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Personalized
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ArticleCausal Effect of the Tokyo 2020 Olympic and Paralympic Gameson the Number of COVID-19 Cases under COVID-19 Pandemic:
An Ecological Study Using the Synthetic Control Method
Norio Yamamoto 1,2,3, * , Toshiharu Mitsuhashi 4 , Yuuki Tsuchihashi 5 and Takashi Yorifuji 1
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Citation: Yamamoto, N.; Mitsuhashi,T.; Tsuchihashi, Y.; Yorifuji, T. Causal
Effect of the Tokyo 2020 Olympic andParalympic Games on the Number of
COVID-19 Cases under COVID-19
Pandemic: An Ecological Study
Using the Synthetic Control Method.
J. Pers. Med. 2022, 12, 209.

Department of Epidemiology, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences,
Okayama University, Okayama 700-8558, Japan; yorichan@md.okayama-u.ac.jp
Department of Orthopedic Surgery, Miyamoto Orthopedic Hospital, Okayama 773-8236, Japan
Systematic Review Workshop Peer Support Group (SRWS-PSG), Osaka 541-0043, Japan
Center for Innovative Clinical Medicine, Okayama University Hospital, Okayama 541-0043, Japan;
mitsuh-t@cc.okayama-u.ac.jp
Center for Field Epidemic Intelligence, Research, and Professional Development,
National Institute of Infectious Diseases, Tokyo 162-8640, Japan; yuuki@niid.go.jp
Correspondence: norio-yamamoto@umin.ac.jp; Tel.: +81-86-272-1211; Fax: +81-86-271-5554

Abstract: Previous studies have not assessed the causal effect of the Olympic Games on the spread of
pandemics. Using the synthetic control method and the national public city data in Japan recorded
from February to September 2021, we estimated the causal effects of the Tokyo 2020 Olympic andParalympic Games on the number of coronavirus disease 2019 (COVID-19) cases. The difference
between the number of COVID-19 cases in Tokyo and a counterfactual “synthetic Tokyo” (createdusing synthetic control method) after the opening of the Tokyo 2020 Games (23 July 2021) widened
gradually and then considerably over time. It was predicted that the Tokyo 2020 Games increased thenumber of COVID-19 cases in Tokyo by approximately 469.4 per 100,000 population from the openingof the event to 30 September. However, sensitivity analysis of the ratio of the pre- and post-gameroot mean square prediction errors using regression weights did not suggest robustness. Our results
showed that the Tokyo 2020 Games probably increased the number of COVID-19 cases even underpreventive regulations; however, the extent of this increase was difficult to estimate clearly due to an
overlap with the fifth wave associated with the Delta variant.

https://doi.org/10.3390/
jpm12020209
Academic Editors: Shabbir

Keywords: Tokyo 2020; Olympic and Paralympic Games; COVID-19; pandemic; synthetic control
method; causal effect; ecological study

Syed-Abdul and Yu Chuan Li
Received: 5 January 2022
Accepted: 29 January 2022
Published: 3 February 2022
Publisher’s Note: MDPI stays neutral
with regard to jurisdictional claims inpublished maps and institutional affiliations.

Copyright: © 2022 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms andconditions of the Creative Commons
Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

1. Introduction
The Olympic Games are a large-scale international sporting event, wherein many
international competitors and spectators visit the host country for a limited period. In thepast, the Olympic Games have been conducted during other pandemics; examples include
the 1920 Antwerp Olympics (held during the Spanish flu pandemic) and the 2016 Rio
Olympics (held during the Zika virus outbreak in Brazil). In 2021, the Tokyo 2020 Olympics
and the Tokyo 2020 Paralympic Games were held in Japan during the coronavirus disease2019 (COVID-19) pandemic [1]. During the Olympic Games, each host country needs todelegate public health resources to the event. Because pandemics cause severe medical
and financial difficulties throughout the world, the decision to host the Olympics under apandemic is an important political judgement. However, studies have not systematicallyanalyzed the association between the Olympic Games and the spread of infectious diseases.
Many researchers have attempted to objectively evaluate the various public healthpolicies that have been adopted to curb the spread of COVID-19. The synthetic control
method (SCM) is a method for policy evaluation [2,3]. In this method, a counterfactual
trend is created after considering the time trends and the demographic and geographic

J. Pers. Med. 2022, 12, 209. https://doi.org/10.3390/jpm12020209

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disparities; it is then compared with the actual trend to evaluate the effectiveness of thepolicy. Some policies under the COVID-19 pandemic, such as the implementation of
lockdowns, use of face masks, compulsory home quarantining, and mandatory COVID-19
vaccination certificates, have been evaluated using the SCM [2–7].
Estimating the influence of the Olympic Games on pandemics will provide important
insights that will enable future decisions on infection control policies and hosting the event.
Therefore, this study aimed to clarify the causal effect of the Tokyo 2020 Olympic Games on
the number of COVID-19 cases using the SCM. We hypothesized that the number of cases of
COVID-19 would increase due to the increased flow of people associated with the Olympic
Games and the decreased distribution of public health resources to COVID-19 control.
2. Materials and Methods
This ecological study was performed using national public city data in Japan. We
used SCM to clarify the causal effect of the Tokyo 2020 Games on the number of COVID-19
cases [2,8–10]. In SCM, the counterfactual outcome of the Tokyo 2020 Games not being held
was estimated from a weighted combination of donor pools. This study was performed inaccordance with the Declaration of Helsinki. It did not require ethical approval, because it
analyzed anonymous public data and not individual personal data.
2.1. Situation in Japan before the Tokyo 2020 GamesIn 2021, the Japanese government urged the public to avoid the “three Cs”, namely
crowded places, closed spaces, and close-contact settings. It also temporarily declared “astate of emergency” (not a lockdown) in prefectures associated with a continuing surge
of COVID-19 cases. Before the Tokyo 2020 Games, approximately 25% of the Japanese
population had been vaccinated by June 2021 [11]. The impact of COVID-19 in Japanwas relatively low; a cumulative 859,056 cases of infection and 15,110 deaths had beenrecorded in a total population of 125 million by 23 July 2021 [12]. On 23 July 2021, Japan
faced a strong “fifth wave” of COVID-19. Some coronavirus variants, such as the highly
transmissive Delta variant, have been spreading in Japan [13]. Globally, various infectious
variants of COVID-19 are spreading [14].
2.2. Situation during the Tokyo 2020 Games
After a one-year postponement, Tokyo hosted the Tokyo 2020 Olympics from 23 July
to 8 August 2021 and the Tokyo 2020 Paralympic Games from 24 August to 5 September
2021. Approximately 20,000 athletes and staff from more than 200 countries participatedin the events [15]. Therefore, some infection control strategies were implemented. Games
without spectators were held in the prefectures around Tokyo and Hokkaido. Most athletes
and staff members had been vaccinated and were frequently tested for COVID-19 [16]. In
accordance with the Olympic bubble system, they were restricted from moving outside thehotel and practice venue [16].
2.3. Study Period
This study was performed from 1 February to 30 September 2021. 1 February 2021
was defined as the beginning of the study period because the fourth wave of COVID-19
in Japan had started sometime between February and March 2021. The time lag between
virus exposure and COVID-19 occurrence is estimated to be 1–2 weeks [15,17]. Therefore,
30 September 2021 was defined as the end of the study period because the Paralympics
ended on 5 September and a few weeks had passed by then.
2.4. The Donor Pool
Abadie et al. defined donor pools as regions with similar characteristics to the region
exposed to the event of interest [2]. We constructed the counterfactual “synthetic Tokyo”
from a unit in the donor pool of prefectures in Japan. In terms of the influence of the
Olympics-related people flow into Tokyo from abroad, the inclusion criteria for the donor

J. Pers. Med. 2022, 12, 209

3 of 13pool mainly comprised the following: (1) distance from the host city (Tokyo) and the gamevenue and (2) presence of an advanced training camp. Advanced training camps for the
Olympics or Paralympics were held throughout Japan. Therefore, based on the distance,
we selected 29 prefectures west of Toyama, Gifu, and Aichi as the donor pool to attenuate
the effect of the Olympic or Paralympic Games (Figure 1).

Figure 1. Map of the 47 prefectures in Japan. Red—Tokyo (host city); Green—prefectures where theTokyo 2020 games were held; Blue—donor pool (29 prefectures west of Toyama, Gifu, and Aichi).

2.5. Outcomes
The primary study outcome was the daily number of newly confirmed COVID-19 casesin the Tokyo metropolitan area and in synthetic Tokyo after the Tokyo 2020 Olympic andParalympic Games. These patients were diagnosed and publicly announced in Japan [12].
However, the patients’ identities and information on whether this was their first or second infection episode were unknown. The 7-day moving average of the new cases wasevaluated as the smallest unit.
2.6. Variables
The social and economic variables analyzed for each prefecture comprised the proportion of the population over 65 years of age, proportion of the population aged between
15 and 64 years in 2019 [18], daily number of COVID-19 cases before the Tokyo 2020
Games [12], and proportion of the population vaccinated with the second dose on 22 July
2021 [11]. To determine the proportion of the variants of severe acute respiratory syndrome
coronavirus 2 in circulation from 19 July to 25 July 2021, we used polymerase chain reaction(PCR) assays and the following formula: (number of cases of the L452R mutation (Delta
variant) the PCR test was positive for)/(number of PCR tests performed for the L452R
mutation) [19].
2.7. Statistical Analysis
We evaluated the causal effect of the Tokyo 2020 Games on the number of COVID-19
cases using the SCM, as proposed by Abadie et al. [2]. We calculated the weight of the
no-event (control) region from the donor pool in a way that the average pre-event trend
and other selected variables were similar to the trend and characteristics of the event region(Tokyo). The weights were assigned based on the extent of the donor pool’s similarity toTokyo prior to the Tokyo 2020 Games. The weights from the donor pool were chosen as
follows: the weights for each prefecture ranged from 0 to 1, such that the weights for all
control prefectures totaled 1.
The following variables were used to determine the weights: number of new cases from
1 February to 22 July 2021, proportion of vaccinated individuals on 26 July 2021, proportion

J. Pers. Med. 2022, 12, 209

4 of 13of the variants detected from 19 July to 25 July 2021, proportion of the population aged
15–64 years, and proportion of the population aged 65 years and above. In total, weights
were calculated using 12 variables across 29 donor pools. The study period was divided
into the following eight sections based on the trends of the 7-day moving average of the
COVID-19 cases in Tokyo: end of the data 1 February, 22 July (the day before the opening
ceremony of Tokyo 2020), the nadir of the decreasing trend (valley) 8 March, 15 June, thetop of the increasing trend—13 May, middle days of the valleys—19 February, 10 April,
30 May, 4 July (Supplementary Figures S1 and S2). The weights for the donor pool werechosen as follows. X1 is the (12 × 1) vector representing the variables of the Kanto region
before the intervention; X0 is the (12 × 29) matrix signifying the variables of the control
pool before the intervention; and W is the (29 × 1) vector denoting the weight of the control
pool. The weight W = (w1, w2, . . . , w29) was selected to minimize ||X1-X0W||, subject
to 0 5 wj 5 1 for all j, and w1 + w2 + . . . + w29 = 1 (Figures S3 and S4).
In addition, the SCM was performed by using regression weights [8]. This approach
employs a linear combination for which the sum of the weights in the control pool is 1.
Therefore, the weights were calculated as:
Wreg = X0 0 (X0×0 0 )−1 X1In this approach, the weights ranged from less than zero to greater than one; thiswas different from the range of the synthetic control weights. While this allows us tocreate well-fitting synthetic controls, it may lead to extrapolation outside of the support ofthe data.
The causal effect was evaluated by the difference in the 7-day moving average of thenumber of new cases after the intervention (the Tokyo 2020 Games) between Tokyo andsynthetic Tokyo, as shown in the graph. The root mean square prediction error (RMSPE)
was calculated to evaluate the goodness of the weights.
Robustness was checked using the in-space placebo effect. The in-space placebo
effect was calculated by considering each prefecture in the donor pools as if it were theintervention area. This placebo effect expresses the variability of the results under the null
hypothesis. The ratio of the post-RMSPE to pre-RMSPE was calculated as the effect size;by comparing the ratio of Tokyo with those of the other prefectures, we could determine
whether the effect size of Tokyo was larger than the null hypothesis. In the regression
weights, the post-RMSPE-to-pre-RMSPE ratio could not be calculated because the preRMSPE was zero. Therefore, post-RMSPE was used for the comparison.
Categorical variables are expressed as numbers and percentages, while continuous
variables are expressed as means and standard deviations. There were no missing datain this study. The analysis was performed using Stata/MP4 17.0 with the user-generated
command “synth” 0.0.7 [20]. This calculation for regression weights was performed using
Python 3.7.4, with NumPy 1.18.1, Pandas 0.25.3, and Jupyter Notebook 6.0.2.
3. Results
Table 1 displays the synthetic control and regression weights of each prefecture inTokyo. The synthetic control weights indicated that a combination of the three prefectures,
namely Ishikawa, Osaka, and Okinawa (weights: 0.455, 0.277, and 0.267, respectively),
reproduced the COVID-19 case trends in Tokyo before the Tokyo 2020 Games.
Table 2 describes the demographic data of the synthetic control estimators before theTokyo 2020 Games. Most variables were adjusted to be well-balanced. The discrepancy in
the COVID-19 cases between Tokyo and synthetic Tokyo from 4 June to 22 July 2021, and in
the proportion of COVID-19 variants remains. The RMSPE of synthetic Tokyo (1.92) was
less than that of the population-weighted average of the control pool (2.39). The regression
weight of synthetic Tokyo was a perfect match for that of Tokyo.

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Table 1. Synthetic control and regression weights for Tokyo.
Prefecture

Synthetic
Control Weight

Regression
WeightPrefecture

Okayama
Tokushima
MiyazakiKagoshima
Shiga
KagawaHyogo
Wakayama
Oita
Hiroshima
Saga
Gifu
ToyamaNagasaki

0
0
0
0
0
0
0
0
0
0
0
0
0
0

−0.729−0.716−0.660−0.548−0.546−0.540−0.493−0.492−0.414−0.357−0.328−0.250−0.233−0.175

Kumamoto
Shimane
Aichi
Okinawa
Kochi
Nara
Ehime
Yamaguchi
Mie
Kyoto
Osaka
Fukui
Tottori
FukuokaSynthetic
Control Weight

Regression
Weight

0
0
0
0.267
0
0
0
0
0
0
0.277
0
0
0
0.455

−0.171−0.067−0.030
0.075
0.218
0.284
0.327
0.398
0.497
0.569
0.601
0.888
1.024
1.392
1.475

Table 2. Predictor means before the Tokyo 2020 Olympic and Paralympic Games.

New case per 100,000 persons
Mean between 1 February and 19 February 2021
Mean between 19 February and 8 March 2021
Mean between 8 March and 10 April 2021
Mean between 10 April and 13 May 2021
Mean between 13 May and 30 May 2021
Mean between 30 May and 15 June 2021
Mean between 15 June and 4 July 2021
Mean between 4 July and 22 July 2021
RMSPE of new COVID-19 cases
Related conditions
Proportion of the population having received
the second dose
Proportion of the COVID-19
variants in circulation
Proportion of people aged 15–64 years old
Proportion of people aged over 65 years oldTokyo

Synthetic
Tokyo

RegressionWeighted
Tokyo

Population-Weighted
Averageof the Control Pool

3.71
2.08
2.40
5.02
5.05
3.16
3.26
6.21
-

1.78
0.92
2.06
5.87
6.42
5.06
1.97
2.39
1.92

3.71
2.08
2.40
5.02
5.05
3.16
3.26
6.21
0.00

1.18
0.50
1.14
4.60
4.81
1.97
0.77
0.96
2.39

41.3%

42.4%

41.3%

44.2%

54.0%

18.9%

54.0%

27.1%

65.8%
23.1%

59.5%
27.0%

65.8%
23.1%

58.3%
29.2%

RMSPE—root mean squared prediction error; COVID-19—coronavirus disease 2019.

The COVID-19 case trend in synthetic Tokyo, estimated using synthetic control
weights, closely tracked the COVID-19 case trend in Tokyo during the entire period preceding the Tokyo 2020 Olympic and Paralympic Games (Figures 2 and 3). Although the ideal
setting was that the difference was zero before the opening of the Olympic Games, the difference was not regulated to zero before the opening of the Olympic Games (Figure 3). The
difference in the COVID-19 cases between Tokyo and synthetic Tokyo after the Olympics
were opened indicates the influence of the Tokyo 2020 Games on the number of COVID-19
cases. Soon after the 2020 Tokyo Olympics were opened, the gap between the two trendlines widened gradually and then considerably over time. The number of COVID-19
cases in Tokyo increased sharply, whereas that in synthetic Tokyo increased moderately.
The largest difference in the number of COVID-19 cases between the two was 13.2 per
100,000 population; this was observed on 19 August 2021 (27 days after the opening of the
Olympics; Figure 3). After the closing of the Paralympics, the difference sharply became
zero. Thus, the number of COVID-19 cases in Tokyo, from the opening of the Tokyo 2020

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Games to their closing on 30 September was predicted to have increased by approximately
469.4 cases per 100,000 population (Figures 4 and 5).

Figure 2. Trends in the daily number of newly confirmed COVID-19 cases in Tokyo and synthetic
Tokyo, estimated using synthetic control weights. The vertical line on 23 July 2021 indicates the
opening of The Tokyo 2020 Games.

Figure 3. Difference in the daily number of newly confirmed COVID-19 cases between Tokyo andsynthetic Tokyo, estimated using synthetic control weights.

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Figure 4. Trends of the cumulative daily number of newly confirmed COVID-19 cases in Tokyo andsynthetic Tokyo, determined using synthetic control weights.

Figure 5. Difference in the cumulative daily number of newly confirmed COVID-19 cases between
Tokyo and synthetic Tokyo, estimated using synthetic control weights.

Similarly, the COVID-19 case trend in synthetic Tokyo, determined using regression
weight, closely tracks the COVID-19 case trend in Tokyo throughout the period preceding
the Tokyo 2020 Olympic and Paralympic Games (Figures 6 and 7). Before the opening of
the Olympics, the difference was regulated to approximately zero (Figure 7). Soon after
the Tokyo 2020 Olympics were opened, the number of COVID-19 cases in synthetic Tokyo
increased, but decreased between 11 August and 31 August 2021. Therefore, the number of

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COVID-19 cases in Tokyo, from the opening of the Tokyo 2020 Games to their closing on 30
September was predicted to have increased by approximately 70.3 per 100,000 population(Figures 8 and 9).

Figure 6. Trends of the daily number of newly confirmed COVID-19 cases in Tokyo and synthetic
Tokyo, determined using regression weights.

Figure 7. Difference in the daily number of newly confirmed COVID-19 cases between Tokyo andsynthetic Tokyo, determined using regression weights.

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Figure 8. Trends of the cumulative daily number of newly confirmed COVID-19 cases in Tokyo andsynthetic Tokyo, determined using regression weights.

Figure 9. Difference in the cumulative daily number of newly confirmed COVID-19 cases between
Tokyo and synthetic Tokyo, determined using regression weights.

In the analysis for robustness, Tokyo ranked second among 30 prefectures in termsof the post-RMSPE determined using regression weight; this implies a large change in thedonor pools (Table S1, Figure 10). Conversely, Tokyo ranked 15th among 30 prefectures interms of the post-RMSPE-to-pre-RMSPE ratio determined using synthetic control weight;
this implied that a significant change did not occur among the donor pools. These findings
suggest that the estimates of the causal effects of the Tokyo 2020 Games on the number of
COVID-19 cases during the event may not be robust.

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Figure 10. Total results of the synthetic control weights and regression weights. RMSPE—root meansquare prediction error. Tokyo ranks 15th in the synthetic control weight analysis and second in the
regression weight analysis.

4. Discussion
This study estimated the causal effects of the Tokyo 2020 Games on the number of
COVID-19 cases using SCM. Our findings indicated that the Tokyo 2020 Games probably increased the number of COVID-19 cases, despite the implementation of preventive
regulations. However, the extent of the increase was difficult to estimate clearly, because
it coincided with the fifth wave associated with the Delta variant (which began aroundearly July).
We believe that the Tokyo 2020 Games would have contributed to an increase in thenumber of COVID-19 cases in Tokyo to some extent. In this study, SCM performed usingsynthetic control weights was more reliable than SCM performed using regression weights,
because it provided a smoother curve after the Tokyo 2020 Games. When regression weights
were used, the number of cases in synthetic Tokyo decreased between 11 August and
31 August 2021; however, this decrease was not observed in any other factual prefectures.
We speculate that this decrease was merely because the case numbers were calculated using
negative values of the weights. Therefore, we may have overestimated the results of theregression weight analysis. When SCM was performed using synthetic control weights,
a steep increase in the number of infections was observed 1–2 weeks after the opening of
the Olympics; this may have coincided with the influence of the Olympics and the onset of
COVID-19. However, the impact of the Tokyo 2020 Games remained uncertain, because thein-space placebo effect was not extremely large in Tokyo as compared to in the prefecturesin the donor pool.
There are three possible explanations for our results regarding the comprehensive
impact of the Tokyo 2020 Games. First, hosting the Olympic Games during the pandemic
conveyed mixed messages to the public. The “Olympic mood” may have made it easy toneglect infection control practices. For example, a significant increase in the number of
COVID-19 cases was observed 14 days after sporting events without strict face-mask requirements [21]. The behavior surrounding attendance at related events, rather than match
attendance, may have uniquely contributed to the number of COVID-19 cases [22]. Thismay be epidemiologically plausible, because sporting events with mass gatherings makepeople enthusiastic; this decreases adherence to preventive regulations. Second, due to therelatively decreased delivery of messages on COVID-19 prevention during the Olympics,
we believe that the public may not have been adequately informed of the preventive regu-

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lations. Third, the distribution of resources, including political and healthcare resources,
also decreases during the event. Experience gained from the management of previous mass
gathering events during the pandemic should have been applied here [21,22].
4.1. Strengths
This is the first study to analyze the causal effects of the Tokyo 2020 Games on the number of COVID-19 cases using SCM. By employing SCM, we were able to analyze the change
in the number of COVID-19 cases over time and match the trends in Tokyo and synthetic
Tokyo before the event [2,23]. Furthermore, the donor pool could be systematically selectedby calculating the weights [8]. This could address the problem that the comparison groups
were not sufficiently similar to each other in characteristics other than the intervention.
Finally, this study had transparency and validity, because we used national public data.
The results of this study can offer important insights for future decisions on hosting the
Olympics and international mass gathering events and framing policies during pandemics,
considering the socioeconomic benefits and burdens. This timely research on COVID-19
also provides a generalizable analytical framework for epidemiological evaluation.
4.2. Limitations
This study has several limitations. First, the variables were not easy to adjust, because
Tokyo is a megacity in Japan and has extreme values for some variables. In particular,
the percentages of the variant strains and COVID-19 cases before the opening of theTokyo 2020 Games could not be adjusted. The proportion of the Delta variant in the
COVID-19 cases in Tokyo rapidly increased from 21.5% (28 June to 4 July 2021) to 94.0%(23 to 29 August 2021) [24,25]. In synthetic Tokyo, the proportion of variant strains detected
was small; had it been high, the number of COVID-19 cases could have been higher thanthe calculated results in this study. Thus, we might have overestimated the causal effectsof the Tokyo 2020 Games. Second, the impact of the Tokyo 2020 Games may have also
been present in the prefectures of the donor pool. We selected 29 prefectures west of
Aichi based on their distance from Tokyo. However, it is possible that these prefectures
had been affected by Olympic-related factors, such as the presence of advanced training
camps and flow of people from overseas, leading to an overestimation of the number of
COVID-19 cases in synthetic Tokyo. Japan is a small island nation, and it was challenging
to select a donor that would be completely uninfluenced by the Tokyo 2020 Games. For
this reason, the causal effect of the Tokyo 2020 Olympic and Paralympic Games on thenumber of COVID-19 cases is underestimated. Third, measurement bias cannot be ruled
out; some COVID-19 cases may have been missed due to a limited testing capacity andlack of comprehensive testing of asymptomatic individuals. The proportion of COVID-19
variants was estimated in the number of tests performed, and not in the total number ofcases. Furthermore, the type of variant strain is unknown. Because COVID-19 cases caused
by Delta strains were spreading very widely in Japan in the period before the opening of the
Olympics, we wanted to at least adjust the proportion of the Delta variant; however, valid
data were difficult to obtain. This measurement bias is a non-differential misclassification
among all prefectures, leading to an underestimation of the COVID-19 cases. Fourth, there
were unmeasured meteorological factors, such as the air temperature, relative humidity,
and air quality, which significantly influence the spread and severity of pandemics [26]. In
addition, we could not adjust for time-varying variables after the opening of the Tokyo 2020
Games and for specific or original policies in each prefecture for COVID-19 suppression.
Alternatively, we adjusted for the number of COVID-19 cases over a long period before
the event. Because this number was affected by a variety of unmeasured factors, thisadjustment led to the minimization of the effect of unmeasured factors. Fifth, there was alack of external validity. Our results cannot be applied to other countries and infectious
diseases, because hosting the Tokyo 2020 Games under the COVID-19 pandemic is a uniquesituation. National characteristics, infectious diseases, and climatic conditions will change
the results for other Olympics events and host cities. Therefore, these results should be

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generalized after careful consideration of the settings. Finally, although there were some
limitations in this study, these limitations neither overestimated nor underestimated theevaluation of the impact of the Tokyo 2020 Games on the number of COVID-19 cases
totally. Therefore, our results will draw attention to the potential impact of the Olympics
on COVID-19 cases during the pandemic. Ultimately, a well-designed study with better
statistical methods is necessary to clarify the causal inferences.
5. Conclusions
The Tokyo 2020 Olympic and Paralympic Games probably increased the number of
COVID-19 cases, although it was difficult to adjust the influence of the fifth wave associatedwith the Delta variant. Further studies in different regions with more valid variables areneeded to validate our findings.
Supplementary Materials: The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/jpm12020209/s1, Figure S1: Eight sections based on the trendsof the 7-day moving average of COVID-19 cases in Tokyo; Figure S2: Workflow Figure 1. Publicdata files (number of new infections, vaccinations, population, and cases of the Delta variant) byprefecture were converted from Excel files to Stata dta files. Three do files were used: crDataset.do,
crDataset_population.do, and crDataset_variant.do. The converted Stata dta files were mergedusing the prefecture as the key. This was carried out by crMerge.do. For this file, we calculated
the proportion of population under 15 years old, the proportion of population 15–64 years old, andthe proportion of population that was vaccinated. This was carried out by crVariables.do. The file
was named Dataset.dta and saved. Next, we divided the study period based on the peaks andtroughs in the number of infections in Tokyo. In order to divide the periods, the boundary days wereidentified using anDetectPeek.do.; Figure S3: Workflow Figure 2. Normal SCM was performed with
anSCM_simple2.do. To perform the SCM with regression weights, long-type data were converted towide-type data. This was carried out using crConvert_LongtoWide.do. The converted file was output
as an Excel file. This file was named df_wide_for_regwt.xlsx. SCM with regression weights was
performed on this file. For this analysis, we used anRegWt_SCM2.py. Then, anRegWt_SCM2.ipynb
was run to obtain the in-space placebo effect. These files output the weights for each prefecture.;
Figure S4: Workflow Figure 3. Based on the weights, we drew a graph. We first merged the weights
and the number of newly infected people in anDraw_graph_for_excel_newcase.do, and then createda data file for drawing the graph. The data file for drawing is named sum_result_for_graph.dta. Next,
we used anDraw_graph_newcase.do to draw the graph.; Table S1: Post-RMSPE-to-pre-RMSPE ratio
determined using synthetic control weights or regression weights.
Author Contributions: Conceptualization, N.Y. and T.M.; methodology, N.Y., T.M., Y.T. and T.Y.;
software, T.M.; formal analysis, T.M.; resources, N.Y.; data curation, N.Y. and T.M.; writing—originaldraft preparation, N.Y. and T.M.; writing—review and editing, N.Y., T.M., Y.T. and T.Y.; visualization,
N.Y. and T.M.; supervision, T.Y.; project administration, T.Y.; funding acquisition, T.Y. All authors
have read and agreed to the published version of the manuscript.
Funding: This study was supported by a grant from Okayama City (no. 7402100053) for COVID-19
research projects.
Institutional Review Board Statement: The study was conducted in accordance with the Declarationof Helsinki. Ethical review and approval were waived for this study, because it used anonymous
public data and not an individual’s personal data.
Informed Consent Statement: Patient consent was waived because the data in this study wereanonymous. No information describing individual participants was obtained.
Data Availability Statement: All the materials (The raw data and code) needed for the analyses are reposited on GitHub (https://github.com/sankyoh/Tokyo2020_COVID19) (accessed on
31 January 2022).
Acknowledgments: We would like to thank all the local government officials who worked hardagainst COVID-19 and collected accurate data.
Conflicts of Interest: The authors declare no conflict of interest.

J. Pers. Med. 2022, 12, 209

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