 (during the Influenza season)present

leisure/recreation

medicalactivities

activity category of social
contact

Target periods[2] [3] [4] [5] [6] [7]work/study

company/ school

[1]

[2]

[3]

[4]

[1]

[2]

[3]

[4]

shoppingsupermarket/shopping mall

[1]

[2]

[3]

[4]

[1]

[2]

[3]

[4]

restaurant

[1]

[2]

[3]

[4]

[1]

[2]

[3]

[4]

eating out

184

Appendicesleisure/recreation

medicalactivities

exercise/sports

indoor places (e.g., gym)

[1]

[2]

[3]

[4]

[1]

[2]

[3]

[4]

party

indoor public places(e.g., bar)

[1]

[2]

[3]

[4]

[1]

[2]

[3]

[4]

culture leisurecinema, concert hall, etc.

[1]

[2]

[3]

[4]

[1]

[2]

[3]

[4]

see a
doctor/purchase
medicine

hospital, clinic, etc.

[1]

[2]

[3]

[4]

[1]

[2]

[3]

[4]

activity category of social
contact

activity location of social contact

Target periods

Q6. Please tell us your main companion when performing this activity at
this location.
[1] Spouse/Couple[2] Children/Grandchildren[3] Parents/Grandparents[4] Friends[5] Other acquaintances[6] Alone
Before the COVID-19 pandemic: in During the pandemic: from 2020 to2019 (during the Influenza season)present

shoppingsupermarket/shopping mall

[1]

[2]

[3]

[4] [5] [6]

[1]

[2]

[3]

[4] [5] [6]leisure/recreation

restaurant

[1]

[2]

[3]

[4] [5] [6]

[1]

[2]

[3]

[4] [5]exercise/sports

indoor places (e.g., gym)

[1]

[2]

[3]

[4] [5] [6]

[1]

[2]

[3]

[4] [5] [6]partyindoor public places(e.g., bar)

[1]

[2]

[3]

[4] [5] [6]

[1]

[2]

[3]

[4] [5] [6]culture leisurecinema, concert hall, etc.

[1]

[2]

[3]

[4] [5] [6]

[1]

[2]

[3]

[4] [5] [6]see adoctor/purchase
medicine

hospital, clinic, etc.

[1]

[2]

[3]

[4] [5] [6]

[1]

[2]

[3]

[4] [5] [6]activity category of social
contact

activity location of social contact[6]Q7. Except for your main companion, do you have contact with otherpersons during performing this activity at this location?
[1] No contact[2] Yes: only non-physical contacts (e.g., in the physical presence of anotherperson without face to face conversation, skin-to-skin touching or otherindirect physical contacts)[3] Yes: only physical contacts (e.g., having a face to face conversation or,

185

AppendicesTarget periods

skin-to-skin touching or other indirect physical contacts)[4] Yes: both non-physical and physical contacts
Before the COVID-19 pandemic: in During the pandemic: from 2020 to2019 (during the Influenza season)present

work/study

company/ school (Your colleagues and classmates are also included as
contacts with other persons for this location of company/school)

[1]

[2]

[3]

[4]

[1]

[2]

[3]

[4]

shoppingsupermarket/shopping mall

[1]

[2]

[3]

[4]

[1]

[2]

[3]

[4]

eating out

restaurant

[1]

[2]

[3]

[4]

[1]

[2]

[3]

[4]

exercise/sports

indoor places (e.g., gym)

[1]

[2]

[3]

[4]

[1]

[2]

[3]

[4]

party

indoor public places(e.g., bar)

[1]

[2]

[3]

[4]

[1]

[2]

[3]

[4]

culture leisurecinema, concert hall, etc.

[1]

[2]

[3]

[4]

[1]

[2]

[3]

[4]

see a
doctor/purchase
medicine

hospital, clinic, etc.

[1]

[2]

[3]

[4]

[1]

[2]

[3]

[4]

leisure/recreation

medicalactivities

activity category of social
contact

activity location of social contact

Target periods

Q8. Except for your main companion, how many persons do you contact
when performing the following activity?
[1] 2 persons or fewer[2] 3~4 persons[3] 5~6 persons[4] 7~9 persons[5] 10 persons or more
Before the COVID-19 pandemic: in During the pandemic: from 2020 to2019 (during the Influenza season)present

work/study

company/ school (Your colleagues and classmates are also included as
contacts with other persons for this location of company/school)

[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

shoppingsupermarket/shopping mall

[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

eating out

restaurant

[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

exercise/sports

indoor places (e.g., gym)

[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

party

indoor public places(e.g., bar)

[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

culture leisurecinema, concert hall, etc.

[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

leisure/recreation

186

Appendicesmedicalactivities

see a
doctor/purchase
medicine

activity category of social
contact

hospital, clinic, etc.

[1]

activity location of social contact

Target periods

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

Q9. Do you take the following protective measures when performing thisactivity at this location?
[1] Wear a mask
[2] Disinfect your hands before/during/after activity[3] Keep a proper distance with others[4] Other measures[5] No measures are taken
Before the COVID-19 pandemic: in During the pandemic: from 2020 to2019 (during the Influenza season)present

work/study

company/ school

[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

shoppingsupermarket/shopping mall

[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

eating out

restaurant

[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

exercise/sports

indoor places (e.g., gym)

[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

party

indoor public places(e.g., bar)

[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

culture leisurecinema, concert hall, etc.

[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

see a
doctor/purchase
medicine

hospital, clinic, etc.

[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

leisure/recreation

medicalactivities

activity category of social
contact

activity location of social contact

Target periods

187

Q10. How do you think about your self-efficiency for protecting yourself
when performing the following activity at the following location? (here,
self-efficiency means your belief for your power of protecting yourself
from infection)[1] Very low[2] Low[3] Neither[4] High[5] Very high
Before the COVID-19 pandemic: in During the pandemic: from 2020 to2019 (during the Influenza season)present

Appendiceswork/study

company/ school

[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

shoppingsupermarket/shopping mall

[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

eating out

restaurant

[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

exercise/sports

indoor places (e.g., gym)

[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

party

indoor public places(e.g., bar)

[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

culture leisurecinema, concert hall, etc.

[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

see a
doctor/purchase
medicine

hospital, clinic, etc.

[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

leisure/recreation

medicalactivities

activity category of social
contact

activity location of social contact

Target periods

Q11. Do you agree that people (e.g., staffs, other consumers) in thefollowing activity location take proper measures to protect other persons(including you)?
[1] Strongly disagree[2] Somewhat disagree[3] Neither[4] Somewhat agree[5] Strongly agree
Before the COVID-19 pandemic: in During the pandemic: from 2020 to2019 (during the Influenza season)present

work/study

company/ school

[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

shoppingsupermarket/shopping mall

[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

eating out

restaurant

[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

exercise/sports

indoor places (e.g., gym)

[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

party

indoor public places(e.g., bar)

[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

culture leisurecinema, concert hall, etc.

[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

see a
doctor/purchase
medicine

hospital, clinic, etc.

[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

leisure/recreation

medicalactivities

188

Appendicesactivity category of social
contact

activity location of social contact

Target periods

Q12. Do you think that the following activity is performed at the following
location with either of the following three Cs (Closed spaces with poor
ventilation, Crowded places, Close-contact settings)?
[1] Closed spaces with poor ventilation[2] Crowded places[3] Close-contact settings[4] None of the above
Before the COVID-19 pandemic: in During the pandemic: from 2020 to2019 (during the Influenza season)present

work/study

company/ school

[1]

[2]

[3]

[4]

[1]

[2]

[3]

[4]

shoppingsupermarket/shopping mall

[1]

[2]

[3]

[4]

[1]

[2]

[3]

[4]

eating out

restaurant

[1]

[2]

[3]

[4]

[1]

[2]

[3]

[4]

exercise/sports

indoor places (e.g., gym)

[1]

[2]

[3]

[4]

[1]

[2]

[3]

[4]

party

indoor public places(e.g., bar)

[1]

[2]

[3]

[4]

[1]

[2]

[3]

[4]

culture leisurecinema, concert hall, etc.

[1]

[2]

[3]

[4]

[1]

[2]

[3]

[4]

see a
doctor/purchase
medicine

hospital, clinic, etc.

[1]

[2]

[3]

[4]

[1]

[2]

[3]

[4]

leisure/recreation

medicalactivities

activity category of social
contact

activity location of social contact

Target periods

Q13. Do you feel you are safe (from infection) when performing thefollowing activity at the following location?
[1] Strongly disagree[2] Somewhat disagree[3] Neither[4] Somewhat agree[5] Strongly agree
Before the COVID-19 pandemic: in During the pandemic: from 2020 to2019 (during the Influenza season)present

work/study

company/ school

[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

shoppingsupermarket/shopping mall

[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

restaurant

[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

eating out

189

Appendicesleisure/recreation

medicalactivities

exercise/sports

indoor places (e.g., gym)

[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

party

indoor public places(e.g., bar)

[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

culture leisurecinema, concert hall, etc.

[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

see a
doctor/purchase
medicine

hospital, clinic, etc.

[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

activity category of social
contact

activity location of social contact

Target periods

Q14. Do you feel your household members is are safe (from infection) if
they are performing the following activity at the following location?
[1] Strongly disagree[2] Somewhat disagree[3] Neither[4] Somewhat agree[5] Strongly agree
Before the COVID-19 pandemic: in During the pandemic: from 2020 to2019 (during the Influenza season)present

work/study

company/ school

[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

shoppingsupermarket/shopping mall

[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

eating out

restaurant

[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

exercise/sports

indoor places (e.g., gym)

[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

party

indoor public places(e.g., bar)

[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

culture leisurecinema, concert hall, etc.

[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

see a
doctor/purchase
medicine

hospital, clinic, etc.

[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

leisure/recreation

medicalactivities

activity category of social
contact

activity location of social contact

Q15. What is your psychological state when performing the following
activity at the following location?
[1] Exhausted[2] Depressed, discouraged[3] Nervous[4] Energetic, vigorous

190

AppendicesTarget periods

work/study

company/ school

shoppingsupermarket/shopping mallleisure/recreation

medicalactivities[5] Tensed, under pressure[6] Happy, satisfied[7] Helpless[8] Refresh, rested[9] Impetuous, inpatient[10] Anxious[11] Other
Before the COVID-19 pandemic: in2019 (during the Influenza season)

During the pandemic: from 2020 topresent

[1]
[2]
[3]
[4]
[5] [6][7][8][9][10] [11]
[1]
[2]
[3]
[4]
[5] [6][7][8][9][10] [11]
[1]
[2]
[3]
[4]
[5] [6][7][8][9][10] [11]
[1]
[2]
[3]
[4]
[5] [6][7][8][9][10] [11]
[1]
[2]
[3]
[4]
[5] [6][7][8][9][10] [11]
[1]
[2]
[3]
[4]
[5] [6][7][8][9][10] [11]

[1]
[2]
[3]
[4]
[5] [6][7][8][9][10] [11]
[1]
[2]
[3]
[4]
[5] [6][7][8][9][10] [11]
[1]
[2]
[3]
[4]
[5] [6][7][8][9][10] [11]
[1]
[2]
[3]
[4]
[5] [6][7][8][9][10] [11]
[1]
[2]
[3]
[4]
[5] [6][7][8][9][10] [11]
[1]
[2]
[3]
[4]
[5] [6][7][8][9][10] [11]
[1]
[2]
[3]
[4]
[5] [6][7][8][9][10] [11]eating out

restaurant

exercise/sports

indoor places (e.g., gym)

party

indoor public places(e.g., bar)culture leisurecinema, concert hall, etc.

see adoctor/purchase
medicine

hospital, clinic, etc.

[1]
[2]
[3]
[4]
[5] [6][7][8][9][10] [11]activity location of social contact

Q16. What are your benefits/purposes for doing the following activities?
[1] Warm relationships with others[2] Convenience of life
[3] Supply of food and daily life
[4] Relieve the pressure[5] Preserving my public image/ Social recognition /Being well-respected[6] Self-Security[7] Saving money[8] Make money[9] Wisdom[10] Physical Health[11] Sense of accomplishment[12] Feeling of freedom[13] Self-fulfillment/ Self-respect[14] Sense of belonging[15] Fun-enjoyment-excitement

activity category of social
contact

191

AppendicesTarget periods

work/study

company/ school

shoppingsupermarket/shopping mallleisure/recreation

medicalactivites

eating out

restaurant

exercise/sports

indoor places (e.g., gym)

party

indoor public places(e.g., bar)culture leisurecinema, concert hall, etc.

see adoctor/purchase
medicine

hospital, clinic, etc.

activity category of social
contact

activity location of social contact

Target periods[16] Relaxation[17] Other
Before the COVID-19 pandemic: in2019 (during the Influenza season)

During the pandemic: from 2020 topresent

[1]
[2]
[3]
[4]
[…...
[15
[16
[1]
[2]
[3]
[4]
[…...
[15
[16
[1]
[2]
[3]
[4]
[…...
[15
[16
[1]
[2]
[3]
[4]
[…...
[15
[16
[1]
[2]
[3]
[4]
[…...
[15
[16
[1]
[2]
[3]
[4]
[…...
[15
[16

[5]
[17
[5]
[17
[5]
[17
[5]
[17
[5]
[17
[5]
[17

[1]
[2]
[3]
[4]
[…...
[15
[16
[1]
[2]
[3]
[4]
[…...
[15
[16
[1]
[2]
[3]
[4]
[…...
[15
[16
[1]
[2]
[3]
[4]
[…...
[15
[16
[1]
[2]
[3]
[4]
[…...
[15
[16
[1]
[2]
[3]
[4]
[…...
[15] [16]

[5]
[17
[5]
[17
[5]
[17
[5]
[17
[5]
[17
[5]
[17]

[1]
[2]
[3]
[4]
[…...
[15
[16

[5]
[17

[1]
[2]
[3]
[4]
[…...
[15
[16

[5]
[17

Q17. Do you agree with that in order to avoid the spread of virus, without
governments' permission, the people performing the following activity atsuch a location should be punished?
[1]Totally disagree[2]Somewhat disagree[3]Neither[4]Somewhat agree[5]Totally agree
Before the COVID-19 pandemic: in During the pandemic: from 2020 to2019 (during the Influenza season)present

work/study

company/ school

[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

shoppingsupermarket/shopping mall

[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

eating out

restaurant

[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

exercise/sports

indoor places (e.g., gym)

[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

party

indoor public places(e.g., bar)

[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

leisure/recreation

192

Appendicesmedicalactivities

culture leisurecinema, concert hall, etc.

[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

see a
doctor/purchase
medicine

hospital, clinic, etc.

[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

activity category of social
contact

activity location of social contact

Target periods

Q18. Do you agree with that you will perform the following activity at thefollowing location, even if a high infection risk is known?
[1] Strongly disagree[2] Somewhat disagree[3] Neither[4] Somewhat agree[5] Strongly agree
Before the COVID-19 pandemic: in During the pandemic: from 2020 to2019 (during the Influenza season)present

work/study

company/ school

[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

shoppingsupermarket/shopping mall

[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

eating out

restaurant

[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

exercise/sports

indoor places (e.g., gym)

[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

party

indoor public places(e.g., bar)

[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

culture leisurecinema, concert hall, etc.

[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

see a
doctor/purchase
medicine

hospital, clinic, etc.

[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

leisure/recreation

medicalactivities

Part 3 Social Contacts in Transport
Transport modes

Q19. How often to use each of the following transport modes in your daily life?
[1] Never[2] The first time[3] Less than once a month[4] Once or twice a month[5] Once or twice a week
[6] Daily or almost daily

193

AppendicesTarget periods Before the COVID-19 pandemic: in 2019 (during the
Influenza season)

During the pandemic: from 2020 to present

Taxi; Shared mobility (Uber/DiDi/Grab, etc.)

[1]

[2]

[3]

[4]

[5]

[6]

[1]

[2]

[3]

[4]

[5]

[6]

Public transport (train, metro, street car, bus, etc.)

[1]

[2]

[3]

[4]

[5]

[6]

[1]

[2]

[3]

[4]

[5]

[6]

Flight

[1]

[2]

[3]

[4]

[5]

[6]

[1]

[2]

[3]

[4]

[5]

[6]

Ship / cruise

[1]

[2]

[3]

[4]

[5]

[6]

[1]

[2]

[3]

[4]

[5]

[6]

Transport modes

Q20. How much is your average time to use the following transport modes each time?
[1] less than 5 mins
[2] 5 mins or longer, but less than 15 mins
[3] 15 mins or longer, but less than 30 mins
[4] 30 mins or longer, but less than 1 hour[5] 1 hour or longer, but less than 4 hours[6] 4 hours or longer
Target periods Before the COVID-19 pandemic: in 2019 (during the
Influenza season)

During the pandemic: from 2020 to present

Taxi, Shared mobility (Uber/DiDi/Grab, etc.)

[1]

[2]

[3]

[4]

[5]

[6]

[1]

[2]

[3]

[4]

[5]

[6]

Public transport (train, metro, street car, bus, etc.)

[1]

[2]

[3]

[4]

[5]

[6]

[1]

[2]

[3]

[4]

[5]

[6]

Flight

[1]

[2]

[3]

[4]

[5]

[6]

[1]

[2]

[3]

[4]

[5]

[6]

Ship / cruise

[1]

[2]

[3]

[4]

[5]

[6]

[1]

[2]

[3]

[4]

[5]

[6]

Transport modes

Q21. Who are your main companion when using each of the following transport modes?
[1] spouse/couple[2] children/grandchildren[3] parents/grandparents[4] friends[5] other acquaintances[6] alone
Target periods Before the COVID-19 pandemic: in 2019194

During the pandemic: from 2020 to present

AppendicesTaxi, Shared mobility (Uber/DiDi/Grab, etc.)

[1]

[2]

[3]

[4]

[5]

[6]

[1]

[2]

[3]

[4]

[5]

[6]

Public transport (train, metro, street car, bus, etc.)

[1]

[2]

[3]

[4]

[5]

[6]

[1]

[2]

[3]

[4]

[5]

[6]

Flight

[1]

[2]

[3]

[4]

[5]

[6]

[1]

[2]

[3]

[4]

[5]

[6]

Ship / cruise

[1]

[2]

[3]

[4]

[5]

[6]

[1]

[2]

[3]

[4]

[5]

[6]

Transport modes

Q22. Except for your companion, do you have contacts with other persons when using each of the following
transport modes in your daily life?
[1] No contact[2] Yes: only non-physical contacts (e.g., in the physical presence of another person without face to face conversation,skin-to-skin touching or other indirect physical contacts)[3]Yes: only physical contacts (e.g., having a face to face conversation, skin-to-skin touching or other indirect
physical contacts or other indirect physical contacts)[4] Yes: both non-physical and physical contacts
Target periods Before the COVID-19 pandemic: in 2019 (during the
Influenza season)

During the pandemic: from 2020 to present

Taxi, Shared mobility (Uber/DiDi/Grab, etc.)

[1]

[2]

[3]

[4]

[1]

[2]

[3]

[4]

Public transport (train, metro, street car, bus, etc.)

[1]

[2]

[3]

[4]

[1]

[2]

[3]

[4]

Flight

[1]

[2]

[3]

[4]

[1]

[2]

[3]

[4]

Ship / cruise

[1]

[2]

[3]

[4]

[1]

[2]

[3]

[4]

Transport modes

Q23. Except your companion, how many persons do you contact when using each of the following transportmodes in your daily life?
[1] 2 persons or fewer[2] 3~4 persons[3] 5~6 persons[4] 7~9 persons[5] 10 persons or more
Target periods Before the COVID-19 pandemic: in 2019 (during the
Influenza season)

Taxi, Shared mobility (Uber/DiDi/Grab, etc.)

[1]

[2]

[3]

195

[4]

[5]

During the pandemic: from 2020 to present
[1]

[2]

[3]

[4]

[5]

Appendices

Public transport (train, metro, street car, bus, etc.)

[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

Flight

[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

Ship / cruise

[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

Transport modes

Q24. Do you take the following protective measures when using each of the following transport modes in yourdaily life?
[1] wear a mask
[2] disinfect your hands before/during/after using it[3] distancing with others[4] other measures[5] no measures are taken
Target periods Before the COVID-19 pandemic: in 2019 (during the
Influenza season)

During the pandemic: from 2020 to present

Taxi, Shared mobility (Uber/DiDi/Grab, etc.)

[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

Public transport (train, metro, street car, bus, etc.)

[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

Flight

[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

Ship / cruise

[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

Transport modes

Q25. How do you think about your self-efficiency for protecting yourself when using each of the following
transport modes in your daily life? (here, self-efficiency means your belief for your power of protecting
yourself from infection)[1] very low[2] low[3] neither low or high[4] high[5] very highTarget periods Before the COVID-19 pandemic: in 2019 (during the
Influenza season)

During the pandemic: from 2020 to present

Taxi, Shared mobility (Uber/DiDi/Grab, etc.)

[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

Public transport (train, metro, street car, bus, etc.)

[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

196

Appendices

Flight

[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

Ship / cruise

[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

Transport modes

Q26. Do you think each of the following transport modes involves either of the following three Cs (Closed
spaces with poor ventilation, Crowded places, Close-contact settings)?
[1] Closed spaces with poor ventilation[2] Crowded places[3] Close-contact settings[4] None of the above
Target periods Before the COVID-19 pandemic: in 2019 (during the
Influenza season)

During the pandemic: from 2020 to present

Taxi, Shared mobility (Uber/DiDi/Grab, etc.)

[1]

[2]

[3]

[4]

[1]

[2]

[3]

[4]

Public transport (train, metro, street car, bus, etc.)

[1]

[2]

[3]

[4]

[1]

[2]

[3]

[4]

Flight

[1]

[2]

[3]

[4]

[1]

[2]

[3]

[4]

Ship / cruise

[1]

[2]

[3]

[4]

[1]

[2]

[3]

[4]

Transport modes

Q27. Do you agree that people using each of the following transport modes take sufficient measures forprotecting other persons (including you) in your daily life?
[1] strongly disagree[2] somewhat disagree[3] neither agree nor disagree[4] somewhat agree[5] strongly agree
Target periods Before the COVID-19 pandemic: in 2019 (during the
Influenza season)

During the pandemic: from 2020 to present

Taxi, Shared mobility (Uber/DiDi/Grab, etc.)

[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

Public transport (train, metro, street car, bus, etc.)

[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

Flight

[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

Ship / cruise

[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

197

AppendicesTransport modes

Q28. Do you think you are safe (from infection) when using each of the following transport modes in yourdaily life?
[1] strongly disagree[2] somewhat disagree[3] neither agree nor disagree[4] somewhat agree[5] strongly agree
Target periods Before the COVID-19 pandemic: in 2019 (during the
Influenza season)

During the pandemic: from 2020 to present

Taxi, Shared mobility (Uber/DiDi/Grab, etc.)

[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

Public transport (train, metro, street car, bus, etc.)

[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

Flight

[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

Ship / cruise

[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

Transport modes

Q29. Do you think your household members are safe (from infection) when using each of the following
transport modes in your their daily life?
[1] strongly disagree[2] somewhat disagree[3] neither agree nor disagree[4] somewhat agree[5] strongly agree
Target periods Before the COVID-19 pandemic: in 2019 (during the
Influenza season)

During the pandemic: from 2020 to present

Taxi, Shared mobility (Uber/DiDi/Grab, etc.)

[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

Public transport (train, metro, street car, bus, etc.)

[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

Flight

[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

Ship / cruise

[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

198

AppendicesPart 4 Psychological Factors
Q30. What is your evaluation of reliability for the following
information sources?

strongly
disagreesomewhatdisagree

Neither agree
nor disagree

somewhatagreestrongly
agree

Information (homepage information, news, public relations
magazines, etc.) announced by the central government of yourresidence country

1

2

3

4

5

Information (homepage information, news, public relations
magazines, etc.) announced by the local government of yourresidence city/town

1

2

3

4

5

Domestic news

1

2

3

4

5

Overseas news

1

2

3

4

5

Medical institution

1

2

3

4

5

Workplace / school

1

2

3

4

5

SNS (Social Networking Service): Facebook, LINE, Twitter,
WhatsApp, etc.

1

2

3

4

5

Q31. To what extend do you agree or disagree to each of thefollowing statements related to COVID-19?

strongly
disagreesomewhatdisagree

Neither agree
nor disagree

somewhatagreestrongly
agree

The COVID-19 outbreak in your residence country is severe
than many other countries.

1

2

3

4

5

The infection risk of COVID-19 in your residence country is
high

1

2

3

4

5

The infection risk of COVID-19 in your residence region(prefecture, province, state) is high

1

2

3

4

5

The infection risk of COVID-19 in your residence city/town is
high

1

2

3

4

5

199

Appendices

The infection risk of COVID-19 in the place where you often go(workplaces, schools, supermarkets, restaurants, gyms, etc.) inyour daily life is high

1

2

3

4

5

The infection risk of COVID-19 in crowded train or bus is high

1

2

3

4

5

Q32. There were various triggers causing the changes inyour life during the current COVID-19 pandemic. To what
extent do you think the following things triggered you tomake the correspoding change?

strongly
disagreesomewhatdisagree

Neither agree
nor disagree

somewhatagreestronglyagree

You have changed your behavior because of your own
understanding of the virus.

1

2

3

4

5

You have changed your behavior because of the formal rules
which the government of your residence country has introduced.

1

2

3

4

5

You have changed your behavior because of the advice by yourhousehold.

1

2

3

4

5

You have changed your behavior because of the advice by yourcolleagues, acquaintances and friends.

1

2

3

4

5

You have changed your behavior because of the formal rules
which your workplace/organization/affiliation has introduced.

1

2

3

4

5

You have changed your behavior because of the number of
infected cases.

1

2

3

4

5

You have changed your behavior because of worrying aboutother people's eyes or blames.

1

2

3

4

5

200

AppendicesQ33. To what extend do you agree or disagree to each of thefollowing statements related to COVID-19?

strongly
disagreesomewhatdisagree

Neither agree
nor disagree

somewhatagreestronglyagree

You can trust your residence country's health and medical
institutions in preventing the spread of COVID-19.

1

2

3

4

5

You can trust your residence country's central government inpreventing the spread of COVID-19.

1

2

3

4

5

You can trust your residence city's/town's local government inyour residence place in preventing the spread of COVID-19.

1

2

3

4

5

Behavior trajectories of infected persons should be obtained
from telecommunication companies and published on maps on
the Internet, by following proper privacy protection.

1

2

3

4

5

In order to keep yourself or others from infection, even if you
find it is very inconvenient, you are totally accepting to change
the needs in your life, and your lifestyles.

1

2

3

4

5

In order to keep yourself or others from infection, even if you
find it is very inconvenient, you are totally accepting to change
your daily activities.

1

2

3

4

5

In order to keep yourself or others from infection, even if you
find it is very inconvenient, you are totally accepting to change
places that you perform activities.

1

2

3

4

5

In order to keep yourself or others from infection, even if you
find it is very inconvenient, you are totally accepting to change
time/timing that you perform activities.

1

2

3

4

5

You are a person who well prepare for emergencies like this
COVID-19 pandemic.

1

2

3

4

5

201

Appendices

In order to keep yourself or others from infection, you are theperson who can keep physical distance well, even if you find it isvery inconvenient.

1

2

3

4

5

In order to keep yourself or others from infection, you are totally
accepting to provide your details of behavior trajectories to
medical and government agencies.

1

2

3

4

5

In order to keep yourself or others from infection, you are theperson who can avoid performing activities and making trips
with three Cs (closed spaces, crowded places, and close
contacts), even if you find it is very inconvenient.

1

2

3

4

5

In order to keep yourself or others from infection, you are theperson who can adjust your activity-travel schedules or plans,even if you find it is very inconvenient.

1

2

3

4

5

In order to keep yourself or others from infection, you are theperson who can shift to the telework working style and to use of
travel modes other than public transport, even if you find it isvery inconvenient.

1

2

3

4

5

In order to keep yourself or others from infection, you are totally
accepting to share your behavior trajectories and health status
information with other persons.

1

2

3

4

5

In order to keep yourself or others from infection, you are theperson who can perform more online activities, postpone travel
and tourism, reduce the frequency and distance of trips, and stop
unnecessary and non-urgent activities, even if you find it is very
inconvenient.

1

2

3

4

5

In order to keep yourself or others from infection, you are theperson who can stop activities involving trips and avoid parties
at home, even if you find it is very inconvenient.

1

2

3

4

5

Even if vaccination against COVID-19 is very expensive to you,you are still willing to get vaccinated.

1

2

3

4

5

202

Appendices

You are a person who leaves important decisions to experts orgovernment and support the social order.

1

2

3

4

5

You agree with that everyone is equal and the good of the many
people comes before the good of any individual, the risk decision
should be made by all people instead of a small elite or authority.

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

You agree with that individual freedom is absolutely
important, and individual choices should not be constrained bysociety and other people?
You agree with that there is little you can do to control theenvironment so that you will receive whatever fate throws at you
and try not to know or worry about it..

203

Appendices

Appendix 3: Questionnaire of comparative survey of activity-travel and social contact before-during
COVID-19 in apanese
現在の新型コロナウイルス感染拡大（COVID-19 パンデミック）が人々の生活に様々なIntro影響を与えてきています。このアンケート調査は、COVID-19 パンデミック期間中（2020 年と 2021 年）とパンデミック前（2019 年とそれ以前のインフルエンザ季節）における、あなたの日常生活、ソーシャルコンタクト（社会的接触：人と人との触れあい）、ウイルスに感染されないための保護措置などをお尋ねいたします。また、現在のパンデミックのコントロールに関するあなたの知覚や態度なども教えていただければ幸いです。収集されたデータはすべて統計的に処理し、現在のパンデミックのコントロー
ルと将来のパンデミックの防止に役立つ科学的な研究のみに使います。特定の個人を識別することのできるプライバシー情報をデータに取り入れません。本調査の趣旨にご理解していただき、ぜひ、調査に参加していただければ幸いでございます。

パートＩ

あなた個人と世帯のことについて教えてください。

A1

A2

あなたの性別をお選びください。
1

男性

2

女性

あなたの年齢をお選びください。
1

15 歳未満2

15~19 歳204

AppendicesA3

3

20~29 歳430~39 歳540~49 歳650~59 歳7

60~64 歳8

65~69 歳

970 歳以上

現在の居住地をご入力ください。
都道府県
市区町村
郵便番号

A4

A5

ご自身の職業をお選びください。
1

会社員／会社役員または自営業

2

公務員・団体職員

3

学校や大学などの教育機関の教職員

4

専業主婦

5

パート・アルバイト

6

学生

7

その他の無職（年金生活者を含む）

8

その他

あなたの具体的な職業を教えてください。

205

Appendices

A6

A7

1

農林漁業

2

鉱業、建設業

3

製造業

4

電気・ガス・熱供給・水道業

5

情報通信業

6

運輸業

7

卸売・小売業

8

金融・保険業

9

不動産業

10

飲食店・宿泊業

11

医療・福祉

12

教育・学習支援業

13

その他のサービス業（法務、学術、宗教、複合サービスなど）

14

その他

ご自身を含む世帯人数をご入力ください。
1

ご自身を含む世帯総人数2

11 歳未満の子供の数

3

11-18 歳未満の子供の数

4

65 歳以上の高齢者の数

現在の婚姻状況をお選びください。
1

未婚

2

既婚

206

Appendices

3

A8

A9

A10

離別・死別

同居家族について教えてください。
1

配偶者・カップル

2

自身の子ども

3

孫

4

自身の親・祖父母、配偶者の親・祖父母

5

その他

6

同居していない（一人暮らし）

住居のタイプについて教えてください。
1

持ち家：一戸建て

2

持ち家：マンション等の集合住宅

3

賃貸：一戸建て4

賃貸：マンション等の集合住宅

5

その他

ご自身の最終学歴をお選びください。
1

学校に行ったことはない

2

小学

3

中学

4

高校

5

専修・専門学校・短大・高専

207

Appendices

A11

6

大学

7

大学院博士前期（修士）課程

8

大学院博士後期（博士）課程

あなたの世帯の年収（税込み）は次のどれに当てはまりますか。もし収入がある方が２名以上いる場合は、すべての方の収入を合算してください。
1

３００万円未満

2

３００万円～４００万円未満

3

４００万円～５００万円未満

4

５００万円～６００万円未満

5

６００万円～７００万円未満

6

７００万円～８００万円未満

7

８００万円～９００万円未満

8

９００万円～１０００万円未満

9

１０００万円～１５００万円未満

10

１５００万円～２０００万円未満

11

２０００万円以上

パート II 新型コロナウイルス感染拡大中（COVID-19 パンデミック期間中：2020 年から現在に至るまで）とその以前（2019 年）の活動種類・場所ごとのソーシャル
コンタクト（社会的接触）の状況について教えてください。

活動種類

活動場所

Q1. 当該活動を当該場所で行う頻度を教えてください。
[1] まったくない

208

Appendices[2] 初めて[3] １ヶ月 1 回未満[4] １ヶ月 1 回か 2 回[5] 毎週 1 回か 2 回[6] 毎日かほぼ毎日

対象期間

COVID-19 パンデミック前：2019 年のインフ

COVID-19 パンデミック期間中：2020 年から

ルエンザ流行時期

現在に至るまで

仕事・勉強

職場・学校

[1]

[2]

[3]

[4]

[5]

[6]

[1]

[2]

[3]

[4]

[5]

[6]

買物

スーパー、ショッピングモール

[1]

[2]

[3]

[4]

[5]

[6]

[1]

[2]

[3]

[4]

[5]

[6]

外食

レストラン

[1]

[2]

[3]

[4]

[5]

[6]

[1]

[2]

[3]

[4]

[5]

[6]

運動・スポーツ

室内施設（体育館やジムなど）

[1]

[2]

[3]

[4]

[5]

[6]

[1]

[2]

[3]

[4]

[5]

[6]

ど）

[1]

[2]

[3]

[4]

[5]

[6]

[1]

[2]

[3]

[4]

[5]

[6]

文化的余暇

映画館やコンサートホール

[1]

[2]

[3]

[4]

[5]

[6]

[1]

[2]

[3]

[4]

[5]

[6]

受診、薬の購入

病院・診療所など

[1]

[2]

[3]

[4]

[5]

[6]

[1]

[2]

[3]

[4]

[5]

[6]

活動場所

Q2. 当該活動場所とご自宅との距離はどれぐらいですか。

余暇・娯楽
パーティー

医療活動

活動種類

室内施設（レストランやバーな[1] 1 キロ未満
[2] 1 キロ以上、2 キロ未満
[3] 2 キロ以上、5 キロ未満
[4] 5 キロ以上、10 キロ未満
[5] 10 キロ以上

対象期間

COVID-19 パンデミック前：2019 年のインフ

COVID-19 パンデミック期間中：2020 年から

ルエンザ流行時期

現在に至るまで

209

Appendices

仕事・勉強

職場・学校

[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

買物

スーパー、ショッピングモール

[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

外食

レストラン

[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

運動・スポーツ

室内施設（体育館やジムなど）

[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

ど）

[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

文化的余暇

映画館やコンサートホール

[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

受診、薬の購入

病院・診療所など

[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

余暇・娯楽
パーティー

医療活動

活動種類

室内施設（レストランやバーなQ3. 毎回、当該活動を当該場所で行う時間の長さを教えてください。

活動場所[1] 5 分未満
[2] 5 分以上、15 分未満[3] 15 分以上、1 時間未満
[4] 1 時間以上、4 時間未満
[5] 4 時間以上

対象期間

COVID-19 パンデミック前：2019 年のインフ

COVID-19 パンデミック期間中：2020 年から

ルエンザ流行時期

現在に至るまで

仕事・勉強

職場・学校

[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

買物

スーパー、ショッピングモール

[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

外食

レストラン

[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

運動・スポーツ

室内施設（体育館やジムなど）

[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

ど）

[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

映画館やコンサートホール

[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

余暇・娯楽
パーティー
文化的余暇

室内施設（レストランやバーな

210

Appendices

医療活動

受診、薬の購入

活動種類[1]

病院・診療所など

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

Q4. 当該活動を当該場所で行うための主な移動手段を教えてください。

活動場所[1] Uber や DiDi のサービスを有するタクシー[2] 通常のタクシー[3] バス[4] 軌道系交通：鉄道、メトロ、路面電車、新交通システムなど[5] 自家用車[6] 徒歩・自転車[7] その他

対象期間

COVID-19 パンデミック前：2019 年のインフ

COVID-19 パンデミック期間中：2020 年から

ルエンザ流行時期

現在に至るまで

仕事・勉強

職場・学校

[1]

[2]

[3]

[4]

[5]

[6]

[7]

[1]

[2]

[3]

[4]

[5]

[6]

[7]

買物

スーパー、ショッピングモール

[1]

[2]

[3]

[4]

[5]

[6]

[7]

[1]

[2]

[3]

[4]

[5]

[6]

[7]

外食

レストラン

[1]

[2]

[3]

[4]

[5]

[6]

[7]

[1]

[2]

[3]

[4]

[5]

[6]

[7]

運動・スポーツ

室内施設（体育館やジムなど）

[1]

[2]

[3]

[4]

[5]

[6]

[7]

[1]

[2]

[3]

[4]

[5]

[6]

[7]

ど）

[1]

[2]

[3]

[4]

[5]

[6]

[7]

[1]

[2]

[3]

[4]

[5]

[6]

[7]

文化的余暇

映画館やコンサートホール

[1]

[2]

[3]

[4]

[5]

[6]

[7]

[1]

[2]

[3]

[4]

[5]

[6]

[7]

受診、薬の購入

病院・診療所など

[1]

[2]

[3]

[4]

[5]

[6]

[7]

[1]

[2]

[3]

[4]

[5]

[6]

[7]

余暇・娯楽
パーティー

医療活動

活動種類

室内施設（レストランやバーな活動場所

Q5. 当該活動を当該場所で行うための移動時間（片道）はどれぐらいですか。
[1] 10 分未満[2] 10 分以上、30 分未満

211Appendices[3] 30 分以上、1 時間未満
[4] 1 時間以上

対象期間

COVID-19 パンデミック前：2019 年のインフ

COVID-19 パンデミック期間中：2020 年から

ルエンザ流行時期

現在に至るまで

仕事・勉強

職場・学校

[1]

[2]

[3]

[4]

[1]

[2]

[3]

[4]

買物

スーパー、ショッピングモール

[1]

[2]

[3]

[4]

[1]

[2]

[3]

[4]

外食

レストラン

[1]

[2]

[3]

[4]

[1]

[2]

[3]

[4]

運動・スポーツ

室内施設（体育館やジムなど）

[1]

[2]

[3]

[4]

[1]

[2]

[3]

[4]

ど）

[1]

[2]

[3]

[4]

[1]

[2]

[3]

[4]

文化的余暇

映画館やコンサートホール

[1]

[2]

[3]

[4]

[1]

[2]

[3]

[4]

受診、薬の購入

病院・診療所など

[1]

[2]

[3]

[4]

[1]

[2]

[3]

[4]

余暇・娯楽
パーティー

医療活動

活動種類

室内施設（レストランやバーなQ6. 当該活動を当該場所で行うための主な同伴者を教えてください。

活動場所[1] 配偶者・カップル（職場・学校関係を除外）[2] 子供、孫[3] 両親、祖父母[4] 友人[5] ほかの知人[6] ひとり

対象期間

買物
余暇・娯楽

外食

COVID-19 パンデミック前：2019 年のインフ

COVID-19 パンデミック期間中：2020 年から

ルエンザ流行時期

現在に至るまで

スーパー、ショッピングモール

[1]

[2]

[3]

[4]

[5]

[6]

[1]

[2]

[3]

[4]

[5]

[6]

レストラン

[1]

[2]

[3]

[4]

[5]

[6]

[1]

[2]

[3]

[4]

[5]

[6]

212

Appendices

[1]

[2]

[3]

[4]

[5]

[6]

[1]

[2]

[3]

[4]

[5]

[6]

ど）

[1]

[2]

[3]

[4]

[5]

[6]

[1]

[2]

[3]

[4]

[5]

[6]

文化的余暇

映画館やコンサートホール

[1]

[2]

[3]

[4]

[5]

[6]

[1]

[2]

[3]

[4]

[5]

[6]

受診、薬の購入

病院・診療所など

[1]

[2]

[3]

[4]

[5]

[6]

[1]

[2]

[3]

[4]

[5]

[6]

活動場所

Q7. 主な同伴者以外に、当該活動を当該場所で行う際に何らかのソーシャルコンタクトはあり

運動・スポーツ
パーティー

医療活動

活動種類

室内施設（体育館やジムなど）
室内施設（レストランやバーなますか。
[1] いいえ、まったくない[2] はい：非物理的な接触のみ（例：他の人が物理的に存在する場合、対面会話や身体に触れるような物理的接触がない）[3] はい：物理的な接触のみ（例：対面会話や身体に触れるような物理的接触とその他の間接的な物理接触がある）[4] はい：非物理的な接触と物理的な接触の両方

対象期間

COVID-19 パンデミック前：2019 年のインフ

COVID-19 パンデミック期間中：2020 年から

ルエンザ流行時期

現在に至るまで

職場・学校（あなたの同僚・クラ
[1]

[2]

[3]

[4]

[1]

[2]

[3]

[4]

スーパー、ショッピングモール

[1]

[2]

[3]

[4]

[1]

[2]

[3]

[4]

外食

レストラン

[1]

[2]

[3]

[4]

[1]

[2]

[3]

[4]

運動・スポーツ

室内施設（体育館やジムなど）

[1]

[2]

[3]

[4]

[1]

[2]

[3]

[4]

[1]

[2]

[3]

[4]

[1]

[2]

[3]

[4]

仕事・勉強

スメートもこの場所でのソーシャ
ルコンタクトに含まれる）

買物

余暇・娯楽
パーティー

室内施設（レストランやバーなど）213

Appendices

医療活動

文化的余暇

映画館やコンサートホール

[1]

[2]

[3]

[4]

[1]

[2]

[3]

[4]

受診、薬の購入

病院・診療所など

[1]

[2]

[3]

[4]

[1]

[2]

[3]

[4]

活動種類

Q8. 主な同伴者以外で、当該活動を当該場所で行う際に接触する人数を教えてください。

活動場所[1] 2 人以下
[2] 3～4 人[3] 5～6 人[4] 7～9 人[5] 10 人以上

対象期間

COVID-19 パンデミック前：2019 年のインフ

COVID-19 パンデミック期間中：2020 年から

ルエンザ流行時期

現在に至るまで

職場・学校（あなたの同僚・クラ
仕事・勉強

スメートもこの場所でのソーシャ
ルコンタクトに含まれる）

[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

スーパー、ショッピングモール

[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

外食

レストラン

[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

運動・スポーツ

室内施設（体育館やジムなど）

[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

ど）

[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

文化的余暇

映画館やコンサートホール

[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

受診、薬の購入

病院・診療所など

[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

買物

余暇・娯楽
パーティー

医療活動

活動種類

室内施設（レストランやバーな活動場所

Q9. 当該活動を当該場所で行う際の保護措置を教えてください。
[1] マスク着用

214

Appendices[2] 活動前後または活動中に手の消毒を行う[3] 人と適切な距離を取る[4] ほかの保護措置[5] 保護措置をまったく取らない

対象期間

COVID-19 パンデミック前：2019 年のインフ

COVID-19 パンデミック期間中：2020 年から

ルエンザ流行時期

現在に至るまで

仕事・勉強

職場・学校

[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

買物

スーパー、ショッピングモール

[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

外食

レストラン

[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

運動・スポーツ

室内施設（体育館やジムなど）

[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

ど）

[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

文化的余暇

映画館やコンサートホール

[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

受診、薬の購入

病院・診療所など

[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

余暇・娯楽
パーティー

医療活動

活動種類

室内施設（レストランやバーなQ10. 当該活動を当該場所で行う際の感染保護の自己効力感（自己保護能力の意識・認識）に

活動場所

ついて教えてください。
[1] 非常に低い[2] 低い[3] どちらとも言えない[4] 高い[5] 非常に高い

対象期間

COVID-19 パンデミック前：2019 年のインフ

COVID-19 パンデミック期間中：2020 年から

ルエンザ流行時期

現在に至るまで

215

Appendices

仕事・勉強

職場・学校

[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

買物

スーパー、ショッピングモール

[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

外食

レストラン

[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

運動・スポーツ

室内施設（体育館やジムなど）

[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

ど）

[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

文化的余暇

映画館やコンサートホール

[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

受診、薬の購入

病院・診療所など

[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

余暇・娯楽
パーティー

医療活動

活動種類

室内施設（レストランやバーなQ11. 当該活動場所においてほかの人々（例：スタッフ、他の消費者）が他人（ご自身を含活動場所む）を保護するために適切な措置を取ったと思いますか。
[1] 強くそう思わない[2] あまりそう思わない[3] どちらとも言えない[4] まあまあそう思う[5] 強くそう思う

対象期間

COVID-19 パンデミック前：2019 年のインフ

COVID-19 パンデミック期間中：2020 年から

ルエンザ流行時期

現在に至るまで

仕事・勉強

職場・学校

[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

買物

スーパー、ショッピングモール

[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

外食

レストラン

[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

運動・スポーツ

室内施設（体育館やジムなど）

[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

余暇・娯楽
パーティー

室内施設（レストランやバーなど）216

Appendices

医療活動

文化的余暇

映画館やコンサートホール

[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

受診、薬の購入

病院・診療所など

[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

活動種類

Q12. 当該活動を当該場所で行う際にその状況は以下の 3 密のいずれかに当たると思います

活動場所

か。
[1] 密閉（空間が密閉し、換気が悪い）[2] 密集（多くの人で混んでいる）[3] 密接（接触距離が近すぎる）[4] 左記の３密のいずれかにも当たらない

対象期間

COVID-19 パンデミック前：2019 年のインフ

COVID-19 パンデミック期間中：2020 年から

ルエンザ流行時期

現在に至るまで

仕事・勉強

職場・学校

[1]

[2]

[3]

[4]

[1]

[2]

[3]

[4]

買物

スーパー、ショッピングモール

[1]

[2]

[3]

[4]

[1]

[2]

[3]

[4]

外食

レストラン

[1]

[2]

[3]

[4]

[1]

[2]

[3]

[4]

運動・スポーツ

室内施設（体育館やジムなど）

[1]

[2]

[3]

[4]

[1]

[2]

[3]

[4]

ど）

[1]

[2]

[3]

[4]

[1]

[2]

[3]

[4]

文化的余暇

映画館やコンサートホール

[1]

[2]

[3]

[4]

[1]

[2]

[3]

[4]

受診、薬の購入

病院・診療所など

[1]

[2]

[3]

[4]

[1]

[2]

[3]

[4]

余暇・娯楽
パーティー

医療活動

活動種類

室内施設（レストランやバーな活動場所

Q13. 感染リスクを考えた際に、当該活動を当該場所で行うことは安全だと思いますか。
[1] 強くそう思わない[2] あまりそう思わない[3] どちらとも言えない217

Appendices[4] まあまあそう思う[5] 強くそう思う

対象期間

COVID-19 パンデミック前：2019 年のインフ

COVID-19 パンデミック期間中：2020 年から

ルエンザ流行時期

現在に至るまで

仕事・勉強

職場・学校

[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

買物

スーパー、ショッピングモール

[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

外食

レストラン

[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

運動・スポーツ

室内施設（体育館やジムなど）

[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

ど）

[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

文化的余暇

映画館やコンサートホール

[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

受診、薬の購入

病院・診療所など

[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

余暇・娯楽
パーティー

医療活動

活動種類

室内施設（レストランやバーなQ14. もしあなたの家族が当該活動を当該場所で行う場合、感染リスクを考えた際に安全だと活動場所思いますか。
[1] 強くそう思わない[2] あまりそう思わない[3] どちらとも言えない[4] まあまあそう思う[5] 強くそう思う

対象期間

COVID-19 パンデミック前：2019 年のインフ

COVID-19 パンデミック期間中：2020 年から

ルエンザ流行時期

現在に至るまで

仕事・勉強

職場・学校

[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

買物

スーパー、ショッピングモール

[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

218

Appendices

外食

レストラン

[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

運動・スポーツ

室内施設（体育館やジムなど）

[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

ど）

[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

文化的余暇

映画館やコンサートホール

[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

受診、薬の購入

病院・診療所など

[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

余暇・娯楽
パーティー

医療活動

活動種類

室内施設（レストランやバーなQ15. 当該活動を当該場所で行う際のあなたの心理的状態について教えてください。

活動場所[1] 疲れ果てた[2] うつ状態、落胆した[3] 神経的[4] エネルギッシュで元気[5] プレッシャーを感じた[6] 幸せ、満足的[7] 無力[8] 気分がさわやか、くつろぎを感じた[9] 耐えらない[10] 不安[11] その他

対象期間

COVID-19 パンデミック前：2019 年のインフ

COVID-19 パンデミック期間中：2020 年から

ルエンザ流行時期

現在に至るまで

仕事・勉強

職場・学校

[1]

[2]

[3]

[4]

[5]

[6]

[7]

[8]

[9]

[10] [11]

[1]

[2]

[3]

[4]

[5]

[6]

[7]

[8]

[9]

[10] [11]買物

スーパー、ショッピングモール

[1]

[2]

[3]

[4]

[5]

[6]

[7]

[8]

[9]

[10] [11]

[1]

[2]

[3]

[4]

[5]

[6]

[7]

[8]

[9]

[10] [11]

レストラン

[1]

[2]

[3]

[4]

[5]

[6]

[7]

[8]

[9]

[10] [11]

[1]

[2]

[3]

[4]

[5]

[6]

[7]

[8]

[9]

[10] [11]

余暇・娯楽

外食

219

Appendices

運動・スポーツ

[1]

[2]

[3]

[4]

[5]

[6]

[7]

[8]

[9]

[10] [11]

[1]

[2]

[3]

[4]

[5]

[6]

[7]

[8]

[9]

[10] [11]

ど）

[1]

[2]

[3]

[4]

[5]

[6]

[7]

[8]

[9]

[10] [11]

[1]

[2]

[3]

[4]

[5]

[6]

[7]

[8]

[9]

[10] [11]文化的余暇

映画館やコンサートホール

[1]

[2]

[3]

[4]

[5]

[6]

[7]

[8]

[9]

[10] [11]

[1]

[2]

[3]

[4]

[5]

[6]

[7]

[8]

[9]

[10] [11]

受診、薬の購入

病院・診療所など

[1]

[2]

[3]

[4]

[5]

[6]

[7]

[8]

[9]

[10] [11]

[1]

[2]

[3]

[4]

[5]

[6]

[7]

[8]

[9]

[10] [11]活動場所

Q16. 当該活動を当該場所で行う良さ（メリット）・目的を教えてください。

パーティー

医療活動

活動種類

室内施設（体育館やジムなど）
室内施設（レストランやバーな[1] 他者との温かい関係[2] 生活の利便性[3] 食料の供給と日常生活[4] プレッシャーを和らげる[5] 私の公的イメージ、社会的認知、他人からの尊敬を保つ[6] 自己安心感[7] お金を節約する[8] お金を稼ぐ[9] 知恵[10] 身体的健康[11] 達成感[12] 自由の感覚[13] 自己実現/自尊心[14] 帰属意識[15] 楽しさ－うれしさ－刺激[16] くつろぎ[17] その他

220

Appendices

対象期間

COVID-19 パンデミック前：2019 年のインフ

COVID-19 パンデミック期間中：2020 年から

ルエンザ流行時期

現在に至るまで

仕事・勉強

職場・学校

[1

[2

[3

[4

[5

[…...

[15

[16

[17

[1

[2

[3

[4

[5

[…...

[15

[16

[17

買物

スーパー、ショッピングモール

[1]

[2]

[3]

[4]

[5]

[…...

[15

[16

[17

[1

[2

[3

[4

[5

[…...

[15

[16

[17

外食

レストラン

[1

[2

[3

[4

[5

[…...

[15

[16

[17

[1

[2

[3

[4

[5

[…...

[15

[16

[17

運動・スポーツ

室内施設（体育館やジムなど）

[1]

[2]

[3

[4

[5

[…...

[15

[16

[17

[1

[2

[3

[4

[5

[…...

[15

[16

[17

ど）

[1

[2

[3

[4

[5

[…...

[15

[16

[17

[1

[2

[3

[4

[5

[…...

[15

[16

[17

文化的余暇

映画館やコンサートホール

[1

[2

[3

[4

[5

[…...

[15

[16

[17

[1

[2

[3

[4

[5

[…...

[15

[16

[17

受診、薬の購入

病院・診療所など

[1

[2

[3

[4

[5

[…...

[15

[16

[17

[1]

[2]

[3]

[4]

[5]

[…...

[15

[16

[17

活動場所

Q17. 政府の許可なしでこの活動をこのような場所で行う人々を罰するべきだと思いますか

余暇・娯楽
パーティー

医療活動

活動種類

室内施設（レストランやバーな[1] 強くそう思わない[2] あまりそう思わない[3] どちらとも言えない[4] まあまあそう思う[5] 強くそう思う

対象期間

COVID-19 パンデミック前：2019 年のインフ

COVID-19 パンデミック期間中：2020 年から

ルエンザ流行時期

現在に至るまで

仕事・勉強

職場・学校

[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

買物

スーパー、ショッピングモール

[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

外食

レストラン

[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

運動・スポーツ

室内施設（体育館やジムなど）

[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

余暇・娯楽

221

Appendices

パーティー

医療活動

室内施設（レストランやバーな
ど）

[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

文化的余暇

映画館やコンサートホール

[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

受診、薬の購入

病院・診療所など

[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

活動種類

Q18. たとえ感染リスクが高くても、あなたは当該活動を当該場所で行うと思いますか。

活動場所[1] 強くそう思わない[2] あまりそう思わない[3] どちらとも言えない[4] まあまあそう思う[5] 強くそう思う

対象期間

COVID-19 パンデミック前：2019 年のインフ

COVID-19 パンデミック期間中：2020 年から

ルエンザ流行時期

現在に至るまで

仕事・勉強

職場・学校

[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

買物

スーパー、ショッピングモール

[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

外食

レストラン

[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

運動・スポーツ

室内施設（体育館やジムなど）

[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

ど）

[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

文化的余暇

映画館やコンサートホール

[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

受診、薬の購入

病院・診療所など

[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

余暇・娯楽
パーティー

医療活動

室内施設（レストランやバーな222

Appendices

パート III 新型コロナウイルス感染拡大中（COVID-19 パンデミック期間中：2020 年から現在に至る）とその以前（2019 年）の移動手段を使う際のソーシャルコンタ
クト（社会的接触）の状況について教えてください。

Q19. 普段の当該移動手段の利用頻度はどれぐらいですか。

移動手段[1] まったくない[2] 初めて[3] １ヶ月 1 回未満[4] １ヶ月 1 回か 2 回[5] 毎週 1 回か 2 回[6] 毎日かほぼ毎日

対象期間

COVID-19 パンデミック前：2019 年のインフルエンザ流行時

COVID-19 パンデミック期間中：2020 年から現在に至るまで

期
[1]

[2]

[3]

[4]

[5]

[6]

[1]

[2]

[3]

[4]

[5]

[6]

[1]

[2]

[3]

[4]

[5]

[6]

[1]

[2]

[3]

[4]

[5]

[6]

飛行機

[1]

[2]

[3]

[4]

[5]

[6]

[1]

[2]

[3]

[4]

[5]

[6]

船・クルーズ船

[1]

[2]

[3]

[4]

[5]

[6]

[1]

[2]

[3]

[4]

[5]

[6]

タクシー
公共交通手段：鉄道、メトロ、路面電
車、新交通システム、バスなど

移動手段

Q20. 毎回、当該移動手段を使うときの平均移動時間（片道）はどれぐらいですか。
[1] 5 分未満
[2] 5 分以上、15 分未満[3] 15 分以上、30 分未満[4] 30 分以上、1 時間未満223

Appendices[5] 1 時間以上、4 時間未満
[6] 4 時間以上

対象期間

COVID-19 パンデミック前：2019 年のインフルエンザ流行時

COVID-19 パンデミック期間中：2020 年から現在に至るまで

期
[1]

[2]

[3]

[4]

[5]

[6]

[1]

[2]

[3]

[4]

[5]

[6]

車、新交通システム、バスなど

[1]

[2]

[3]

[4]

[5]

[6]

[1]

[2]

[3]

[4]

[5]

[6]

飛行機

[1]

[2]

[3]

[4]

[5]

[6]

[1]

[2]

[3]

[4]

[5]

[6]

船・クルーズ船

[1]

[2]

[3]

[4]

[5]

[6]

[1]

[2]

[3]

[4]

[5]

[6]

タクシー
公共交通手段：鉄道、メトロ、路面電

Q21. 当該移動手段を使うときの主な同伴者を教えてください。

移動手段[1] 配偶者・カップル[2] 子供、孫[3] 両親、祖父母[4] 友人[5] ほかの知人[6] ご自身のみ

対象期間

COVID-19 パンデミック前：2019 年のインフルエンザ流行時

COVID-19 パンデミック期間中：2020 年から現在に至るまで

期
[1]

[2]

[3]

[4]

[5]

[6]

[1]

[2]

[3]

[4]

[5]

[6]

車、新交通システム、バスなど

[1]

[2]

[3]

[4]

[5]

[6]

[1]

[2]

[3]

[4]

[5]

[6]

飛行機

[1]

[2]

[3]

[4]

[5]

[6]

[1]

[2]

[3]

[4]

[5]

[6]

船・クルーズ船

[1]

[2]

[3]

[4]

[5]

[6]

[1]

[2]

[3]

[4]

[5]

[6]

タクシー
公共交通手段：鉄道、メトロ、路面電

224

AppendicesQ22. 主な同伴者以外に、当該移動手段を使う際に何らかのソーシャルコンタクトはありますか。

移動手段[1] いいえ、まったくない[2] はい：非物理的な接触のみ（例：他の人が物理的に存在する場合、対面会話や身体に触れるような物理的接触がない）[3] はい：物理的な接触のみ（例：対面会話や身体に触れるような物理的接触とその他の間接的な物理接触がある）[4] はい：非物理的な接触と物理的な接触の両方

対象期間

COVID-19 パンデミック前：2019 年のインフルエンザ流行時

COVID-19 パンデミック期間中：2020 年から現在に至るまで

期
[1]

[2]

[3]

[4]

[1]

[2]

[3]

[4]

車、新交通システム、バスなど

[1]

[2]

[3]

[4]

[1]

[2]

[3]

[4]

飛行機

[1]

[2]

[3]

[4]

[1]

[2]

[3]

[4]

船・クルーズ船

[1]

[2]

[3]

[4]

[1]

[2]

[3]

[4]

タクシー
公共交通手段：鉄道、メトロ、路面電

Q23. 主な同伴者以外、当該移動手段を使う際に接触する人数を教えてください。

移動手段[1] 2 人以下
[2] 3～4 人[3] 5～6 人[4] 7～9 人[5] 10 人以上

対象期間

COVID-19 パンデミック前：2019 年のインフルエンザ流行時

COVID-19 パンデミック期間中：2020 年から現在に至るまで

期
タクシー

[1]

[2]

[3]

[4]

225

[5]

[1]

[2]

[3]

[4]

[5]

Appendices

公共交通手段：鉄道、メトロ、路面電
車、新交通システム、バスなど

[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

飛行機

[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

船・クルーズ船

[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

Q24. 当該移動手段を使う際の保護措置を教えてください。

移動手段[1] マスク着用[2] 移動前後または移動中に手の消毒を行う[3] 人と適切な距離を取る[4] ほかの保護措置[5] 保護措置をまったく取らない

対象期間

COVID-19 パンデミック前：2019 年のインフルエンザ流行時

COVID-19 パンデミック期間中：2020 年から現在に至るまで

期
[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

車、新交通システム、バスなど

[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

飛行機

[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

船・クルーズ船

[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

タクシー
公共交通手段：鉄道、メトロ、路面電

移動手段

Q25. 当該移動手段を使う際の感染保護の自己効力感（自己保護能力の意識・認識）について教えてください。
[1] 非常に低い[2] 低い[3] どちらとも言えない

226

Appendices[4] 高い[5] 非常に高い

対象期間

COVID-19 パンデミック前：2019 年のインフルエンザ流行時

COVID-19 パンデミック期間中：2020 年から現在に至るまで

期
[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

車、新交通システム、バスなど

[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

飛行機

[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

船・クルーズ船

[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

タクシー
公共交通手段：鉄道、メトロ、路面電

Q26. 当該移動手段を使う際にその状況は以下の 3 密のいずれかに当たると思いますか 。

移動手段[1] 密閉（空間が密閉し、換気が悪い）[2] 密集（多くの人で混んでいる）[3] 密接（接触距離が近すぎる）[4] 左記の３密のいずれかにも当たらない

対象期間

COVID-19 パンデミック前：2019 年のインフルエンザ流行時

COVID-19 パンデミック期間中：2020 年から現在に至るまで

期
[1]

[2]

[3]

[4]

[1]

[2]

[3]

[4]

車、新交通システム、バスなど

[1]

[2]

[3]

[4]

[1]

[2]

[3]

[4]

飛行機

[1]

[2]

[3]

[4]

[1]

[2]

[3]

[4]

船・クルーズ船

[1]

[2]

[3]

[4]

[1]

[2]

[3]

[4]

タクシー
公共交通手段：鉄道、メトロ、路面電

227

AppendicesQ27. 当該移動手段を使うほかの人々が他人（ご自身を含む）を保護するために適切な措置を取ったと思いますか。

移動手段[1] 強くそう思わない[2] あまりそう思わない[3] どちらとも言えない[4] まあまあそう思う[5] 強くそう思う

対象期間

COVID-19 パンデミック前：2019 年のインフルエンザ流行時

COVID-19 パンデミック期間中：2020 年から現在に至るまで

期
[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

車、新交通システム、バスなど

[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

飛行機

[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

船・クルーズ船

[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

タクシー
公共交通手段：鉄道、メトロ、路面電

Q28. 感染リスクを考えた際に、当該移動手段を使うことは安全だと思いますか。

移動手段[1] 強くそう思わない[2] あまりそう思わない[3] どちらとも言えない[4] まあまあそう思う[5] 強くそう思う

対象期間

COVID-19 パンデミック前：2019 年のインフルエンザ流行時

COVID-19 パンデミック期間中：2020 年から現在に至るまで

期
タクシー

[1]

[2]

[3]

[4]

228

[5]

[1]

[2]

[3]

[4]

[5]

Appendices

公共交通手段：鉄道、メトロ、路面電
車、新交通システム、バスなど

[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

飛行機

[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

船・クルーズ船

[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

Q29. もしあなたの家族が当該移動手段を使う場合、感染リスクを考えた際に安全だと思いますか。

移動手段[1] 強くそう思わない[2] あまりそう思わない[3] どちらとも言えない[4] まあまあそう思う[5] 強くそう思う

対象期間

COVID-19 パンデミック前：2019 年のインフルエンザ流行時

COVID-19 パンデミック期間中：2020 年から現在に至るまで

期
[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

車、新交通システム、バスなど

[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

飛行機

[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

船・クルーズ船

[1]

[2]

[3]

[4]

[5]

[1]

[2]

[3]

[4]

[5]

タクシー
公共交通手段：鉄道、メトロ、路面電

パートⅣ 心理的ファクター
Q30. 新型コロナウイルスに関する情報源の「信頼性」について、あなたご自身の評価をお答えくだ
さい。

強くそう

あまりそう

どちらと

まあまあ

強くそう

思わない

思わない

も言えな

そう思う

思う

い

229

Appendices

日本政府の発表情報（ホームページ情報、ニュース、広報誌など）

1

2

3

4

5

お住まいの地方自治体の発表情報（ホームページ情報、ニュース、広報誌など）

1

2

3

4

5

国内ニュース

1

2

3

4

5

海外ニュース

1

2

3

4

5

医療機関

1

2

3

4

5

職場・学校

1

2

3

4

5

SNS (ソーシャルネットワーキングサービス): Facebook, LINE, Twitter, WhatsApp など

1

2

3

4

5

強くそう

あまりそう

どちらと

まあまあ

強くそう

思わない

思わない

も言えな

そう思う

思う

Q31. 新型コロナウイルスの感染リスクについて、あなたはどのように感じていますか。

い日本全体の感染リスクがほかの国より高いと思っていますか

1

2

3

4

5

日本全体の感染リスクが高まっていると思っていますか

1

2

3

4

5

お住まいの都道府県での感染リスクが高まっていると思っていますか

1

2

3

4

5

お住まいの市町村での感染リスクが高まっていると思っていますか

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

あなたがよく行く場所（職場や学校、スーパー、レストラン、スポーツジムなど）での感染リスクが高まっていると思っていますか
混んでいる電車やバスの感染リスクが高いと思いますか230

AppendicesQ32. 新型コロナウイルス感染拡大によって、あなたご自身の生活行動に様々な変化が生じていま

強くそう

あまりそう

どちらと

まあまあ

強くそう

す。その変化は色々なキッカケによって引き起こされたと考えられますが、以下のような事柄は、ど

思わない

思わない

も言えな

そう思う

思う

い

の程度そのきっかけになったと思いますか。
自分の感染状況に対する理解によって、自分の行動を変えた。

1

2

3

4

5

日本政府の要請によって、自分の行動を変えた。

1

2

3

4

5

家族からの勧めによって、自分の行動を変えた。

1

2

3

4

5

同僚、知人や友人などからの勧めによって、自分の行動を変えた。

1

2

3

4

5

所属団体からの要請によって、自分の行動を変えた。

1

2

3

4

5

感染者数をみて、自分の行動を変えた。

1

2

3

4

5

周りの目や批判を気にして、自分の行動を変えた。

1

2

3

4

5

強くそう

あまりそう

どちらと

まあまあ

強くそう

思わない

思わない

も言えな

そう思う

思う

Q33. 以下の新型コロナウイルス感染拡大に関するそれぞれの事柄について、どう思うかについてお答えください。

い
感染拡大を防ぐにあたり、日本の保健・医療機関を信頼できる。

1

2

3

4

5

新型コロナウイルスの感染拡大を防ぐにあたり、日本政府を信頼できる。

1

2

3

4

5

新型コロナウイルスの感染拡大を防ぐにあたり、ご自身が居住している地方自治体を信頼できる。

1

2

3

4

5

231

Appendices

感染者の行動履歴を通信会社などから入手し、個人情報を保護の上、ホームページの地図上に公開すべき。
感染しない、または他人に感染させないようにするために、非常に不便だと感じても、自分の生活ニーズ、ライフタイルを見直すことにまったく抵抗がないほうだ。
感染しない、または他人に感染させないようにするために、非常に不便だと感じても、毎日の活動内
容を見直すことにまったく抵抗がないほうだ。
感染しない、または他人に感染させないようにするために、非常に不便だと感じても、毎日の活動場
所を見直すことにまったく抵抗がないほうだ。
感染しない、または他人に感染させないようにするために、非常に不便だと感じても、毎日の活動時
間（長さ、タイミング）を見直すことにまったく抵抗がないほうだ。
私はパンデミックのような非常事態によく備えるほうだ。
感染しない、または他人に感染させないようにするために、非常に不便だと感じても、フィジカルデ
ィスタンシングをよく実践しているほうだ。
感染しない、または他人に感染させないようにするために、ご自身の行動履歴の詳細を医療機関・政府機関に提供することにまったく抵抗がないほうだ。
感染しない、または他人に感染させないようにするために、非常に不便だと感じても、３密（密集、密接、密閉）のある活動・移動を常に避けるようにするほうだ。
感染しない、または他人に感染させないようにするために、非常に不便だと感じても、ご自身の活動・行動スケジュールや計画を常に調整するほうだ。
感染しない、または他人に感染させないようにするために、非常に不便だと感じても、仕事の勤務形
態をテレワークにシフトしたり、公共交通以外の交通手段の利用にシフトしたりするほうだ。

232

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

Appendices

感染しない、または他人に感染させないようにするために、ご自身の行動履歴、健康状況の情報を他

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

たとえ新型コロナウイルスのワクチンの接種費用が高くても、私は接種したい。

1

2

3

4

5

重要な意思決定を専門家や政府に任せて、そのような社会的な秩序を支持するほうだ。

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

人に共用することにまったく抵抗がないほうだ。
感染しない、または他人に感染させないようにするために、非常に不便だと感じても、オンライン活動への切り換え、旅行などの延期、移動回数や距離を減らしたり、不要不急の活動を止めたりするほうだ。
感染しない、または他人に感染させないようにするために、非常に不便だと感じても、移動を伴う活動を中止し、家でパーティーしないようにするほうだ。

だれもが平等で、多数の人々の利益がどの個人の利益よりも優先されるべき、そして、重大なリスクに関する意思決定は少数のエリートや権威ではなく、すべての人々で行うべきだと思う。
個人の自由が絶対に重要であり、個人の選択が社会や他の人々によって制約されるべきではないと思う。
自分自身が周りの環境を左右することができないので、運命に身を任せ、その運命を知ろうとしな
い、心配しないように努めるほうだ。

233

Appendices

Appendix : Statistics of social contacts for Work/Study, Shopping, Eating out, Physical exercise setting
before and during the COVID-19 pandemic
Work/Study
Pre-pandemic
Variable

Sample(%)

Total

3606 (1)Mean(95%CI)
8.85 (8.679.03)

Shopping

During-pandemic
Sample(%)
2838 (1)Mean(95%CI)
7.42 (7.227.62)

Pre-pandemic
Sample(%)
5236 (1)Mean(95%CI)
4.98 (4.855.11)

Eating out
During-pandemic

Sample(%)
4512 (1)Mean(95%CI)
4.5 (4.364.64)

Pre-pandemic
Sample(%)
5318 (1)Mean(95%CI)
4.85 (4.744.96)

Physical exercise

During-pandemic
Sample(%)
3355 (1)Mean(95%CI)
4.26 (4.134.39)

Pre-pandemic
Sample(%)
3427 (1)Mean(95%CI)
5.8 (5.635.97)

During-pandemic
Sample(%)
2254 (1)Mean(95%CI)
4.83 (4.665)

Age

30~39

111
(0.031)943(0.262)787(0.218)40~49758 (0.21)

50~59

65~69

599
(0.166)174(0.048)126(0.035)70 and above

108 (0.03)

Less than 19
20~29

60~64

9.04 (810.08)
7.91 (7.578.25)
8.66 (8.279.05)
8.99 (8.589.4)
10.24 (9.810.68)
9.83 (8.9610.7)
9.6 (8.5910.61)
7.12 (6.018.23)

77 (0.027)
765 (0.27)
651(0.229)579(0.204)454 (0.16)
136(0.048)88 (0.031)
88 (0.031)

7.73 (6.449.02)
6.41 (6.066.76)
7.13 (6.727.54)
7.53 (7.087.98)
8.87 (8.349.4)
9.32 (8.2910.35)
8.27 (79.54)
6.21 (5.067.36)

118(0.023)1056(0.202)938(0.179)912(0.174)853(0.163)343(0.066)379(0.072)637(0.122)5.36 (4.486.24)
5.18 (4.95.46)
5.14 (4.835.45)
5.3 (4.975.63)
5.4 (5.035.77)
4.77 (4.235.31)
4.34 (3.864.82)
3.83 (3.54.16)

944(0.209)830(0.184)790(0.175)709(0.157)276(0.061)328(0.073)547(0.121)4.93 (4.025.84)
4.64 (4.364.92)
4.61 (4.34.92)
4.57 (4.254.89)
4.92 (4.535.31)
4.59 (3.985.2)
3.93 (3.454.41)
3.66 (3.34.02)

117(0.022)1071(0.201)954(0.179)908(0.171)855(0.161)349(0.066)389(0.073)675(0.127)5.65 (4.836.47)
5.16 (4.915.41)
5 (4.735.27)
4.94 (4.655.23)
5.12 (4.815.43)
4.46 (4.034.89)
4.43 (4.034.83)
4.03 (3.754.31)

88 (0.02)797(0.238)668(0.199)583(0.174)501(0.149)162(0.048)197(0.059)372(0.111)

5.29 (4.336.25)
4.43 (4.174.69)
4.43 (4.144.72)
4.34 (4.024.66)
4.32 (3.944.7)
3.82 (3.234.41)
3.87 (3.334.41)
3.59 (3.223.96)

100(0.029)875(0.255)739(0.216)619(0.181)473(0.138)149(0.043)180(0.053)292(0.085)6.01 (5.027)
5.42 (5.135.71)
5.52 (5.195.85)
5.81 (5.416.21)
6.24 (5.746.74)
5.97 (5.086.86)
6.31 (5.57.12)
6.45 (5.837.07)

75 (0.022)

102(0.045)172(0.076)

5.69 (4.396.99)
4.53 (4.264.8)
4.74 (4.415.07)
4.66 (4.275.05)
5.14 (4.555.73)
5.11 (3.886.34)
4.95 (45.9)
5.78 (4.966.6)

55 (0.024)
664(0.295)528(0.234)398(0.177)264(0.117)71 (0.031)

GenderMale
Female1889(0.524)1717(0.476)

8.46 (8.218.71)
9.28 (9.019.55)

1542(0.543)1296(0.457)7.13 (6.867.4)
7.77 (7.468.08)

2593(0.495)2643(0.505)4.9 (4.725.08)
5.06 (4.875.25)

2287(0.507)2225(0.493)

4.58 (4.394.77)
4.41 (4.214.61)

2629(0.494)2689(0.506)

4.81 (4.654.97)
4.9 (4.735.07)

1745(0.52)
1610(0.48)

4.37 (4.194.55)
4.14 (3.954.33)

1808(0.528)1619(0.472)

5.6 (5.385.82)
6.02 (5.776.27)

1280(0.568)974(0.432)4.75 (4.534.97)
4.95 (4.675.23)

535(0.148)651(0.181)619(0.172)563(0.156)647(0.179)591(0.164)8.29 (7.828.76)
7.92 (7.58.34)
9.44 (99.88)
8.74 (8.269.22)
9.05 (8.629.48)
9.65 (9.1910.11)446(0.157)489(0.172)553(0.195)360(0.127)445(0.157)545(0.192)

7.21 (6.717.71)
6.23 (5.786.68)
8.63 (8.169.1)
6.86 (6.37.42)
6.52 (6.037.01)
8.55 (8.079.03)

795(0.152)1009(0.193)908(0.173)717(0.159)851(0.189)823(0.182)

4.46 (4.174.75)
4.46 (4.154.77)
4.68 (4.394.97)854(0.161)895(0.168)750(0.141)4.87 (4.595.15)
4.91 (4.655.17)
5.1 (4.825.38)
4.76 (4.475.05)
4.62 (4.364.88)
4.82 (4.495.15)

631(0.188)673(0.201)762(0.227)394(0.117)680(0.151)808(0.179)4.73 (4.395.07)
4.35 (4.064.64)
5 (4.665.34)
4.28 (3.924.64)
3.82 (3.514.13)
4.68 (4.335.03)

835(0.157)1028(0.193)880(0.168)804(0.154)5.02 (4.685.36)
4.85 (4.575.13)
5.28 (4.955.61)
5.11 (4.775.45)
4.59 (4.284.9)
5.07 (4.715.43)

571(0.167)675(0.197)608(0.177)538(0.157)590(0.172)5.51 (5.135.89)
5.64 (5.286)
6.29 (5.886.7)
6.12 (5.676.57)
5.23 (4.855.61)
6.12 (5.636.61)

479(0.213)455(0.202)489(0.217)228(0.101)266(0.118)4.82 (4.465.18)
4.64 (4.285)
5.9 (5.466.34)
4.01 (3.554.47)
3.8 (3.444.16)
4.95 (4.455.45)

2054(0.57)

8.85 (8.619.09)

1663(0.586)

7.42 (7.167.68)

2236(0.427)5.42 (5.215.63)

1951(0.432)

4.84 (4.635.05)

2273(0.427)

5.21 (5.035.39)

Country
AU
US
NZ
CA
UK
JP

840 (0.16)

633 (0.14)956 (0.18)

3.3 (3-3.6)

592(0.176)

3.84 (3.484.2)
4.16 (3.824.5)1524(0.454)4.46 (4.264.66)

303 (0.09)445 (0.13)

337 (0.15)

Occupation
Company employee / selfemployed

234

1673
(0.488)5.75 (5.525.98)

1140(0.506)4.6 (4.374.83)

AppendicesWork/Study
Pre-pandemic

Governmental officer

Sample(%)
273(0.076)

Faculty or staff of an
educational institution

178(0.049)Housewife orhousehusband

88 (0.024)

Variable

Part-time job
Student
Other unemployed(including retired)
Other

424
(0.118)242(0.067)200(0.055)147(0.041)

Mean(95%CI)
9.97 (9.3110.63)
10.42(9.6111.23)
8.18 (6.959.41)
8.1 (7.568.64)
8.94 (8.229.66)
8.63 (7.819.45)
7.55 (6.638.47)

552(0.153)1056(0.293)818(0.227)787(0.218)260(0.072)133(0.037)8.83 (8.359.31)
8.95 (8.619.29)
8.88 (8.59.26)
8.75 (8.369.14)
8.63 (7.959.31)
9.02 (8.089.96)

Shopping

During-pandemic
Sample(%)
228 (0.08)
150(0.053)47 (0.017)
371(0.131)171 (0.06)
82 (0.029)
126(0.044)Pre-pandemic

Eating out
During-pandemic

Pre-pandemic

Physical exercise

During-pandemic

Pre-pandemic

During-pandemic

Mean(95%CI)
8.49 (7.769.22)

Sample(%)
281(0.054)

Mean(95%CI)
5.67 (5.076.27)

Sample(%)
248(0.055)

Mean(95%CI)
5.15 (4.555.75)

Sample(%)
305(0.057)

Mean(95%CI)
5.01 (4.545.48)

Sample(%)
208(0.062)

Mean(95%CI)
4.6 (4.055.15)

Sample(%)
229(0.067)

Mean(95%CI)
6.03 (5.396.67)

Sample(%)
170(0.075)

Mean(95%CI)
5.22 (4.615.83)

8.82 (7.919.73)

171(0.033)6.35 (5.527.18)

152(0.034)5.94 (5.16.78)

185(0.035)5.81 (5.146.48)

125(0.037)5.6 (4.896.31)

143(0.042)

6.34 (5.497.19)

96 (0.043)

5.92 (5.066.78)

5.32 (3.866.78)
7.34 (6.787.9)
7.02 (6.197.85)
6.31 (5.127.5)
6.17 (5.267.08)

394(0.075)490(0.094)250(0.048)1128(0.215)286(0.055)

4.11 (3.674.55)
5.17 (4.725.62)
5.5 (4.886.12)
3.85 (3.64.1)
4.91 (4.345.48)

329(0.073)444(0.098)202(0.045)945(0.209)241(0.053)3.75 (3.284.22)
4.71 (4.275.15)
4.54 (3.935.15)
3.54 (3.283.8)
4.48 (3.95.06)

382(0.072)499(0.094)251(0.047)1142(0.215)281(0.053)4.01 (3.634.39)
4.83 (4.455.21)
5.72 (5.146.3)
4.09 (3.874.31)
4.73 (4.225.24)

238(0.071)338(0.101)161(0.048)585(0.174)176(0.052)

3.44 (3.033.85)
4.3 (3.884.72)
4.51 (3.945.08)
3.55 (3.263.84)
4.38 (3.765)

211(0.062)321(0.094)210(0.061)467(0.136)

5.51 (4.856.17)
5.42 (4.895.95)
6.1 (5.416.79)
5.97 (5.486.46)
5.79 (5.046.54)

124(0.055)223(0.099)124(0.055)250(0.111)127(0.056)

4.48 (3.755.21)
4.81 (4.265.36)
5.23 (4.446.02)
5.36 (4.716.01)
4.54 (3.855.23)

7.35 (6.817.89)
7.66 (7.278.05)
7.26 (6.857.67)
7.34 (6.927.76)
7.45 (6.78.2)
7.38 (6.318.45)926(0.177)1827(0.349)1092(0.209)916(0.175)309(0.059)166(0.032)4.45 (4.144.76)
4.81 (4.585.04)
5.15 (4.865.44)
5.43 (5.115.75)
5.64 (5.086.2)
5.02 (4.35.74)

798(0.177)1565(0.347)

4.11 (3.794.43)
4.45 (4.214.69)
4.58 (4.294.87)
4.58 (4.284.88)
5.24 (4.655.83)
4.74 (45.48)

936(0.176)
1890(0.355)1079(0.203)929(0.175)309(0.058)175(0.033)

4.48 (4.224.74)
4.66 (4.474.85)
4.88 (4.635.13)
5.27 (4.995.55)
5.53 (5.075.99)
5.48 (4.816.15)

531(0.158)1098(0.327)723(0.215)666(0.199)209(0.062)
128(0.038)3.71 (3.414.01)
4.15 (3.914.39)
4.28 (4.014.55)
4.57 (4.284.86)
4.72 (4.225.22)
5.08 (4.315.85)

6.53 (3.659.41)

37 (0.007)

4.36 (2.935.79)

4.15 (2.555.75)

40 (0.008)

4.23 (3.115.35)

24 (0.007)

3.4 (2.424.38)

173 (0.05)

Household size
1
2
3
4
5
6+

424
(0.149)793(0.279)654 (0.23)
648(0.228)213(0.075)106(0.037)947 (0.21)
791(0.175)267(0.059)144(0.032)503(0.147)1061(0.31)
767(0.224)716(0.209)241 (0.07)
139(0.041)5.72 (5.266.18)
5.87 (5.566.18)
5.66 (5.326)
5.86 (5.526.2)
5.89 (5.326.46)
5.82 (5.036.61)

337 (0.15)623(0.276)515(0.228)509(0.226)162(0.072)
108(0.048)4.53 (4.065)
4.87 (4.515.23)
4.67 (4.335.01)
5 (4.655.35)
4.71 (4.165.26)
5.76 (4.876.65)

Education
No formal qualification

17 (0.005)

Elementary school

24 (0.007)

Middle school/ Juniorhigh school
High school/ Senior highschoolCollege/ Vocational
school199(0.055)

10.26(7.4113.11)
6.88 (4.968.8)
8.56 (7.769.36)

606(0.168)Undergraduate/ Bachelor
Master degree
Doctoral degree

16 (0.006)

167(0.059)5.78 (4.177.39)
7.06 (6.247.88)

8.53 (8.088.98)

490(0.173)725(0.201)1347(0.374)588(0.163)100(0.028)8.86 (8.459.27)
9.22 (8.929.52)
8.5 (8.058.95)
8.58 (7.459.71)

565(0.199)1025(0.361)461(0.162)317(0.088)269(0.075)8.03 (7.48.66)
7.62 (6.958.29)

32 (0.011)

82 (0.029)

387(0.074)4.47 (3.125.82)
4.59 (4.15.08)

7.41 (6.927.9)

998(0.191)

7.39 (6.937.85)
7.94 (7.68.28)
6.68 (6.27.16)
6.99 (5.798.19)

7.11 (6.47.82)
6.83 (6.127.54)

26 (0.006)

321(0.071)4.32 (3.415.23)
4.11 (3.624.6)

4.63 (4.344.92)880(0.195)1162(0.222)1770(0.338)711(0.136)133(0.025)5.06 (4.775.35)
5.14 (4.95.38)
5.22 (4.875.57)
4.99 (4.215.77)

621(0.119)533(0.102)

4.52 (4.154.89)
3.81 (3.454.17)

38 (0.007)

376(0.071)

4.41 (3.365.46)
4.27 (3.894.65)

4.28 (3.994.57)

960(0.181)4.47 (4.214.73)

990(0.219)1527(0.338)606(0.134)116(0.026)

4.53 (4.234.83)
4.54 (4.34.78)
4.83 (4.475.19)
4.87 (4.045.7)

1199(0.225)1824(0.343)730(0.137)132(0.025)4.97 (4.725.22)
4.99 (4.795.19)
5.18 (4.875.49)
5.03 (4.315.75)

569(0.126)458(0.102)

4.39 (44.78)
3.7 (3.324.08)

541(0.102)539(0.101)

4.36 (4.014.71)
4.01 (3.694.33)

46 (0.01)

57 (0.011)

213(0.063)

5.57 (4.426.72)
3.79 (3.374.21)

584(0.174)4.19 (3.874.51)

47 (0.014)

737 (0.22)1175(0.35)
486(0.145)89 (0.027)

4.19 (3.894.49)
4.24 (4.024.46)
4.53 (4.194.87)
4.83 (3.895.77)

21 (0.006)

5.33 (2.997.67)

200(0.058)5.02 (4.135.91)
5.28 (4.65.96)

569(0.166)15 (0.007)

4.8 (2.636.97)

143(0.063)5.81 (4.836.79)
4.87 (4.165.58)

5.49 (5.095.89)

358(0.159)4.82 (4.385.26)

1200(0.35)
575(0.168)100(0.029)5.83 (5.466.2)
6 (5.716.29)
5.91 (5.526.3)
5.78 (4.846.72)

434(0.193)786(0.349)408(0.181)

4.86 (4.455.27)
4.73 (4.435.03)
4.91 (4.535.29)
4.81 (3.855.77)

300(0.088)285(0.083)5.44 (4.895.99)
5.52 (4.956.09)

42 (0.012)

720 (0.21)

43 (0.019)

67 (0.03)

IncomeUnder 24,999USD
25,000 to 34,999USD

241(0.085)223(0.079)235

365(0.109)318(0.095)

4.18 (3.774.59)
3.56 (3.23.92)

206(0.091)205(0.091)4.9 (4.285.52)
4.9 (4.35.5)

AppendicesWork/Study
Pre-pandemic

175,000 to 199,999USD

Sample(%)
388(0.108)606(0.168)583(0.162)487(0.135)374(0.104)266(0.074)134(0.037)200,000USD and over182 (0.05)

Variable
35,000 to 49,999USD
50,000 to 74,999USD
75,000 to 99,999USD
100,000 to 124,999USD
125,000 to 149,999USD
150,000 to 174,999USD

Mean(95%CI)
8.21 (7.658.77)
8.86 (8.419.31)
9.05 (8.69.5)
9.2 (8.79.7)
9.2 (8.649.76)
9.37 (8.710.04)
9.37 (8.3910.35)
9.99 (9.1510.83)

Shopping

During-pandemic
Sample(%)
310(0.109)463(0.163)446(0.157)390(0.137)
293(0.103)229(0.081)103(0.036)140(0.049)Mean(95%CI)
6.99 (6.397.59)
7.14 (6.657.63)
7.58 (7.078.09)
7.39 (6.857.93)
7.71 (7.088.34)
8.09 (7.378.81)
8.07 (6.939.21)
8.26 (7.299.23)

Pre-pandemic
Sample(%)
681 (0.13)885(0.169)804(0.154)605(0.116)443(0.085)294(0.056)159 (0.03)
211 (0.04)

Eating out
During-pandemic

Mean(95%CI)
4.61 (4.264.96)
4.84 (4.525.16)
5.53 (5.175.89)
5.24 (4.845.64)
5.4 (4.945.86)
5.63 (5.046.22)
5.73 (4.936.53)
5.9 (5.166.64)

Sample(%)
579(0.128)744(0.165)686(0.152)518(0.115)379(0.084)266(0.059)129(0.029)184(0.041)

Mean(95%CI)
4.08 (3.724.44)
3.96 (3.664.26)
4.88 (4.515.25)
4.68 (4.285.08)
5.11 (4.635.59)
5.17 (4.565.78)
5.41 (4.576.25)
5.48 (4.696.27)

4.16 (3.464.86)
4.37 (3.914.83)
4.66 (4.344.98)
5.05 (4.885.22)
5.34 (4.935.75)

107(0.024)503(0.111)969(0.215)2594(0.575)339(0.075)4.62 (3.925.32)
3.93 (3.594.27)
4.25 (3.984.52)
4.61 (4.424.8)
5.18 (4.655.71)

3.61 (2.924.3)
4.19 (3.844.54)
4.81 (4.644.98)
5.52 (5.265.78)

123(0.027)578(0.128)2813(0.623)924(0.205)Pre-pandemic
Sample(%)
677(0.127)923(0.174)847(0.159)648(0.122)451(0.085)307(0.058)165(0.031)220(0.041)

Mean(95%CI)
4.58 (4.274.89)
4.69 (4.424.96)
5.23 (4.935.53)
4.94 (4.625.26)
5.06 (4.675.45)
5.58 (5.076.09)
5.55 (4.866.24)
6.02 (5.366.68)

Physical exercise

During-pandemic
Sample(%)
371(0.111)543(0.162)410(0.122)317(0.094)230(0.069)110(0.033)153(0.046)

Mean(95%CI)
3.95 (3.574.33)
3.83 (3.544.12)
4.59 (4.244.94)
4.26 (3.894.63)
4.61 (4.185.04)
4.96 (4.395.53)
4.67 (3.945.4)
5.06 (4.315.81)

538 (0.16)

Pre-pandemic
Sample(%)
360(0.105)611(0.178)553(0.161)463(0.135)331(0.097)235(0.069)130(0.038)159(0.046)

Mean(95%CI)
5.75 (5.226.28)
5.4 (5.025.78)
5.99 (5.576.41)
5.67 (5.236.11)
5.93 (5.416.45)
6.56 (5.897.23)
6.42 (5.547.3)
6.48 (5.647.32)

During-pandemic
Sample(%)
222(0.098)371(0.165)374(0.166)289(0.128)224(0.099)167(0.074)87 (0.039)
109(0.048)

Mean(95%CI)
4.73 (4.165.3)
4.51 (4.14.92)
4.62 (4.215.03)
4.62 (4.195.05)
5.09 (4.555.63)
5.56 (4.856.27)
4.7 (3.835.57)
5.67 (4.76.64)

Frequency
A few times a year
Less than once a month
Once or twice a month
Once or twice a week
Daily or almost daily

116(0.032)212(0.059)191(0.053)413(0.115)2674(0.742)

4.63 (3.975.29)
5.5 (4.936.07)
5.52 (4.926.12)
7.26 (6.747.78)
9.78 (9.579.99)

268(0.094)260(0.092)524(0.185)1692(0.596)

4.27 (3.574.97)
4.92 (4.445.4)
5.25 (4.755.75)
6.7 (6.267.14)
8.55 (8.288.82)

5.79 (4.696.89)
5.39 (4.815.97)
6.21 (5.856.57)
6.98 (6.457.51)
10.38(10.1510.61)

109(0.038)190(0.067)599(0.211)365(0.129)4 (3.174.83)
4.75 (4.145.36)
5.13 (4.825.44)
6.25 (5.736.77)

1575(0.555)9.13 (8.859.41)

107 (0.02)

7.68 (6.618.75)

6.54 (6.236.85)
8.69 (8.329.06)
10.41(10.1510.67)

1188(0.419)633(0.223)5.88 (5.66.16)
7.24 (6.847.64)2442(0.466)1279(0.244)1017(0.358)9.34 (8.999.69)

7 (6.617.39)
9.38 (9.189.58)

1775(0.625)1063(0.375)7.51 (7.257.77)
7.28 (6.977.59)

94 (0.033)

1263(0.237)1914(0.36)
1819(0.342)245(0.046)

4.34 (3.615.07)
4.39 (4.174.61)
4.96 (4.765.16)
4.97 (4.775.17)
5.71 (5.136.29)

103(0.031)1483(0.442)966(0.288)654(0.195)149(0.044)4.37 (3.695.05)
3.97 (3.774.17)
4.38 (4.134.63)
4.49 (4.194.79)
5.3 (4.645.96)

3.85 (3.14.6)
3.95 (3.624.28)
4.34 (4.174.51)
5.29 (4.965.62)

123(0.023)387(0.073)2045(0.385)2656(0.499)3.53 (2.984.08)
3.85 (3.514.19)
4.51 (4.344.68)
5.24 (5.075.41)

148(0.044)292(0.087)1659(0.494)1180(0.352)74 (0.016)

6.05 (4.827.28)

107 (0.02)

7 (6.027.98)

4.51 (4.324.7)
4.96 (4.715.21)

2703(0.599)977(0.217)4.03 (3.864.2)
4.79 (4.515.07)

2051(0.386)1514(0.285)1515(0.289)5.75 (5.486.02)

832(0.184)5.65 (5.286.02)

1006(0.192)4230(0.808)4.31 (4.034.59)
5.14 (4.995.29)

3093(0.686)1419(0.314)4.2 (4.044.36)
5.16 (4.925.4)

73 (0.014)
262 (0.05)
739(0.141)3558(0.68)
604(0.115)692(0.202)683(0.199)1323(0.386)663(0.193)4.26 (3.365.16)
5.08 (4.735.43)
5.16 (4.855.47)
6.26 (5.986.54)
6.44 (6.026.86)

3.15 (2.583.72)
3.64 (3.254.03)
4.03 (3.864.2)
4.77 (4.525.02)

146(0.043)267(0.078)1476(0.431)1417(0.413)

3.91 (3.234.59)
4.5 (4.024.98)
5.08 (4.865.3)
6.87 (6.597.15)

116(0.051)237(0.105)1102(0.489)712(0.316)

3.4 (2.754.05)
3.97 (3.564.38)
4.34 (4.134.55)
5.81 (5.456.17)

76 (0.023)

6.08 (5.057.11)

121(0.035)

7.19 (6.278.11)

87 (0.039)

7.3 (6.218.39)

4.12 (3.954.29)
5.03 (4.825.24)

1694(0.505)883(0.263)3.7 (3.533.87)
4.6 (4.354.85)

1267(0.37)
1041(0.304)4.86 (4.615.11)
5.9 (5.626.18)

1753(0.33)

5.57 (5.355.79)

778(0.232)5.1 (4.795.41)

1119(0.327)

6.77 (6.457.09)

516(0.229)5.8 (5.386.22)

769(0.145)4549(0.855)4.34 (4.064.62)
4.94 (4.815.07)

1700(0.507)1655(0.493)3.8 (3.623.98)
4.74 (4.554.93)

533(0.156)2894(0.844)4.56 (4.234.89)
6.03 (5.846.22)

1010(0.448)1244(0.552)

4.14 (3.894.39)
5.39 (5.155.63)

77 (0.014)

66 (0.019)

100(0.044)648(0.287)499(0.221)669(0.297)338 (0.15)

4.53 (3.785.28)
4.29 (3.994.59)
4.29 (3.994.59)
5.48 (5.145.82)
5.49 (4.966.02)

Duration
Less than 5 mins

90 (0.025)

5 mins - 15 mins

1 hour - 4 hours

245(0.068)623(0.173)377(0.105)

4 hours or longer

2271(0.63)

15 mins - 1 hour

106 (0.02)
562(0.107)2953(0.564)1508(0.288)

Contact mode
Only non-physicalcontacts

1082 (0.3)

Only physical contacts

838(0.232)

Both non-physical andphysical contacts

1686(0.468)1039(0.461)699 (0.31)

4.12 (3.884.36)
5.18 (4.895.47)

Protecting measures
Wear a
mask

Yes
No

797
(0.221)2809(0.779)236

AppendicesWork/Study
Pre-pandemic
Sample(%)

Variable
Disinfect
hands

Yes

936 (0.26)

No

Distancing
with others

Yes

No measures
are taken

Yes

2670
(0.74)
617(0.171)2989(0.829)1900(0.527)1706(0.473)

No

No

Mean
(95%CI)
8.19 (7.838.55)
9.08 (8.879.29)
7.34 (6.937.75)
9.16 (8.969.36)
10.05 (9.810.3)
7.51 (7.257.77)

Shopping

During-pandemic
Sample(%)
1709(0.602)1129(0.398)1606(0.566)1232(0.434)220(0.078)2618(0.922)

Mean(95%CI)
7.88 (7.618.15)
6.73 (6.437.03)
7.68 (7.417.95)
7.09 (6.797.39)
7.78 (78.56)
7.39 (7.187.6)

Pre-pandemic
Sample(%)
1351(0.258)3885(0.742)1068(0.204)4168(0.796)2933(0.56)
2303(0.44)

Mean(95%CI)
4.87 (4.625.12)
5.02 (4.865.18)
4.52 (4.264.78)
5.1 (4.955.25)
5.22 (5.035.41)
4.67 (4.494.85)

Eating out
During-pandemic

Sample(%)
3108(0.689)1404(0.311)3011(0.667)1501(0.333)
194(0.043)4318(0.957)237

Mean(95%CI)
4.41 (4.244.58)
4.68 (4.444.92)
4.39 (4.224.56)
4.72 (4.494.95)
5.69 (4.916.47)
4.44 (4.34.58)

Pre-pandemic
Sample(%)
1345(0.253)3973(0.747)995(0.187)4323(0.813)3041(0.572)2277(0.428)

Mean(95%CI)
4.61 (4.394.83)
4.94 (4.815.07)
4.57 (4.334.81)
4.92 (4.795.05)
5 (4.845.16)
4.66 (4.54.82)

Physical exercise

During-pandemic
Sample(%)
2104(0.627)1251(0.373)2136(0.637)1219(0.363)216(0.064)3139(0.936)

Mean(95%CI)
3.98 (3.824.14)
4.73 (4.514.95)
4.1 (3.944.26)
4.55 (4.334.77)
5.39 (4.736.05)
4.19 (4.064.32)

Pre-pandemic
Sample(%)
828(0.242)2599(0.758)692(0.202)2735(0.798)1805(0.527)1622(0.473)

Mean(95%CI)
5.5 (5.195.81)
5.9 (5.716.09)
4.97 (4.675.27)
6.01 (5.826.2)
6.34 (6.086.6)
5.2 (5-5.4)

During-pandemic
Sample(%)
1199(0.532)1055(0.468)1207(0.535)1047(0.465)177(0.079)2077(0.921)

Mean(95%CI)
4.75 (4.55)
4.93 (4.695.17)
4.75 (4.514.99)
4.93 (4.685.18)
5.83 (5.046.62)
4.75 (4.574.93)

Appendices

Appendix : Statistics of social contacts for Party, Cultural leisure, Medical activities, Public transportsetting before and during the COVID-19 pandemic

Party
Pre-pandemic

Cultural leisureDuring-pandemic

Pre-pandemic

Medical activities

During-pandemic

Pre-pandemic

Public transport

During-pandemic

Pre-pandemic

During-pandemic

Variable

Sample(%)

Mean(95%CI)

Sample(%)

Mean(95%CI)

Sample(%)

Mean(95%CI)

Sample(%)

Mean(95%CI)

Sample(%)

Mean(95%CI)

Sample(%)

Mean(95%CI)

Sample(%)

Mean(95%CI)

Sample(%)

Mean(95%CI)

Total

4278 (1)

7.21 (7.057.37)

2304 (1)5.68 (5.55.86)4476 (1)

6.32 (6.166.48)

2223 (1)5.29 (5.15.48)5365 (1)3.66 (3.563.76)

4211 (1)3.34 (3.243.44)

3186 (1)

6.96 (6.777.15)

1928 (1)

5.96 (5.746.18)

55(0.024)663(0.288)539(0.234)422(0.183)
293(0.127)73(0.032)97(0.042)162(0.07)

6.74 (5.398.09)
5.45 (5.135.77)
5.55 (5.185.92)
5.67 (5.236.11)
6.33 (5.746.92)
6.41 (5.247.58)
5.57 (4.666.48)
5.3 (4.615.99)

101(0.023)974(0.218)899(0.201)798(0.178)700(0.156)275(0.061)272(0.061)457(0.102)

6.67 (5.77.64)
6.17 (5.866.48)
6.24 (5.916.57)
6.46 (6.096.83)
6.91 (6.487.34)
6.48 (5.87.16)
5.96 (5.296.63)
5.69 (5.196.19)

55(0.025)632(0.284)539(0.242)404(0.182)284(0.128)74(0.033)86(0.039)149(0.067)

5.25 (4.116.39)
5.07 (4.755.39)
4.99 (4.645.34)
5.18 (4.745.62)
6.06 (5.436.69)
5.16 (3.916.41)
6.17 (4.947.4)
5.66 (4.796.53)

113(0.021)1041(0.194)940(0.175)900(0.168)861(0.16)
358(0.067)418(0.078)734(0.137)4.75 (3.955.55)
4.47 (4.224.72)
4.4 (4.134.67)
3.78 (3.534.03)
3.19 (2.963.42)
3.04 (2.683.4)
2.75 (2.493.01)
2.6 (2.422.78)

834(0.198)741(0.176)663(0.157)659(0.156)265(0.063)339(0.081)626(0.149)3.62 (2.874.37)
4.06 (3.814.31)
3.93 (3.664.2)
3.47 (3.213.73)
2.91 (2.683.14)
2.93 (2.533.33)
2.62 (2.352.89)
2.52 (2.332.71)

87(0.027)787(0.247)673(0.211)563(0.177)447(0.14)
178(0.056)174(0.055)277(0.087)

Age

20~29

96
(0.022)959(0.224)30~39857 (0.2)

40~49

769
(0.18)
672(0.157)250(0.058)262(0.061)413(0.097)

6.87 (6.567.18)
7.06 (6.727.4)
7.39 (7.017.77)
7.67 (7.258.09)
7.67 (6.998.35)
7.22 (6.587.86)
6.73 (6.257.21)

2189(0.512)2089(0.488)6.87 (6.667.08)
7.56 (7.337.79)

1302(0.565)1002(0.435)5.65 (5.415.89)
5.72 (5.436.01)

2204(0.492)2272(0.508)6.04 (5.836.25)
6.59 (6.366.82)

1250(0.562)973(0.438)5.15 (4.915.39)
5.47 (5.165.78)

2615(0.487)2750(0.513)3.83 (3.693.97)
3.5 (3.363.64)2103(0.499)2108(0.501)

3.56 (3.413.71)
3.12 (2.983.26)

1664(0.522)1522(0.478)6.68 (6.426.94)
7.26 (6.977.55)

1072(0.556)856(0.444)5.83 (5.556.11)
6.14 (5.796.49)

726(0.17)
827(0.193)795(0.186)696(0.163)798(0.187)436(0.102)

6.88 (6.527.24)
6.84 (6.57.18)
7.97 (7.68.34)
7.46 (7.067.86)
6.78 (6.427.14)
7.44 (6.947.94)

510(0.221)481(0.209)614(0.266)215(0.093)247(0.107)237(0.103)

5.83 (5.446.22)
5.26 (4.895.63)
6.95 (6.547.36)
4.53 (4.025.04)
4.25 (3.844.66)
5.47 (4.866.08)

728(0.163)846(0.189)800(0.179)735(0.164)815(0.182)552(0.123)6.06 (5.76.42)
6.57 (6.226.92)
6.67 (6.297.05)
6.55 (6.166.94)
5.46 (5.125.8)
6.72 (6.237.21)

500(0.225)419(0.188)598(0.269)196(0.088)214(0.096)296(0.133)5.49 (5.095.89)

848(0.158)1065(0.199)969(0.181)867(0.162)865(0.161)751(0.14)

3.86 (3.594.13)
3.81 (3.64.02)
3.41 (3.193.63)
3.36 (3.133.59)
2.9 (2.73.1)
4.75 (4.425.08)

727(0.173)868(0.206)860(0.204)557(0.132)511(0.121)688(0.163)3.51 (3.263.76)
3.33 (3.133.53)
3.11 (2.93.32)
2.72 (2.52.94)
2.81 (2.573.05)
4.34 (4.024.66)

549(0.172)483(0.152)519(0.163)466(0.146)654(0.205)515(0.162)

6.62 (6.187.06)
6.63 (6.177.09)
6.5 (6.046.96)
7.69 (7.158.23)
6.69 (6.267.12)
7.79 (7.268.32)

397(0.206)290(0.15)
373(0.193)203(0.105)302(0.157)363(0.188)5.88 (5.426.34)
5.01 (4.585.44)
6.22 (5.686.76)
5.98 (5.346.62)
4.78 (4.35.26)
7.53 (6.918.15)

Less than 19

50~59
60~64
65~69
70 and above

8.06 (7-9.12)

84 (0.02)

6.93 (5.818.05)

7.15 (6.747.56)
6.85 (6.47.3)
8 (7.428.58)
7.42 (6.538.31)
7.02 (6.117.93)
6.46 (5.757.17)

63(0.033)574(0.298)454(0.235)356(0.185)222(0.115)67(0.035)71(0.037)121(0.063)6.03 (4.817.25)
5.47 (5.125.82)
6.01 (5.586.44)
5.74 (5.236.25)
7.21 (6.437.99)

6.35 (6-6.7)

6.6 (5.2-8)
6.36 (4.977.75)
5.89 (4.856.93)

GenderMale
FemaleCountry
AU
US
NZ
CA
UK
JP

238

5 (4.6-5.4)
6.06 (5.656.47)
4.24 (3.774.71)
3.82 (3.444.2)
5.55 (4.946.16)

Appendices

Party
Pre-pandemic
Variable

Cultural leisureDuring-pandemic

Pre-pandemic

Medical activities

During-pandemic

Pre-pandemic

Public transport

During-pandemic

Pre-pandemic

During-pandemic

Sample(%)

Mean(95%CI)

Sample(%)

Mean(95%CI)

Sample(%)

Mean(95%CI)

Sample(%)

Mean(95%CI)

Sample(%)

Mean(95%CI)

Sample(%)

Mean(95%CI)

Sample(%)

Mean(95%CI)

Sample(%)

Mean(95%CI)

1994(0.466)267(0.062)177(0.041)267(0.062)393(0.092)
221(0.052)752(0.176)207(0.048)7.4 (7.177.63)

5.64 (5.385.9)
6.42 (5.697.15)
6.56 (5.667.46)
4.99 (4.25.78)
5.72 (5.136.31)
6.17 (5.337.01)
5.37 (4.845.9)
5.08 (4.295.87)

2029(0.453)272(0.061)177(0.04)
306(0.068)412(0.092)228(0.051)828(0.185)224(0.05)

6.53 (6.36.76)
6.85 (6.237.47)
7.5 (6.718.29)
5.97 (5.366.58)
5.95 (5.446.46)
6.7 (6.037.37)
5.66 (5.296.03)
6.08 (5.46.76)

1131(0.509)158(0.071)97(0.044)106(0.048)121(0.054)261(0.117)126(0.057)5.29 (5.025.56)
5.93 (5.186.68)
6.62 (5.657.59)
4.82 (3.945.7)
4.78 (4.255.31)
5.61 (4.816.41)
4.98 (4.395.57)
5.07 (4.275.87)

2197(0.41)
296(0.055)
189(0.035)403(0.075)496(0.092)240(0.045)1259(0.235)285(0.053)4 (3.844.16)
4.56 (4.065.06)
4.94 (4.325.56)
3.41 (3.063.76)
3.65 (3.323.98)
4.67 (4.15.24)
2.57 (2.442.7)
3.52 (3.093.95)

1674(0.398)236(0.056)158(0.038)315(0.075)407(0.097)180(0.043)1008(0.239)233(0.055)3.64 (3.473.81)
4.25 (3.724.78)
4.15 (3.564.74)
3.2 (2.823.58)
3.43 (3.113.75)
3.52 (3.024.02)
2.51 (2.362.66)
3.17 (2.783.56)1484(0.466)207(0.065)
119(0.037)203(0.064)302(0.095)
194(0.061)525(0.165)152(0.048)7.22 (6.947.5)
6.93 (6.217.65)
7.39 (6.418.37)
6.62 (5.827.42)
6.91 (6.277.55)
7.21 (6.437.99)
6.49 (5.987)
6.03 (5.186.88)

927(0.481)134(0.07)
86(0.045)113(0.059)197(0.102)130(0.067)237(0.123)104(0.054)

6.22 (5.96.54)

6.78 (6.417.15)
6.75 (6.037.47)

1149(0.499)170(0.074)104(0.045)
119(0.052)238(0.103)126(0.055)278(0.121)120(0.052)698(0.163)1446(0.338)902(0.211)809(0.189)267(0.062)156(0.036)

7.06 (6.677.45)
7.15 (6.887.42)
7.06 (6.737.39)
7.38 (7.027.74)
7.63 (7.018.25)
7.61 (6.818.41)

311(0.135)686(0.298)525(0.228)511(0.222)164(0.071)107(0.046)

5.25 (4.755.75)
5.72 (5.376.07)
5.71 (5.336.09)
5.76 (5.386.14)
5.38 (4.756.01)
6.68 (5.727.64)764(0.171)1483(0.331)952(0.213)850(0.19)
274(0.061)153(0.034)5.83 (5.456.21)
6.45 (6.176.73)
6.15 (5.836.47)
6.53 (6.196.87)
6.75 (6.147.36)
6.65 (5.867.44)

330(0.148)610(0.274)523(0.235)496(0.223)159(0.072)105(0.047)

4.81 (4.35.32)
5.53 (5.145.92)
5.3 (4.925.68)
5.24 (4.855.63)
5.14 (4.525.76)
5.81 (4.936.69)

968(0.18)
1938(0.361)1081(0.201)903(0.168)312(0.058)163(0.03)

2.91 (2.723.1)
3.28 (3.133.43)
4.07 (3.834.31)
4.21 (3.954.47)
4.64 (4.175.11)
4.97 (4.245.7)752(0.179)1494(0.355)861(0.204)724(0.172)239(0.057)141(0.033)

2.78 (2.592.97)
3.03 (2.873.19)
3.63 (3.393.87)
3.67 (3.433.91)
4.14 (3.654.63)
4.71 (3.915.51)

601(0.189)979(0.307)632(0.198)647(0.203)207(0.065)120(0.038)6.71 (6.247.18)
7.3 (6.937.67)
6.79 (6.377.21)
6.93 (6.527.34)
7.26 (6.537.99)
6.07 (5.216.93)

337(0.175)532(0.276)422(0.219)416(0.216)137(0.071)84(0.044)5.95 (5.376.53)
6.17 (5.726.62)
5.92 (5.476.37)
5.68 (5.256.11)
6.16 (5.366.96)
5.98 (4.986.98)

31(0.007)47(0.011)295(0.069)5.73 (3.867.6)
7.02 (5.88.24)
6.98 (6.387.58)

21(0.009)45 (0.02)
155(0.067)

5.21 (3.097.33)
6.49 (5.367.62)
6.22 (5.476.97)

33(0.007)50(0.011)315(0.07)

4.32 (2.765.88)
6.61 (5.587.64)
5.24 (4.715.77)

21(0.009)46(0.021)150(0.067)

3.81 (2.265.36)
7.12 (5.898.35)
4.88 (4.25.56)

39(0.007)59(0.011)406(0.076)

2.63 (1.793.47)
7.52 (6.188.86)
2.91 (2.613.21)

32(0.008)54(0.013)319(0.076)

2.91 (1.694.13)
5.51 (4.36.72)
2.83 (2.513.15)

16(0.005)43(0.013)215(0.067)

3.72 (2.265.18)
5.59 (4.466.72)
5.45 (4.756.15)

13(0.007)46(0.024)112(0.058)2.58 (1.523.64)
5.62 (4.646.6)
5.5 (4.596.41)

717(0.168)916(0.214)1497(0.35)
662(0.155)113(0.026)

6.94 (6.567.32)
7.42 (7.087.76)
7.38 (7.117.65)
6.87 (6.497.25)
7.92 (6.928.92)

365(0.158)458(0.199)794(0.345)405(0.176)61(0.026)

5.81 (5.336.29)
5.76 (5.336.19)
5.47 (5.165.78)
5.59 (5.176.01)
5.87 (4.687.06)

734(0.164)968(0.216)1591(0.355)662(0.148)123(0.027)

6.16 (5.786.54)
6.51 (6.166.86)
6.44 (6.176.71)
6.44 (6.056.83)
6.76 (5.837.69)

343(0.154)428(0.193)773(0.348)400(0.18)
62(0.028)5.31 (4.825.8)
5.06 (4.625.5)
5.29 (4.965.62)
5.46 (5.035.89)
5.75 (4.586.92)

994(0.185)1206(0.225)1784(0.333)739(0.138)138(0.026)

3.43 (3.213.65)
3.51 (3.313.71)
3.74 (3.563.92)
4.09 (3.824.36)
4.11 (3.464.76)

773(0.184)941(0.223)1379(0.327)597(0.142)116(0.028)3.25 (3.023.48)
3.05 (2.863.24)
3.41 (3.233.59)
3.76 (3.54.02)
3.69 (3.024.36)

501(0.157)628(0.197)1098(0.345)510(0.16)
86(0.027)

6.49 (6.016.97)
7 (6.567.44)
7.51 (7.177.85)
6.75 (6.37.2)
8.05 (6.789.32)

334(0.173)345(0.179)644(0.334)340(0.176)59(0.031)

5.83 (5.36.36)
5.83 (5.316.35)
6.35 (5.946.76)
5.65 (5.176.13)
6.42 (5.137.71)

368(0.086)

6.44 (5.936.95)

204(0.089)5.52 (4.886.16)

405(0.09)

5.81 (5.36.32)

211(0.095)5.05 (4.415.69)

626(0.117)3.45 (3.173.73)

525(0.125)3.2 (2.923.48)

358(0.112)

6.92 (6.327.52)

241(0.125)5.75 (5.16.4)

Occupation
Company employee / selfemployed
Governmental officier
Faculty or staff of an
educational institution
Housewife or househusband
Part-time job
Student
Other unemployed(including retired)Other

7.62 (7-8.24)
7.52 (6.778.27)
6.61 (6-7.22)
6.97 (6.477.47)
7.7 (7-8.4)

223 (0.1)

5.75 (5-6.5)
5.78 (4.936.63)
5.35 (4.396.31)
6.5 (5.777.23)
6.13 (5.286.98)
5.48 (4.86.16)
4.67 (3.825.52)

Household size
1
2
3
4
5
6+
Education
No formal qualification
Elementary school
Middle school/ Junior highschoolHigh school/ Senior highschoolCollege/ Vocational school
Undergraduate/ Bachelor
Master degree
Doctoral degree
IncomeUnder 24,999USD

239

Appendices

Party
Pre-pandemic
Variable
25,000 to 34,999USD
35,000 to 49,999USD
50,000 to 74,999USD
75,000 to 99,999USD
100,000 to 124,999USD
125,000 to 149,999USD
150,000 to 174,999USD
175,000 to 199,999USD
200,000USD and over

Cultural leisureDuring-pandemic

Pre-pandemic

Medical activities

During-pandemic

Pre-pandemic

Public transport

During-pandemic

Pre-pandemic

During-pandemic

Sample(%)

Mean(95%CI)

Sample(%)

Mean(95%CI)

Sample(%)

Mean(95%CI)

Sample(%)

Mean(95%CI)

Sample(%)

Mean(95%CI)

Sample(%)

Mean(95%CI)

Sample(%)

Mean(95%CI)

Sample(%)

Mean(95%CI)370(0.086)493(0.115)767(0.179)701(0.164)569(0.133)393(0.092)258(0.06)
151(0.035)208(0.049)6.46 (5.976.95)
6.9 (6.457.35)
6.99 (6.637.35)
7.62 (7.228.02)
7.31 (6.887.74)
7.64 (7.138.15)
7.56 (6.928.2)
7.52 (6.698.35)
8.25 (7.479.03)

190(0.082)
214(0.093)387(0.168)393(0.171)310(0.135)229(0.099)157(0.068)91(0.039)129(0.056)5.1 (4.535.67)
5.45 (4.876.03)
5.26 (4.835.69)
5.82 (5.366.28)
5.65 (5.146.16)
6.27 (5.666.88)
6.06 (5.356.77)
5.87 (4.926.82)
6.46 (5.547.38)

419(0.094)534(0.119)794(0.177)731(0.163)572(0.128)395(0.088)269(0.06)
149(0.033)208(0.046)

5.58 (5.096.07)
5.89 (5.446.34)
6.11 (5.756.47)
6.65 (6.267.04)
6.53 (6.16.96)
6.67 (6.157.19)
6.8 (6.137.47)
7.02 (6.177.87)
7.13 (6.367.9)

199(0.09)
219(0.099)356(0.16)
375(0.169)285(0.128)219(0.099)154(0.069)85(0.038)120(0.054)4.81 (4.215.41)
4.72 (4.145.3)
4.82 (4.395.25)
5.73 (5.246.22)
5.25 (4.745.76)
5.45 (4.876.03)
5.82 (5.056.59)
5.87 (4.816.93)
6.26 (5.297.23)

562(0.105)698(0.13)
920(0.171)826(0.154)634(0.118)443(0.083)287(0.053)155(0.029)214(0.04)

3.28 (2.993.57)
3.23 (2.983.48)
3.36 (3.143.58)
3.95 (3.684.22)
3.69 (3.43.98)
4.04 (3.74.38)
4.61 (4.15.12)
4.18 (3.554.81)
4.27 (3.684.86)446(0.106)524(0.124)687(0.163)631(0.15)
495(0.118)361(0.086)235(0.056)131(0.031)176(0.042)

3.15 (2.853.45)
2.84 (2.613.07)
3.07 (2.853.29)
3.37 (3.113.63)
3.54 (3.233.85)
3.64 (3.33.98)
4.57 (45.14)
3.45 (2.884.02)
3.68 (3.134.23)

323(0.101)355(0.111)534(0.168)455(0.143)429(0.135)296(0.093)
189(0.059)109(0.034)138(0.043)

6.28 (5.696.87)
6.27 (5.76.84)
6.45 (5.986.92)
7.07 (6.557.59)
7.45 (6.917.99)
7.78 (7.168.4)
7.59 (6.788.4)
7.52 (6.468.58)
7.48 (6.58.46)

188(0.098)201(0.104)306(0.159)266(0.138)262(0.136)
185(0.096)134(0.07)
67(0.035)5.85 (5.136.57)
5.4 (4.736.07)
5.73 (5.216.25)
5.7 (5.126.28)
6.16 (5.556.77)
6.47 (5.777.17)
6.88 (5.957.81)
6.64 (5.377.91)
6.15 (4.967.34)

101(0.024)1872(0.438)1319(0.308)809(0.189)177(0.041)

5.46 (4.66.32)
7.48 (7.237.73)
7.08 (6.817.35)
7.16 (6.817.51)
6.46 (5.747.18)

130(0.056)1138(0.494)494(0.214)400(0.174)142(0.062)5.15 (4.445.86)
5.69 (5.415.97)
5.69 (5.36.08)
5.75 (5.356.15)
5.9 (5.186.62)

126(0.028)2334(0.521)1284(0.287)541(0.121)
191(0.043)

5.58 (4.796.37)
6.39 (6.166.62)
6.4 (6.116.69)
6.01 (5.636.39)
6.25 (5.696.81)

117(0.053)1190(0.535)411(0.185)340(0.153)165(0.074)4.53 (3.775.29)
5.32 (5.035.61)
5.03 (4.645.42)
5.31 (4.95.72)
6.23 (5.626.84)

189(0.035)3600(0.671)1065(0.199)363(0.068)148(0.028)3.75 (3.284.22)
3.23 (3.123.34)
3.96 (3.744.18)
5.82 (5.336.31)
6.55 (5.767.34)

185(0.044)2688(0.638)846(0.201)344(0.082)148(0.035)3.92 (3.394.45)
2.84 (2.732.95)
3.67 (3.453.89)
5.11 (4.675.55)
5.56 (4.866.26)

147(0.046)1223(0.384)616(0.193)551(0.173)649(0.204)5.76 (5.016.51)
6.99 (6.677.31)
6.37 (5.956.79)
6.6 (6.177.03)
8.06 (7.68.52)

386 (0.2)393(0.204)311(0.161)

4.46 (3.865.06)
6.05 (5.666.44)
5.36 (4.925.8)
6.05 (5.596.51)
7.01 (6.47.62)

190(0.044)263(0.061)1173(0.274)2230(0.521)422(0.099)5.7 (4.976.43)
5.31 (4.85.82)
5.54 (5.35.78)
7.96 (7.748.18)
9.74 (9.2310.25)

149(0.065)
227(0.099)925(0.401)862(0.374)141(0.061)4.65 (3.95.4)
4.59 (4.095.09)
4.76 (4.534.99)
6.74 (6.47.08)
8.08 (7.218.95)

151(0.034)271(0.061)956(0.214)2839(0.634)259(0.058)4.84 (4.045.64)
5.05 (4.565.54)
5.39 (5.125.66)
6.73 (6.526.94)
7.44 (6.778.11)

130(0.058)231(0.104)761(0.342)1014(0.456)87(0.039)4.66 (3.85.52)
4.22 (3.814.63)
4.53 (4.274.79)
6.04 (5.726.36)
6.94 (5.888)

134(0.025)631(0.118)3369(0.628)1079(0.201)152(0.028)3.38 (2.754.01)
2.86 (2.643.08)
3.3 (3.193.41)
4.53 (4.274.79)
8.98 (8.089.88)

138(0.033)553(0.131)2677(0.636)732(0.174)111(0.026)

3.61 (2.924.3)
2.67 (2.452.89)
3.07 (2.963.18)
4.25 (3.964.54)
6.79 (5.87.78)

111(0.035)368(0.116)2028(0.637)578(0.181)101(0.032)

5.39 (4.416.37)
5.22 (4.735.71)
7.11 (6.867.36)
7.74 (7.278.21)
7.64 (6.618.67)

72(0.037)293(0.152)1181(0.613)319(0.165)63(0.033)4.61 (3.525.7)
4.68 (4.25.16)
6.05 (5.776.33)
6.88 (6.37.46)
7.21 (5.918.51)

1341(0.313)1286(0.301)1651(0.386)

5.89 (5.636.15)

963(0.418)7 (6.73-7.27)

692 (0.3)

8.44 (8.188.7)

649(0.282)4.74 (4.485)
5.81 (5.496.13)
6.93 (6.547.32)

1801(0.402)1280(0.286)1395(0.312)5.5 (5.275.73)
6.49 (6.216.77)
7.21 (6.927.5)1058(0.476)635(0.286)530(0.238)4.77 (4.55.04)
5.43 (5.115.75)
6.16 (5.726.6)

1622(0.302)1699(0.317)2044(0.381)

3.56 (3.383.74)
3.67 (3.53.84)
3.72 (3.553.89)

1666(0.396)1209(0.287)1336(0.317)3.07 (2.923.22)
3.55 (3.373.73)
3.48 (3.293.67)

1553(0.487)805(0.253)828(0.26)

6.49 (6.216.77)
6.48 (6.146.82)
8.32 (7.918.73)

1079(0.56)
531(0.275)318(0.165)5.56 (5.275.85)
5.96 (5.586.34)
7.35 (6.727.98)

582(0.136)3696(0.864)5.18 (4.835.53)
7.53 (7.367.7)

924(0.401)1380(0.599)4.67 (4.44.94)
6.36 (6.126.6)

606(0.135)3870(0.865)5.12 (4.755.49)
6.51 (6.346.68)

976(0.439)1247(0.561)

4.62 (4.344.9)
5.81 (5.566.06)

1005(0.187)4360(0.813)3.68 (3.463.9)
3.65 (3.543.76)

2837(0.674)1374(0.326)2.97 (2.863.08)
4.11 (3.924.3)

533(0.167)2653(0.833)

6.16 (5.716.61)
7.12 (6.917.33)

1168(0.606)760(0.394)

6.25 (5.936.57)
5.52 (5.245.8)

78 (0.04)

Frequency
A few times a year
Less than once a month
Once or twice a month
Once or twice a week
Daily or almost daily

125(0.065)713(0.37)

Duration
Less than 5 mins
5 mins - 15 mins
15 mins - 1 hour
1 hour - 4 hours
4 hours or longer
Contact mode
Only non-physical contacts
Only physical contacts
Both non-physical andphysical contacts
Protecting measures
Wear a
mask

Yes
No

240

Appendices

Party
Pre-pandemic
Variable
Disinfect
hands

Yes

Distancing
with others

Yes

No measures
are taken

Yes

No

No

No

Cultural leisureDuring-pandemic

Pre-pandemic

Medical activities

During-pandemic

Pre-pandemic

Public transport

During-pandemic

Pre-pandemic

During-pandemic

Sample(%)

Mean(95%CI)

Sample(%)

Mean(95%CI)

Sample(%)

Mean(95%CI)

Sample(%)

Mean(95%CI)

Sample(%)

Mean(95%CI)

Sample(%)

Mean(95%CI)

Sample(%)

Mean(95%CI)

Sample(%)

Mean(95%CI)865(0.202)3413(0.798)746(0.174)3532(0.826)2576(0.602)1702(0.398)

6.21 (5.896.53)
7.46 (7.287.64)
5.67 (5.355.99)
7.53 (7.367.7)
8.09 (7.888.3)
5.88 (5.676.09)

1221(0.53)
1083(0.47)
1176(0.51)
1128(0.49)
235(0.102)2069(0.898)5.52 (5.265.78)
5.86 (5.66.12)
5.56 (5.295.83)
5.81 (5.556.07)
7.61 (6.958.27)
5.46 (5.275.65)

917(0.205)3559(0.795)758(0.169)3718(0.831)2752(0.615)1724(0.385)5.73 (5.416.05)
6.47 (6.296.65)
5.49 (5.165.82)
6.49 (6.326.66)
6.75 (6.546.96)
5.62 (5.45.84)

1178(0.53)
1045(0.47)
1160(0.522)1063(0.478)213(0.096)2010(0.904)5.15 (4.885.42)
5.44 (5.185.7)
5.25 (4.985.52)
5.34 (5.085.6)
6.85 (6.157.55)
5.12 (4.925.32)

1372(0.256)3993(0.744)1033(0.193)4332(0.807)2926(0.545)2439(0.455)

3.63 (3.443.82)
3.67 (3.553.79)
3.9 (3.684.12)
3.6 (3.493.71)
3.32 (3.193.45)
4.06 (3.914.21)

2821(0.67)
1390(0.33)
2609(0.62)
1602(0.38)
202(0.048)4009(0.952)2.97 (2.863.08)
4.07 (3.874.27)
3.15 (3.033.27)
3.65 (3.483.82)
4.07 (3.464.68)
3.3 (3.23.4)

816(0.256)2370(0.744)570(0.179)2616(0.821)1669(0.524)1517(0.476)

6.19 (5.846.54)
7.23 (77.46)
6.01 (5.626.4)
7.17 (6.957.39)
7.75 (7.468.04)
6.1 (5.856.35)

1058(0.549)870(0.451)986(0.511)942(0.489)103(0.053)
1825(0.947)5.76 (5.466.06)
6.21 (5.886.54)
6.05 (5.736.37)
5.87 (5.566.18)
7.11 (5.978.25)
5.9 (5.686.12)

241