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Epidemiological Tools that Predict Partial Herd Immunity to SARS Coronavirus 2
Yasuhiko Kamikubo, M.D., Ph.D. and Atsushi Takahashi, M.D., Ph.D.

From the Biomedical Data Intelligence, Department of Human Health Sciences,
Graduate School of Medicine, Kyoto University, Kyoto, 606-8507, Japan (Y. K.); andthe Department of Physical Therapy, School of Health Science and Social Welfare, andResearch Institute of Health and Welfare, Kibi International University, Takahashi,
Japan (A.T). Address reprint requests to Dr. Kamikubo at the Biomedical Data Intelligence,
Department of Human Health Sciences, Graduate School of Medicine, Kyoto University, Sakyo-ku,
Kyoto, 606-8507, Japan, or at kamikubo.yasuhiko.7u@kyoto-u.ac.jp; or to D. Takahashi at the

Department of Physical Therapy, School of Health Science and Social Welfare, Kibi
International University, 8 Igamachi, Takahashi, Okayama 716-8508, Japan or at
atakah7@kiui.ac.jp.

Drs. Kamikubo and Takahashi contributed equally to this work.

medRxiv preprint doi: https://doi.org/10.1101/2020.03.25.20043679.this version posted March 27, 2020. The copyright holder for thispreprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint inperpetuity.
It is made available under a CC-BY-NC-ND 4.0 International license .

SUMMARY
The outbreak of SARS coronavirus 2 (SARS-CoV-2), which occurred in Wuhan, China
in December 2019, has caused a worldwide pandemic of coronavirus disease 2019
(COVID-19). However, there is a lack of epidemiological tools to guide effective public
policy development. Here we present epidemiological evidence that SARS-CoV-2 S
type exited Wuhan or other epicenters in China earlier than L type and conferred partial
resistance to the virus on infected populations. Analysis of regional disparities inincidence has revealed that a sharp decline in influenza epidemics is a useful surrogate
indicator for the undocumented spread of SARS-CoV-2. The biggest concern in theworld is knowing when herd immunity has been achieved and scheduling a time toregain the living activities of each country. This study provides a useful tool to guide thedevelopment of local policies to contain the virus.

medRxiv preprint doi: https://doi.org/10.1101/2020.03.25.20043679.this version posted March 27, 2020. The copyright holder for thispreprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint inperpetuity.
It is made available under a CC-BY-NC-ND 4.0 International license .

The pandemic of SARS coronavirus 2 (SARS-CoV-2) 1-4 fills the world with fear andconfusion, threatening the collapse of health care and the global financial crisis.
Flattening the epidemic curve to avoid loss of healthcare capacity is a major global
strategy, but has the downside of slowing the achievement of full herd immunity andsacrificing economic activity. Once the herd immunity is completed, the government
can confidently decide when to lift the restriction. Therefore, the government must
predict trends in the outbreak of SARS-CoV-2. There is no waiting in a pandemic andno time to wait for scientific confirmation. At such times epidemiology exists todetermine public health policy first and foremost. Unfortunately, no government knows
epidemiological methods for predicting their own SARS-CoV-2 status. Here, wedeveloped the world's first influenza-based epidemiological method as a useful proxy todetect the spread of SARS-CoV-2 and the establishment of partial herd immunity incountries.

medRxiv preprint doi: https://doi.org/10.1101/2020.03.25.20043679.this version posted March 27, 2020. The copyright holder for thispreprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint inperpetuity.
It is made available under a CC-BY-NC-ND 4.0 International license .

METHODS
SOURCES OF DATA
Epidemiological data were obtained from the Worldometer(https://www.worldometers.info/coronavirus/), the COVID Tracking Project(https://covidtracking.com/data/), and websites operated by Su Wei (https://covid2019.live/), the Infectious Disease Surveillance Center, National Institute of Infectious
Diseases, Japan(https://nesid4g.mhlw.go.jp/Hasseidoko/Levelmap/flu/2019_2020/trend.html), the
Hokkaido government (http://www.pref.hokkaido.lg.jp/hf/kth/kak/hasseijoukyou.htm),
and the World Health Organization(https://app.powerbi.com/view?r=eyJrIjoiZTIxMzAwMzYtZWE4NC00YTU2LWE3M
TUtMTI0OGY1ZjQyMWViIiwidCI6ImY2MTBjMGI3LWJkMjQtNGIzOS04MTBiLTNkYzI4MGFmYjU5MCIsImMiOjh9).

STATISITICAL ANALYSISThe quantification and modeling of the epidemiological curve was performed undercontract with Shin Nippon Biomedical Laboratories, Ltd. (Tokyo, Japan). Analyses ofSpearman correlation coefficient were performed with the use of the Statcel4 add-in
package (OMS Publishing, Tokorozawa, Japan) for Microsoft Excel.

medRxiv preprint doi: https://doi.org/10.1101/2020.03.25.20043679.this version posted March 27, 2020. The copyright holder for thispreprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint inperpetuity.
It is made available under a CC-BY-NC-ND 4.0 International license .

RESULTS
A population genetic study of 103 SARS-CoV-2 genomes has identified two viral
subtypes designate S and L.5 The S type is the ancestral version of the virus. Notably,
only 1 out of 27 (3.7%) viral strains isolated from Wuhan was S type, while 10 out of 31(32.3%) from other places in China and 18 out of 45 (40.0%) outside the country were S
type. The L type has accumulated more mutations than the S type and the orf1ab
replicase/transcriptase gene of L type has a codon that is more preferred for translation
than S type, suggesting that the L type replicates and/or transmits faster than the S type.
However, it remains to be determined whether the L type is more virulent than the Stype.
We examined whether viral subtypes isolated from countries outside China arecorrelated with patterns of SARS-CoV-2 epidemics. As shown in Table 1, lower ratio of
isolated S type is associated with delayed spread of SARS-CoV-2. Moreover, theepidemic seems to be subsiding in areas where S type virus is predominant. These
results prompted us to hypothesize that (i) S type SARS-CoV-2 exit Wuhan or otherepicenter in China earlier than L type virus without recognition by infectious disease
surveillance systems of China and other countries; (ii) the infection by S type induced
herd immunity that provides at least partial protection against spread of SARS-CoV-2.
Clonal competition between L and S subtypes should have taken place in Wuhan and it
is likely that the L type took over the S type because of higher replicative potential
and/or faster transmission. Asymptomatic ratio of Japanese nationals evacuated from
Wuhan, who are more likely to be infected by the L type, is 30.8% (95% confidence
interval: 7.7-53.8%).6 In contrast, 51.4% (318/619) of RT-PCR confirmed cases among
passengers in the cruise ship Diamond Princess, an outbreak traced to a Hong Kong

medRxiv preprint doi: https://doi.org/10.1101/2020.03.25.20043679.this version posted March 27, 2020. The copyright holder for thispreprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint inperpetuity.
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passenger who is most likely to have S type virus, were asymptomatic(https://www.niid.go.jp/niid/en/2019-ncov-e/9417-covid-dp-fe-02.html). Although thesubtypes of the virus remain to be clarified, the higher asymptomatic ratio among cruise
ship passengers supports the view that infection by S type is more likely to be
asymptomatic than that by L type and easier to avoid recognition by surveillancesystems.
Medical doctors in Japan have noticed that influenza epidemic suddenly faded
this winter season. Type I interferons induced by S type SARS-CoV-2 may interferewith infection by influenza virus. We therefore performed regional disparity studies
comparing influenza warning reports from prefectures in Japan with current positive
rate of RT-PCR for SARS-CoV-2. Only 15 prefectures were selected where more than
400 samples were examined because positive rates in other prefectures show Poisson
distribution and are not reliable as an indicator of incidence. As shown in Fig. 1, thepresence of sharp declines in influenza warning reports correlated with lower incidenceof SARS-CoV-2 infection. From early November 2019 to 23 January 2020, about
1,840,000 tourists from China entered Japan, suggesting that unrecognized spread of S
type SARS-CoV-2 interfered with influenza epidemic. Interestingly, the timing of
decline suggests that S type SARS-CoV-2 spread from 23 December 2019 in Tokyo,
Kanagawa, Saitama, and Hiroshima; from 20 January 2020 in Fukuoka and Niigata;from 27 January in Kyoto, Shizuoka, and Ibaraki; and from 2 February in Wakayama.
No outbreak has been recognized in Japan before February 2020 and the Chinese
government implemented blockades in Wuhan and other cities in Hubei Province on 23
January 2020, suggesting that only S form entered those prefectures.
The maximum number of infected cases in Japan has been reported inmedRxiv preprint doi: https://doi.org/10.1101/2020.03.25.20043679.this version posted March 27, 2020. The copyright holder for thispreprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint inperpetuity.
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Hokkaido. Fig. 1 suggests that Hokkaido was not or incompletely exposed to S type andthe current epidemic is due to the L type. Mathematical modeling has shown that
undocumented infections of SARS-CoV-2 were the infection source for mostdocumented cases.7 Because children infected with SARS-CoV-2 are asymptomatic8 or
mildly symptomatic,4 we surmise that children are major sources of SARS-CoV-2
spread. Hokkaido is divided into several General Promotion Bureau jurisdictions.
Incidents of influenza-like illness in kindergartens reported from health centers in thejurisdictions were compared with current prevalence of SARS-CoV-2. As shown in Fig.
2, strong negative correlation was observed (Spearman correlation coefficient ρ= -0.73),
suggesting that undocumented infection SARS-CoV-2 in Hokkaido suppressed
influenza-like illness among small children. Influenza-like illnesses among older
children did not reflect the epidemic (ρ= -0.14 in elementary school, 0.07 in junior highschool, and -0.04 in high school), possibly because influenza-like symptoms of COVID19 in these age groups4 confounded the analyses.
Europe has become the center of the pandemic. But why mortality rates varyfrom country to country remains enigmatic. We arranged influenza epidemic curves ofEuropean countries in descending order of COVID-19 mortality (Supplementary Figs. 1
and 2). There seems to be a pattern in the curve related to COVID-19 case fatality rates(CFR). Depending on the likelihood of S-type SRS-CoV-2 transmission, a scoring
system was developed to model the mortality-related curve patterns. As shown in Fig. 3,
this score correlates with the SARS-CoV-2 CFR in each country, implying that theextent of S type SARS-CoV-2 transmission determines the severity of the current
infection. This scoring system is useful as a country-specific COVID-19 severity risk
score. As shown in Fig. 4, the risk score reveals geographic effects on the spread of themedRxiv preprint doi: https://doi.org/10.1101/2020.03.25.20043679.this version posted March 27, 2020. The copyright holder for thispreprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint inperpetuity.
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S type SRS-CoV-2.

medRxiv preprint doi: https://doi.org/10.1101/2020.03.25.20043679.this version posted March 27, 2020. The copyright holder for thispreprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint inperpetuity.
It is made available under a CC-BY-NC-ND 4.0 International license .

DISCUSSION
Our epidemiological analyses provided evidence for herd immunity against the S type
SARS-CoV-2 that exited Wuhan or other epicenter in China earlier than the L type.
Undocumented spread of SARS-CoV-2 can be monitored through influenza surveillancesystems based on viral competition. The findings have led to the development of an
epidemiological tool that predicts severity of COVID-19 in each region and its
application in Europe.
Because the L type is likely to transmit faster than the S type, the basic
reproduction number of the L type (R0L) is expected to be higher than the S type (R0S).
When herd immunity to the S type is established,, the proportion of the population withimmunity (pS) is 1 - 1/R0S.9 If R0L = 2.2 and R0 S = 2.17, which are plausible values,10 pL= 1 - 1/R0 L = 0.55 (55%) of the population will be infected when a non-immune
population is exposed to the L type. When a population with herd immunity to the S
type is exposed to the L type, pL - pS = 1/R0S – 1/ R0L = 0.006 (0.6%) will be infected,
consistent with the positive rates of SARS-CoV-2 RT-PCR in some Japan prefectures(Fig, 1). If 80% of a population is exposed to the S type and then the whole population
is exposed to the L type, pL – 0.8 x pS = 1 - 1/R0L – 0.8 (1 - 1/ R0L) = 0.114 (11.4%) is
infected, consistent with the positive rates observed in Aichi and Hokkaido in Japan(Fig. 1). Thus, the spread of S type before the L type confers partial herd immunity to
the L type.
Until March 9, 2020, the Japanese government did not restrict entry of tourists
from areas of China outside Hubei Province which might have unintentionally allowed
selective influx of S form to Japan. S type influx into Ireland and the Balkans may be
due to travel restrictions in those countries restricted to infected individuals. In contrast

medRxiv preprint doi: https://doi.org/10.1101/2020.03.25.20043679.this version posted March 27, 2020. The copyright holder for thispreprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint inperpetuity.
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to other areas in Japan, epidemic is ongoing in Nagoya City in Aichi prefecture andSapporo City in Hokkaido, where the spread of the S form may have been incomplete as
discussed above. In the United States of America and most European countries, travelfrom across China was restricted from the beginning of February, blocking the inflow of
the S form from China. There is deep concern that serious outbreaks of SARS-CoV-2 in
the United States and Europe may have been caused by the spread of the L form
preceding the S form, by which up to 55% of the population may be infected as
calculate above.
Although traditional public health outbreak response tactics implemented in
China have been shown to be effective,7 they incur massive costs such as economic
losses.11 Conversely, herd immunity policy will face substantial mortality among highrisk population.12,13 We propose that a policy that expects herd immunity are dangerous
in the areas indicated in brown, red, and magenta in Fig. 4. In areas marked in yellow,
policies that balance social closure and population immunity are desirable. In the areas
marked in green, in contrast, no social isolation may be required except for individuals
over 60 and at high risk, such as those with diabetes, coronary heart disease, high bloodpressure, chronic obstructive pulmonary disease, cerebrovascular disease, andcancer.12,13 Thus, with the use of the new epidemiological tool, governments may beable to predict SARS-CoV-2 morbidity and mortality in their countries and use them inpublic health policies. Applying this scoring system to small geographies should allow
for finer-grained policy creation.
Quantitative analysis for SARS-CoV-2-specific IgM and IgG antibodies in the
blood of healthy persons may reveal herd immunity against S type SARS-CoV-2. Suchseroepidemiological data will predict the effectiveness and risk of herd immunity

medRxiv preprint doi: https://doi.org/10.1101/2020.03.25.20043679.this version posted March 27, 2020. The copyright holder for thispreprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint inperpetuity.
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policies. Detection of "S type SARS-CoV-2 herd immunity" might also help schedule atiming to lift travel restrictions, event restraints, closure of schools, social distancing,and contact precautions. However, collection of sera and analyses require significant
logistic effort, time, and cost,6 which developing countries may not be able to cover.
Moreover, to determine the normal range of the test, the test values in unaffectedindividuals must be determined. For diseases with many subclinical infections, such as
SARS-CoV-2, it may be difficult to determine the cutoff value,9 especially if the
subclinical infection is prevalent in the majority of the population. Meanwhile, quick
epidemiological tools should be useful for policy making that balances herd immunity
approaches with containment policies. The approach used in this study will lay thefoundation for further developing epidemiological tools to support political decisions on
infection control.

medRxiv preprint doi: https://doi.org/10.1101/2020.03.25.20043679.this version posted March 27, 2020. The copyright holder for thispreprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint inperpetuity.
It is made available under a CC-BY-NC-ND 4.0 International license .

REFERENCES
1.

Wu F, Zhao S, Yu B, et al. A new coronavirus associated with humanrespiratory disease in China. Nature 2020;579:265-9.
2.

Zhou P, Yang XL, Wang XG, et al. A pneumonia outbreak associated with a

new coronavirus of probable bat origin. Nature 2020;579:270-3.
3.

Zhu N, Zhang D, Wang W, et al. A Novel Coronavirus from Patients with

Pneumonia in China, 2019. N Engl J Med 2020;382:727-33.
4.

Xu Y, Li X, Zhu B, et al. Characteristics of pediatric SARS-CoV-2 infection

and potential evidence for persistent fecal viral shedding. Nature Medicine 2020.
5.

Tang X, Wu C, Li X, et al. On the origin and continuing evolution of SARS-

CoV-2. National Science Review 2020.
6.

Nishiura H, Kobayashi T, Suzuki A, et al. Estimation of the asymptomatic ratio

of novel coronavirus infections (COVID-19). Int J Infect Dis 2020.
7.

Li R, Pei S, Chen B, et al. Substantial undocumented infection facilitates the

rapid dissemination of novel coronavirus (SARS-CoV2). Science 2020.
8.

Chan JF, Yuan S, Kok KH, et al. A familial cluster of pneumonia associatedwith the 2019 novel coronavirus indicating person-to-person transmission: a study of afamily cluster. Lancet 2020;395:514-23.
9.

Giesecke J. Modern Infectious Disease Epidemiology. 3rd ed: CRC Press;

2017.
10.

Li Q, Guan X, Wu P, et al. Early Transmission Dynamics in Wuhan, China, ofNovel Coronavirus-Infected Pneumonia. N Engl J Med 2020.
11.

Wu Z, McGoogan JM. Characteristics of and Important Lessons From theCoronavirus Disease 2019 (COVID-19) Outbreak in China: Summary of a Report ofmedRxiv preprint doi: https://doi.org/10.1101/2020.03.25.20043679.this version posted March 27, 2020. The copyright holder for thispreprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint inperpetuity.
It is made available under a CC-BY-NC-ND 4.0 International license .

72314 Cases From the Chinese Center for Disease Control and Prevention. JAMA 2020.
12.

Guan WJ, Ni ZY, Hu Y, et al. Clinical Characteristics of Coronavirus Disease2019 in China. N Engl J Med 2020.
13.

Novel Coronavirus Pneumonia Emergency Response Epidemiology T. [Theepidemiological characteristics of an outbreak of 2019 novel coronavirus diseases(COVID-19) in China]. Zhonghua Liu Xing Bing Xue Za Zhi 2020;41:145-51.

This work was supported by Grant-in-Aid for Scientific Research (KAKENHI;
17H03597 and 16K14632) from the Japan Society for the Promotion of Science.
We thank Hiroyuki Asakura for suggesting epidemiological analyses of
COVID-19, Eitaro Ogawa for collecting samples, Toshio Hattori and Hiroaki Kitano forsuggesting the possibility of viral competition, Katsumi Kitagawa and Makoto Naruke
for discussions.

Author contribution
Y.K. and A.T. conceived and designed the study. Y.K. performed the sample collection.
A.T. performed the epidemiological and statistical analyses. Y.K. and A.T. wrote thepaper.

Competing interests
The authors declare no competing interests.

medRxiv preprint doi: https://doi.org/10.1101/2020.03.25.20043679.this version posted March 27, 2020. The copyright holder for thispreprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint inperpetuity.
It is made available under a CC-BY-NC-ND 4.0 International license .

Figure legends
Figure 1. SARS-CoV-2 Trend Curves in Japan Prefectures.
The number of influenza warning reports from November 11, 2019 to March 8, 2020
has been plotted. The positive rate (%) of RT-PCR for SARS-CoV-2 as of March 16,2020 is shown for each prefecture.

Figure 2. Correlation between Influenza in Kindergartens and SARS-CoV-2
Infection.
The correlation diagram shows the prevalence (%) of influenza-like illness inkindergartens in each district in Hokkaido and the number of individuals who were RTPCR positive for SARS-CoV-2. Spearman correlation coefficient ρ = -0.73.

Figure 3. Correlation between the Risk Score and COVID-19 Mortality.
Risk scores and case fatality rate (CFR) of COVID-19 in European countries calculated
from data in the Worldometer were plotted. Spearman correlation coefficient ρ = 0.65.

Figure 4. European Map.
Distribution of risk scores of various strengths. Data on influenza epidemics were notavailable for blank areas.

medRxiv preprint doi: https://doi.org/10.1101/2020.03.25.20043679.this version posted March 27, 2020. The copyright holder for thispreprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint inperpetuity.
It is made available under a CC-BY-NC-ND 4.0 International license .

Table1. Epidemic Data and Virus Subtypes.
Number of cases, the number of death, CFR (%): case fatality rate, virus subtype,%S (the % of S type virus), and epidemic curve are shown according to countries.
country

case

death

CFR (%) virus subtype%S

France

9134

264

2.9

L

0

Thailand

212

1

0.5

L

0

Germany

12327

28

0.2

L

0

Singapore

313

0

0.0

L

0

USA

9225

150

1.6

L, S

43

Korea

8413

93

1.1

L, S

50

Japan

825

27

3.3

L, S

60

Australia

596

6

1.0

L, S

60

UK

2626

104

4.0

S

100

Belgium

1486

14

0.9

S

100

Vietnam

76

0

0.0

S

100

epidemic curve

1.4

5
5

0
0

Saitama

8

3.5
3
2.5
2
1.5
1
0.5
0

3.9

6

4

2

0

Shizuoka

Fukuoka

0.6
5
4

8
7
6
5
4
3
2
1
0

8.6

Niigata

3.9

3
2
1
0

0.5

Kamikubo & Takahashi, Figure 1
20
19/
2 0 11 /1
19
1/1
2
2 0 /1 120/1
2 0 / 11
20
/2
/1
1
20
19
/1
2 0 1 /1
19
1
/1
2/
1
20
20 1
/1
2 0 / 11
20
/2
/1
1

10
10

10.2

20
19
/
2 0 11 /1
19
1
/1
2/
20
1
20 1
/1
2 0 / 11
20
/2
/1
1
20
19
/1
1/
11
20
19
/1
2/
11
20
20
/1
/1
1
20
20
/2
/1
1

15

20
19
/
2 0 11 /19 11/1
2
2 0 /120 1/
2 0 1/ 11
20
/2
/1
1

20
19
/
2 0 11 /19 11/1
2 0 2 /1
20 1/
2 0 1/ 1
20 1
/2
/1
1

20
19
/
2 0 11 /19 11/1
2
2 0 /1 120/
2 0 1/ 11
20
/2
/1
1

15

Hyogo
10
8
6
4
2
0

Osaka

10
8
6
4
2
0

5

4

20
19
/1
1/
20
11
19
/1
2/
11
20
20
/1
/1
20
1
20
/2
/1
1

Wakayama
20
19/
2 0 11 /19 11/1
2 0 2 /1
20 1/
2 0 1/ 1
20 1
/2
/1
1

Tokyo

5.4
20

5
4
3
2
1
0

20
19
/
2 0 11 /19 11/1
2 0 2 /1
20 1/
2 0 1/ 1
20 1
/2
/1
1

1.2
1
0.8
0.6
0.4
0.2
0

11.2

Hokkaido20
19/1
2 0 1 /1
19
1
/1
2/
1
20
20 1
/1
2 0 / 11
20
/2
/1
1

6
5
4
3
2
1
0

20
19
/
2 0 11
1 9 /1 1/1
2 0 2 /1
20 1/
2 0 1/ 1
20 1
/2
/1
1

20
19
/
2 0 11 /1
19
1
/1
2/
20
1
20 1
/1
2 0 / 11
20
/2
/1
1

20

20
19
2 0 / 11
1 9 /1 1/1
2 0 2 /1
20 1/
2 0 1/ 1
20 1
/2
/1
1

01
9/
1
2 0 1 /1
1
19
/1
2/
11
20
20
/1
2 0 / 11
20
/2
/1
1

Aichi
Chiba

4
3
2
1
0

6.2 65Kanagawa

2.5 3.5
3
2.5
2
1.5
1
0.5
0

Hiroshima

0.2 3.53

3

2

1

0

5.8

Kyoto

2.3

Ibaraki

0.0

2.5
2
1.5
1
0.5
0

SARS-CoV-2 (number of patients)

70

Sapporo City
60

50

40

30

Okhotsk
20

Ishikari

Kamikawa

Sorachi

10

Hiyama
0
0.0

5.0

10.0

15.0

20.0

25.0

30.0

35.0

40.0

Influenza-like illness (prevalence)
Kamikubo & Takahashi, Figure 2

45.0

50.0

10.0

Italy
8.0

CFR(%)

Ukraine
6.0

4.0

Netherlands

LatviaFinlandCzech Republic Norway
Belarus

-2.0

-90.0

-80.0

-70.0
Slovakia

FranceAlbania

Greece2.0

0.0-100.0Spain
UK
Hungary

Bulgaria
Germany

Estonia
-60.0

-50.0

risk score

Kamikubo & Takahashi, Figure 3

-40.0

-30.0

-20.0

-10.0

0.0

high
risk Risk score0-10-1-20-11-30-21-40-31-100-41
lowrisk

Kamikubo & Takahashi, Figure 4