Continuing to be Cautious: Japanese Contact Patterns during the COVID-19 Pandemic and their Association with Public Health Recommendations

Continuing to be Cautious: Japanese Contact Patterns during the COVID-19 Pandemic and their Association with Public Health Recommendations | medRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-P4HH5NV'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search Continuing to be Cautious: Japanese Contact Patterns during the COVID-19 Pandemic and their Association with Public Health Recommendations View ORCID Profile Tomoka Nakamura , View ORCID Profile Ryo Kinoshita , View ORCID Profile Akira Endo , Katherine E. Atkins , Hitoshi Oshitani , Yoko Ibuka , View ORCID Profile Motoi Suzuki , Koya Ariyoshi , Kathleen M. O’Reilly doi: https://doi.org/10.1101/2025.04.17.25326019 Tomoka Nakamura 1 Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine , London, United Kingdom 2 School of Tropical Medicine and Global Health, Nagasaki University , Nagasaki, Japan 3 Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine , London, United Kingdom Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Tomoka Nakamura For correspondence: Tomoka.Nakamura{at}alumni.lshtm.ac.uk Ryo Kinoshita 4 Center for Infectious Disease Epidemiology, National Institute of Infectious Diseases, Japan Institute for Health Security , Tokyo, Japan Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Ryo Kinoshita Akira Endo 1 Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine , London, United Kingdom 2 School of Tropical Medicine and Global Health, Nagasaki University , Nagasaki, Japan 3 Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine , London, United Kingdom Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Akira Endo Katherine E. Atkins 1 Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine , London, United Kingdom 3 Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine , London, United Kingdom 5 Centre for Global Health, Usher Institute, Edinburgh Medical School, University of Edinburgh , Edinburgh, United Kingdom Find this author on Google Scholar Find this author on PubMed Search for this author on this site Hitoshi Oshitani 6 Department of Virology, Graduate School of Medicine, Tohoku University , Miyagi, Japan Find this author on Google Scholar Find this author on PubMed Search for this author on this site Yoko Ibuka 7 Faculty of Economics, Keio University , Tokyo, Japan 8 Department of Economics, University of Hawaii at Manoa , Hawaii, United States of America Find this author on Google Scholar Find this author on PubMed Search for this author on this site Motoi Suzuki 2 School of Tropical Medicine and Global Health, Nagasaki University , Nagasaki, Japan 4 Center for Infectious Disease Epidemiology, National Institute of Infectious Diseases, Japan Institute for Health Security , Tokyo, Japan Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Motoi Suzuki Koya Ariyoshi 2 School of Tropical Medicine and Global Health, Nagasaki University , Nagasaki, Japan 9 Department of Clinical Medicine, Institute of Tropical Medicine, Nagasaki University , Nagasaki, Japan Find this author on Google Scholar Find this author on PubMed Search for this author on this site Kathleen M. O’Reilly 1 Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine , London, United Kingdom 3 Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine , London, United Kingdom Find this author on Google Scholar Find this author on PubMed Search for this author on this site Abstract Full Text Info/History Metrics Data/Code Preview PDF Abstract Despite implementing no lockdowns and having a large elderly population, Japan had a low mortality rate due to COVID-19 compared to Europe and North America. The extent to which policies impacted person-to-person contact remains unclear. In this study, we examined changes in contact patterns and their association with behaviors and governmental recommendations in Japan during the pandemic. Ten social contact surveys were conducted between 2021 and 2023 reaching over 1500 participants per survey in Osaka and Fukuoka prefectures where governmental recommendations were first implemented due to high COVID-19 incidence. Their contact patterns were assessed through their demographic characteristics, COVID-19 vaccination status, and individual disease mitigation measures. Generalized linear models were used to identify factors associated with increased contacts. The mean number of contacts during the pandemic declined by at least 49.8% (8.2 weekday contacts and 6.0 weekend contacts per individual, adjusted by age and sex) compared to a study conducted prior to 2020. Weekdays, occupation, larger household sizes, and mask wearing were associated with a higher number of contacts. The frequency and duration of contacts were negatively associated with the issuance of COVID-19 governmental measures, yet the relative change in contacts was not as prominent as pre- and post-lockdown situations in the United Kingdom. There was a gradual increase in contacts with time and less strict public health recommendations. Yet, contacts that did not increase with uptake of COVID-19 vaccination and continuous mask wearing depict cautious behavior across the survey population during the pandemic and into 2023. These results are in contrast with European countries where contacts largely increased among vaccinated individuals compared to the non-vaccinated. Social contacts are country and context specific, highlighting the need for data collection across different communities. Background The reported cases and deaths due to coronavirus disease 2019 (COVID-19) in Japan have been much lower compared to Western countries such as Europe and North America. For comparison, as of 28 June 2023, there were 74,694 cumulative deaths in Japan in a total population of 125.5 million (59.5 deaths per 100,000) while there was a total of 227,524 deaths in the United Kingdom with a population approximately half of Japan (337.9 deaths per 100,000) ( 1 ). Japan is also unique having the second largest aging population in the world, after Monaco, (29.9% of the total population who are 65 years and above) ( 2 ) who are at a higher risk of COVID-19 mortality. Throughout the pandemic from 2020 to early 2022, cumulative confirmed COVID-19 cases (adjusted by population size) were reported 10 to 15 times higher in the United States and United Kingdom compared to Japan ( 1 ). Several factors, such as timing of government regulations and behavioral change interventions, have been discussed as potential determinants of a successful epidemic control ( 3 ). Apart from strict international border control policies, Japan relatively has had less strict rules compared to many other countries. Though public health emergency declarations (EDs) with different levels of strictness were issued, neither lockdowns nor curfews were implemented. National school closure due to COVID-19 regulation spanned for at least three weeks in March 2020, but most schools reopened between April and May 2020 ( 4 ). Yet, there were key messages that continued to be addressed to the public, such as avoiding the “3Cs” which stands for settings that are closed, crowded and close contact ( 5 ). Reducing person-to-person contact has been one of the key tactics in epidemic control as it directly shapes the risk of transmission of respiratory viruses ( 6 ). To quantify these contact patterns, mobile phone data ( 7 ) and synthetic contact matrices ( 8 ) have been used. During the COVID-19 pandemic in Japan, mobile device data was utilized to investigate how governmental interventions could have impacted mobility ( 9 , 10 ). Social contact survey data is another important input for epidemiological and mathematical models of infectious diseases. It has its advantages over mobile device data as it can capture changes in contact patterns with respect to age and sex of both survey participants and their contacts. Additionally, disease mitigation measures, such as vaccination and handwashing, can be linked with contact data. Social contact surveys have been well-utilized prior to the pandemic, such as POLYMOD in Europe ( 11 ) and in Japan ( 12 , 13 ). They showed that contact patterns are highly dependent on age, gender, household size and day of the week ( 14 ). These studies provide a baseline of contact patterns prior to any physical distancing or public health related measures. To elucidate the changes in contact patterns relevant in the transmission of SARS-CoV-2 in Japan, we conducted 10 repeated cross-sectional surveys from 2021 to 2023 ( Figure 1a , 1b ). In this paper, we aim to describe the changes in social contacts and other behaviors, such as hand hygiene and mask wearing, during the pandemic and into 2023. As different levels of EDs were issued and lifted with time, we evaluated the association of these governmental measures on social contacts. We also present a statistical model that explores individual characteristics and behavior that impact the frequency of contacts. Download figure Open in new tab Fig 1a: Number of daily and cumulative reported COVID-19 cases in Japan with the timing of the social contact surveys in this study and introduction of SARS-CoV-2 variants. The dashed vertical lines indicate each time point when the contact surveys were conducted beginning from February 2021 to February 2023. The darker pink rectangles indicate the periods when emergency declarations were issued in Osaka and Fukuoka. The lighter pink rectangle indicates the period of semi-emergency declaration that was issued in Osaka and Fukuoka. In Fig 1a , the green arrows indicate the beginning of the transmission period when at least 50% of the genome sequenced COVID-19 cases was due to specific variants of SARS-CoV-2. In Fig 1b , the purple arrow indicates the start of the Tokyo Summer Olympics in 2021. Navy arrows indicate national holidays in Japan. Light green arrows indicate the dates of mass vaccination that occurred sequentially based on priority groups. Download figure Open in new tab Fig 1b: Number of daily and cumulative reported COVID-19 deaths in Japan with the timing of the social contact surveys in this study and key events related to COVID-19. Methods Survey design Ten contact surveys, in which the participants were asked to report their individual characteristics and contact patterns, were conducted in Osaka and Fukuoka prefectures, Japan in 2021–2023 (Figure 1). Osaka prefecture is located on the west of mainland Japan and has the third highest population next to Tokyo and Kanagawa prefectures ( 15 ). Fukuoka prefecture is on Kyushu island, which is south of mainland Japan, and has the ninth highest population ( 15 ). In 2021, three EDs were issued across Japan including Osaka and Fukuoka during which two surveys (#1 and 3, Figure 1) were conducted. During the height of the BA.1 (omicron) transmission in 2022, a semi-ED was issued, which consisted of a less strict recommendation than an ED (Suppl. Table 1) ( 16 ). Two surveys (#7 and 8, Figure 1) were conducted during this time. Six surveys (#2, 4, 5, 6, 9, and 10, Figure 1) were conducted when ED was absent with the last survey in February 2023. The timing of the surveys was semi-strategic; they were planned during expected changes in contact patterns and funding availability. Survey participants were recruited by a Japanese online survey company (F-press). Participants included anyone at least 18 years old residing in either Osaka or Fukuoka prefecture who agreed to participate through written informed consent. Children under 18 years old participated with the consent of their guardians/parents who recorded their information on their behalf. Every individual in each household can fill out the survey as a participant, and all these individuals can be linked by household. The number of participants per survey was a minimum of 1,500, powered to detect a difference in contact numbers of 2.5 between pairs of observations with a 90% power and 5% Type I error and 20% loss to follow-up. The survey participants were compensated 300 Japanese yen per survey and were able to participate in as many surveys as they opted for between 10 February 2021 and 12 February 2023. The survey was adapted from the UK CoMix study ( 17 – 19 ) in Japanese to capture the daily frequency and type of contacts. In each survey, they recorded their contacts during one weekday and one of the days during the weekend. A “contact” was defined as physical or non-physical: physical contact included handshakes, hugging, kissing, and playing a contact sport while non-physical contact was defined as facing another individual (with or without mask wearing) and exchanging at least three Japanese sentences to each other in a conversation. The participants were asked to record their contacts in a diary format with instructions a week prior to the day when the survey was implemented. They reported their contacts including their age, sex, type of contact (non-physical and/or physical), location of contact, approximate time of contact, and whether the contact was made indoors and/or outdoors (Supp. Table 1). These specified contacts were for the first 10 contacts. Those who reported more than 10 contacts were asked to approximate the number of contacts, location of contacts, and their age categories. Participants were asked to report their individual characteristics as well as their individual preventive behavior such as frequency of mask wearing, handwashing, and teleworking (Supp. Table 1). They were asked COVID-19 related questions including their history of having tested PCR positive for COVID-19, their concern towards getting infected with COVID-19 (rank from one to five), and their vaccination status. Statistical analysis For each survey, we calculated the mean and median of contacts during the weekday and weekend, mean and median age, COVID-19 vaccine coverage, frequency of mask wearing, and the proportion of survey participants who tested COVID-19 positive. The population estimates of the mean contacts and vaccination coverage were adjusted by age and sex based on the 2021 October census ( 15 ). We compared our data to contact patterns analyzed by Ibuka et al. ( 12 ) as baseline data prior to the COVID-19 pandemic. Their survey methodology was similar to ours; the data included all reported contacts per individual during the week as well as the duration and location of contacts. Their contact surveys were also conducted with an objective to understand influenza-related behaviors which is relevant in the context of respiratory disease transmission. For our study, contact patterns were stratified by weekday and weekend and compared across eight age groups (0-9, 10-19, 20-29, 30-39, 40-49, 50-59, 60-69, and 70+) and across the study period. Measures of uncertainty in age-specific contact numbers and duration were obtained using the bootstrap; the mean and 95% confidence intervals (CI) were obtained by sampling with replacement for 1000 times. Consistent with previous contact survey studies, we truncated the total number of contacts to avoid a few observations with hyperinflated contact numbers affecting age-specific summary statistics ( 11 , 19 ). We selected a truncation cutoff of 250 through a visual check by plotting the mean number of contacts with a range of cutoff points using a Weibull distribution. (Supp. Fig 1). To investigate factors associated with the reported number of contacts (February 2023 survey only), we used a multivariable regression assuming a Weibull distribution with log link function. This distribution was selected to address the coefficient of variation in reported contacts and fit the right skewed distribution of contacts better than a negative binomial distribution. The dependent variable was the reported contacts, and 15 variables were selected from a list of variables (Supp. Table 1) as the model covariates based on a hypothesis-driven approach. The model was referenced on a 40-49 year old individual who lives with two others works as a company employee at their workplace, and fully vaccinated against COVID-19 with at least 3 or more doses. The participants’ age was retained in the model as it could be a potential confounder. All other covariates that were selected in the multivariable model were associated ( p < 0.05) with the number of contacts in an univariable model. The multivariable model was developed using both forward and backward stepwise selections until the Akaike Information Criterion (AIC) had no further improvement. The incidence rate ratios calculated from the regression model are referred here as contact rate ratios (CRR) where the relative mean number of contacts per day is compared to the reference of each covariate. Model fit was examined with residual plots and comparing predicted vs. observed data. When analyzing summary statistics of contacts across the various time points and age categories, the Kruskal-Wallis test was used. When comparing two nested generalized linear models to test the significance of a variable, likelihood ratio test was used. All statistical analyses were done in R version 4.0.3, using the packages “MASS”, “survival”, “tidyverse”, “dplyr”, “reshape2”, “scales”, “rstatix”, “ggplot2”, and “cowplot.” Results Comparison of contact survey participants with national data A range of 1513 to 1721 participants recorded their contact patterns between February 2021 and February 2023 in Fukuoka and Osaka prefectures, Japan ( Table 1 ). Depending on the survey, the mean age of the participants was between 45 and 48 years old. Between 52 to 54% of the participants were female. As of the 2021 national census, the mean age of the Japanese population was 48.4 and 51.3% being female ( 15 ). COVID-19 vaccine coverage among the survey participants increased with time. In Japan, healthcare workers were first vaccinated in February 2021, followed by the older population (≥65 years) from March 2021, and the rest of the population from June 2021 ( 20 ). Vaccine coverage reported in February 2023 was slightly higher among the survey participants compared to the national vaccine coverage as of February 2023 ( 21 ). Compared to national vaccine coverage reported in January 2023, vaccine coverage across all age groups were slightly higher among the survey participants in February 2023 (Supp. Figure 6). The percent of individuals and/or household members having tested COVID-19 positive increased from 0.3% (5/1721) in February 2021 to 8.7% (136/1569) in February 2023. A serosurvey (N = 5627) conducted between 3 rd February and 4 th March 2023 showed 35.8% of Osaka residents and 31.3% of Fukuoka residents tested positive with infection-induced antibodies ( 22 ). Our survey appropriately approximated the national statistics in terms of demographics but with a slightly higher vaccination coverage and lower percentage of people who tested COVID-19 positive. View this table: View inline View popup Download powerpoint Table 1: Characteristics of participants across all the contact surveys conducted in Fukuoka and Osaka prefectures from February 2021 to February 2023 compared to pre-pandemic (baseline) contact patterns and the Japanese national census data. Comparison with pre-pandemic contact patterns The mean number of contacts during the pandemic reduced by 49.8% during the weekday and by 53.3% during the weekend when compared to pre-pandemic times ( 12 ); we report an average of 8.18 weekday contacts and 5.98 weekend contacts per individual. The sample distribution of contacts was right-skewed ( Figure 2 ) where 60.2% of the participants contacted less than five individuals per day during the weekday (and 76.5% during the weekend) in February 2023. The sample distribution of contacts from the earlier surveys showed a similar distribution (Supp. Fig 2). Prior to the pandemic, the distribution of daily reported contacts (N=3146 participants) was also right-skewed and less than 2% of contacts reported zero contacts ( 12 ). Download figure Open in new tab Fig 2: Distribution of contacts reported per individual during the weekday in Fukuoka and Osaka prefectures based on the contact survey conducted in February 2023 among a total of 1569 participants. The blue bars show the observed contacts reported from the contact survey. The pink bars show the predicted contacts based on the multivariable regression model using a Weibull distribution. Sixty participants (3.82%) recorded over 50 contacts. Change in contact patterns across a typical week To test whether there were substantial differences in frequency of contacts across a typical week, two generalized linear models with and without survey time were compared using a likelihood ratio test. Survey time point significantly explained the variability in the mean of contacts during the weekday (chi-squared = 29.33, p-value = 0.00057) ( Figure 3a ) and during the weekend (chi-squared = 98.79, p-value < 2.2 x 10 -16 ) ( Figure 3b ). Particularly among the 10’s and 40’s, the mean number of contacts increased in calendar time during the weekday and weekend. Fig 3: Frequency (mean) of contacts by age category of participants during the weekday ( Fig 3a ) and the weekend ( Fig 3b ) for all surveys conducted (2021-2023) in Fukuoka and Osaka prefectures indicated by each color. The mean and 95% confidence intervals are obtained by bootstrapping. Download figure Open in new tab Fig 3a: Weekday Contacts 2021-2023 Download figure Open in new tab Fig 3b: Weekend Contacts 2021-2023 On the contrary, the older population (70+) consistently had low contacts during the weekday and weekend. Particularly during the weekdays, the mean number of contacts across all survey time points (February 2021-February 2023) was significantly lower amongst the 70+ year old compared to the 40’s (chi-squared = 112.65, p-value < 2.2 x 10 -16 ). There were clear temporal changes in the duration of contacts in the 40’s during the weekday (chi-squared = 49.47, p-value = 1.35 x 10 -7 ) ( Figure 4a ) and during the weekend (chi-squared = 54.04, p-value = 1.86 x 10 -8 ) ( Figure 4b ). For example, the mean duration of weekday contacts for the 40’s increased from 6.10 hours (95% CI: 5.29-6.85) in February 2021 to 8.63 hours (95% CI: 7.75-9.48) in February 2023. There were similar temporal changes in the duration of contacts amongst the 70+ year old (chi-squared = 21.04, p-value = 0.012 for weekday; chi-squared = 25.51, p-value = 0.0025 for weekend). Fig 4. Duration of contacts by age category of participants during the weekday ( Fig 4a ) and weekend ( Fig 4b ) for all surveys conducted (2021-2023) in Fukuoka and Osaka prefectures indicated by each color. The mean and 95% confidence intervals are obtained by bootstrapping. Download figure Open in new tab Fig 4a. Weekday Contacts 2021-2023 Download figure Open in new tab Fig 4b. Weekend Contacts 2021-2023 Association of Emergency Declarations (ED) and Semi-ED We explored whether EDs (observed during surveys 1 and 3) were associated with reported contacts and their duration, and whether the strength of ED provided further granularity. After adjusting for age and sex in a generalized linear model, the issuance of an ED was negatively associated with the mean number of contacts during weekends (Adjusted CRR: 0.84 (95% CI: 0.79-0.88) compared with periods without any ED, yet there was no association during weekdays (Adjusted CRR: 0.96 (95% CI: 0.91-1.01). On the other hand, there was a slightly positive association between the issuance of a semi-ED (surveys 7 and 8) with contacts during weekends (Adjusted CRR: 1.06 (95% CI: 1.01-1.12)) and weekdays (Adjusted CRR: 1.05 (95% CI: 1.00-1.11)) compared with periods without any ED. For example, a woman in her 40’s would contact an average of 3.32 individuals (95% CI: 3.09-3.56) during weekends when an ED was issued, but she would contact an average of 3.97 individuals (95% CI: 3.74-4.20) during periods when any level of ED was absent ( Table 2 ). Since surveys 9 and 10 were conducted two years after the initial survey and could have significantly different contact patterns due to “pandemic fatigue,” the same analysis was conducted by excluding these two surveys, but the negative association between ED and contacts remained the same. View this table: View inline View popup Download powerpoint Table 2: Predicted frequency and duration of contacts for a woman in her 40’s during the weekday or weekend. Each row is based on a multivariable regression model that included age, sex, and the level of Emergency Declaration (ED) that was either absent, semi-ED or full ED. Each model includes all 10 survey time points from February 2021 to February 2023 (N=16,178 individuals). When surveys 1 and 3 (ED issued) were compared with survey 2 (ED absent) after adjusting for age and sex, there was no evidence of an association between the frequency of contacts and the issuance of an ED during the weekday (Adjusted CRR: 1.03 (95% CI: 0.95-1.12) or the weekend (Adjusted CRR: 0.95 (95% CI: 0.88-1.04). The issuance of an ED was negatively associated with the duration of contacts during both weekdays (Adjusted CRR: 0.90 (95% CI: 0.86-0.95)) and weekends (Adjusted CRR: 0.87 (95% CI: 0.82-0.91)) after adjusting for age and sex. Following the same example given previously, a woman in her 40’s would have an average contact duration of 5.09 hours (95% CI: 4.76-5.43) with other individuals during weekends when an ED was issued and 5.86 hours (95%CI: 5.55-6.18 during periods without any ED. Because one of the key restrictions during ED was either complete closure or shortening of restaurant/bar hours with restricted hours of serving alcohol (Supp. Table 2), we investigated the number of individuals who reported contacts at restaurants/bars. For this analysis, the contact location of restaurants and bars were combined which included izakaya—informal Japanese bars where alcohol and food are served—karaoke, and movie theatres. After adjusting for age and sex, these contacts were negatively associated with the issuance of ED during weekdays (Adjusted CRR: 0.77 (95% CI: 0.72-0.83)) and weekends (Adjusted CRR: 0.68 (95% CI: 0.63-0.74)) compared to periods when ED was absent. Factors associated with contact patterns Based on our multivariable regression model, an individual with the reference characteristics had an expected 2.35 contacts (95% CI: 1.70-3.23) during the weekday. Each variable was compared to this reference, and we reported on characteristics that were statistically different to this value ( Table 3 ). To check the model fit, the predicted values of weekday contacts were plotted against the observed values reported from the February 2023 contact survey ( Figure 2 ). Residual plots were also evaluated (Supp. Fig 5). Heteroscedasticity was evident (Supp. Fig 5 a-b) which was expected due to the sample distribution showing overdispersion. Model fit was good as the error terms of each predictor in the model showed approximate linearity (Supp. Fig 5 c-f). View this table: View inline View popup Table 3: Characteristics of participants and their reported number of weekday contacts from the February 2023 contact survey conducted in Fukuoka and Osaka prefectures compared to the reference category. The relative mean number of contacts per day is indicated as the contact rate ratio (CRR). The crude CRR is from a univariate regression model and the adjusted CRR is from a multivariable regression model. Rows highlighted in yellow are the variables that were associated (p<0.05) with the mean number of contacts in the multivariable regression model. Contact patterns varied by location ( Table 3 ). Participants who reported contacts at home, school, restaurants, and bar settings during the weekday had higher contacts than those who did not report any contacts at these locations. Those who lived in a household of four or five people had higher contacts than those who lived in a household of three people. The association between contacts and occupation differed depending on the occupation type where healthcare professionals and government employees, including public school teachers, had significantly higher contacts. Different work conditions were associated with contact patterns. While those who teleworked had lower contacts, those who moved to a different prefecture at least six times in the past month for work/school purposes had higher contacts. As of February 2023, among those who reported as employed or attending school, 73.2% (818/1118) reported they have never teleworked from home. Disease mitigation measures, such as mask wearing frequency increased significantly with higher number of contacts, but there was no increasing trend of handwashing frequency with more contacts. There was no evidence of association between contacts and vaccination status and their level of concern in getting infected with COVID-19. Note that among those who reported their frequency of teleworking, there were 451 people (28.71%) who reported not employed and there were 15 people (0.96%) who reported NA on their vaccination status (not shown on Table 3 ). Discussion Our study is one of the few that have characterized social contact patterns in Japan during the COVID-19 pandemic through repeated cross-sectional surveys. Contact surveys were conducted in Japan during and after the Tokyo Olympics in August 2021 that resulted in having a median of three contacts per day (mean of 8.92) ( 23 ), similar to our results from the March 2022 survey. Although our surveys did not capture contact patterns in 2020, a study using mobility data has shown that there was a 70% reduction in daily total contacts in April 2020 after there were government recommendations to telework and close the public schools ( 24 ). This is comparable to countries such as Norway (67-73% decrease) ( 25 ), France (70% decrease) ( 26 ), Germany (73% decrease) ( 27 ), United Kingdom (74% decrease) ( 17 ), Belgium (80% decrease) ( 28 )and United States (82% decrease) ( 29 ) where strict lockdowns were implemented. Many contact patterns remained subdued even after post-lockdowns (e.g. China, 21 European countries) ( 30 , 31 ). Although Japan never implemented lockdowns, our results suggest that the objective of ED issuance was met as it was associated with changes in frequency and duration of contacts. Particularly during the weekend, there were fewer contacts and shorter duration of contacts. This suggests that individuals may have changed their behavior during the days when they have more control over compared to the weekdays when they either commute to school or work. Even as of February 2023, two years after the second ED, the majority (73.2%) of those who were employed or attending school had never worked from home. This corresponds to a survey that showed among approximately 20,000 employed individuals, 70.3% were not teleworking during the first ED in April 2020 when stay-at-home and teleworking recommendations were issued for the first time during the pandemic ( 32 ). Prior to the pandemic, teleworking was a practice rarely implemented in Japanese society; a national survey in September 2019 showed 20.2% of 2,118 companies had implemented teleworking strategies for their employees ( 33 ). The duration of contacts increased with time particularly among children, teenagers, and adults in their 40s. This is correlated with governmental restrictions that were gradually lifted, such as having less stringent measures during semi-EDs compared to EDs. However, the mean number of contacts remained subdued across the first three surveys in 2021, including March when ED was absent, showing that the lifting of ED was not what may have influenced the increase in contact patterns per se. These can be signs of pandemic fatigue where there may be a gradual shift towards behavior more like pre-pandemic times. It is also worth noting that there were other factors that also increased with time, such as vaccination and circulation of omicron, a less virulent strain of SARS-CoV-2. It is, however, important to note that even after ED was lifted in March 2021 before mass vaccination started, the frequency and duration of contacts did not change. This contrasts with periods when the first and second lockdowns were lifted in the UK, also prior to vaccine rollout to the entire public, adults (18 years and above) increased their contacts from 29 to 59% compared to during the first lockdown ( 19 ). These empirical data illustrate how contact patterns can vary from country to country during a pandemic. Understanding how individuals behave, especially during periods prior to vaccine rollout, may provide hints on the level of necessity, timing, and severity of governmental recommendations in future epidemics. The Japanese population that had consistently low number of contacts was those over 70, which consist of 22.6% of the national population ( 15 ). They not only met with fewer than five contacts during the week, but each contact remained short (average of one hour of contact per day) with very limited physical contact throughout the pandemic (Supp. Fig 3a). Their contact patterns also did not change after mass vaccination started from May 2021 for 65 years and above. With the combination of shielding the older populations in long-term care facilities ( 34 ), this may have helped in preventing a surge of COVID-19 outbreaks and deaths amongst this population during the beginning of the pandemic—an issue that was apparent in the US ( 35 ) and the UK ( 35 , 36 ). “Prosocial behavior,” such as physical distancing, mask wearing, and getting tested as described by Sachs JD et al., ( 3 ), is critical in pandemic control, and countries in WHO’s Western Pacific region were quick in encouraging it as part of their “suppression strategy” ( 3 ). Our results in Japan reflect this. In addition to EDs, the 3C policy was implemented from early 2020 which was especially relevant in the Japanese context as our results showed that indoor contacts were more frequent than outdoor contacts (Supp. Fig 4a and 4b). Individuals who went to restaurants and bars had higher contacts compared to those who did not. The close contact that is likely in these settings highlight the importance of mitigation approaches such as improved ventilation for disease control ( 37 ). In our surveys there were increased hours of mask wearing with higher number of contacts, demonstrating prosocial behavior. Frequency of mask wearing continued to be stable throughout 2021 to 2023 which contrasts with the UK where mask wearing was strongly associated with changes in government policy ( 19 ). On the other hand, handwashing was not associated with the frequency of contacts. This may be due to the public message of 3C where avoiding crowded conditions and close-contact settings were highlighted in Japan more than handwashing and disinfection—another key difference compared to other countries ( 5 ). Contacts were not associated with vaccination status, opposite from the European countries where vaccinated individuals reported higher contacts ( 38 ) This could be due to Japan not implementing any restrictions in accessing public or indoor areas due to vaccination status, unlike in Europe where vaccine certificates were issued. Such differences suggest behavioral patterns that are triggered by risk perception vs. governmental intervention. The number of deaths increased after the introduction of the omicron variant, but the suppression strategy in Japan from 2020-2021 – a combination of reduced contacts, prosocial behavior, and high vaccination coverage – may have helped in keeping a low cumulative mortality like other Western Pacific countries where the suppression strategy was implemented early ( 3 ). There are limitations of our study. Our surveys were limited to Osaka and Fukuoka prefectures, where incidence has been typically higher than the remaining 45 prefectures in Japan. Extrapolating our findings to other prefectures in Japan should be done with caution, as contact rates may be lower. However, Japanese contact survey studies prior to the COVID-19 pandemic by Ibuka et al. ( 12 ) and Munasinghe et al. ( 13 ) did not report any differences in contact rates across prefectures, while Tsuzuki et al. ( 23 ) examined contact rates between rural and urban areas and did not find any meaningful differences. We can therefore have moderate confidence in the relevance of our findings for the rest of the country. Selection bias could have been introduced as the surveys captured a sample population that had a slightly higher vaccination coverage than the national average. Recording contacts retrospectively may result in recall bias, leading to an underestimation of contacts. Lastly, due to the limited number of questions that could be asked in each survey, our multivariable regression model could have included covariates that were not included in the model, leading to residual bias. Some of these variables could have impacted contact patterns with time. Thus, future work is planned to investigate factors that could have influenced the change in contacts during the pandemic by analyzing the same individuals across multiple time points. Compared to other countries, Japan has been unique in tackling the COVID-19 pandemic without implementing lockdowns and long school closures; it mostly relied on recommendations that were not legal enforcements. Prosocial behavior, such as limiting contacts especially during the weekend in 2021 and continued mask wearing until 2023 were evident. Although issuance of EDs occurred as the pandemic progressed in waves, contacts remained subdued compared to pre-pandemic times. By recognizing some of the key factors that influence contact patterns in Japan, it can contextualize mathematical models of SARS-CoV-2 that are developed to understand the transmission dynamics and disease. Behavior can be difficult to predict and contact patterns may differ in a future pandemic. The age-specific contacts from our study can be utilized in mathematical modelling, for example to characterize infectious disease spread and support pandemic planning. Most importantly, identifying these country-specific factors that influence human behavior provides further support for policies in controlling disease transmission that are context specific, not only for COVID-19 but also for other infectious diseases that may emerge in the future. Conclusions We conducted repeated surveys in Japan to understand how the population modified its behavior and contact patterns during the pandemic. Our results showed that daily contacts dropped by approximately 50% during the pandemic, rebounding only slowly after the government recommendations were relaxed. People proceeded with caution as they wore masks longer if they had more contacts and consistently wore masks until 2023, even after a full year since the last governmental recommendation was lifted. The frequency of contacts did not increase after individuals received the COVID-19 vaccine which is in contrast with European countries where the opposite trend was reported. Our findings provide evidence on the importance of reduced contacts, careful behavior, and high vaccination coverage that potentially limited disease transmission and mortality in Japan. Data Availability All accompanying code and data to generate all the figures in the main manuscript is available on GitHub: https://github.com/tomokanakamura/jp_contactsurvey.git https://github.com/tomokanakamura/jp_contactsurvey.git Ethics Participation in the survey was voluntary and all analyses were performed on anonymized data. The study design including the survey questions and informed consent were approved by the ethics committee of Nagasaki University School of Tropical Medicine and Global Health (approval number: NU_TMGH_2022_162_4). Informed consent was obtained from all participants who provided data from the surveys. Data availability Aggregated datasets are available to reproduce all the figures in the main manuscript. A processed dataset with the selected covariates in the multivariable generalized linear model is available to reproduce Table 3 . Due to potentially sensitive and identifiable information included, the original dataset of the contact surveys is not made public and is available from the corresponding author upon reasonable request. Code availability All accompanying code to generate all the figures in the main manuscript is available on GitHub: https://github.com/tomokanakamura/jp_contactsurvey.git Author Contributions TN, KA, KMO, RK, and AE contributed to designing the contact surveys. TN and RK collected the contact survey data. TN processed and analyzed the contact survey data. TN and KMO designed the statistical analysis. KA, KMO, KEA, MS, AE, YI, and HO provided feedback on the study design and analysis plan. All authors contributed to the interpretation of the study results, writing and critical revision of the manuscript. Competing Interest Statement The authors have declared that no competing interests exist. Acknowledgements We would like to acknowledge F-press that implemented the social contact surveys online and the study population in Osaka and Fukuoka prefectures who kindly participated in our surveys. We thank John Edmunds and our colleagues within the Centre for Mathematical Modelling of Infectious Diseases at LSHTM, the National Institute for Infectious Diseases in Japan, and Nagasaki University’s School of Tropical Medicine and Global Health and the Department of Clinical Medicine for input during the development of this study. List of abbreviations AIC Akaike Information Criterion CI confidence interval COVID-19 coronavirus disease 2019 CRR Contact Rate Ratio ED Emergency Declaration References 1. ↵ World Health Organization . WHO Coronavirus (COVID-19) Dashboard | WHO Coronavirus (COVID-19) Dashboard With Vaccination Data [Internet] . 2023 [cited 2023 Apr 5]. Available from: https://covid19.who.int/ 2. ↵ The World Bank . World Development Indicators | DataBank [Internet] . 2023 [cited 2023 Jul 5]. Available from: https://databank.worldbank.org/reports.aspx?source=2&series=SP.POP.65UP.TO.ZS&country= 3. ↵ Sachs JD , Karim SSA , Aknin L , Allen J , Brosbøl K , Colombo F , et al. The Lancet Commission on lessons for the future from the COVID-19 pandemic . The Lancet [Internet ]. 2022 Oct 8 [cited 2023 Jun 15]; 400 ( 10359 ): 1224 – 80 . Available from: http://www.thelancet.com/article/S0140673622015859/fulltext OpenUrl 4. ↵ Ministry of Education Culture Sports Science and Technology . Nationwide simultaneous temporary closure of schools (from 28 February to before spring break) . 全国一斉臨時休業関係(2/28~春季休業前まで):文部科学省 [Internet]. 2020 [cited 2023 Apr 4]. Available from: https://www.mext.go.jp/a_menu/coronavirus/mext_00006.html 5. ↵ Oshitani . COVID lessons from Japan: the right messaging empowers citizens. Nature [Internet] . 2022 May 1 [cited 2022 Jul 7]; 605 ( 7911 ): 589 . Available from: https://pubmed.ncbi.nlm.nih.gov/35606523/ 6. ↵ Wallinga J , Teunis P , Kretzschmar M . Using data on social contacts to estimate age-specific transmission parameters for respiratory-spread infectious agents . Am J Epidemiol [Internet ]. 2006 Nov [cited 2021 Oct 7]; 164 ( 10 ): 936 – 44 . Available from: https://pubmed.ncbi.nlm.nih.gov/16968863/ OpenUrl 7. ↵ Leung K , Wu JT , Leung GM . Real-time tracking and prediction of COVID-19 infection using digital proxies of population mobility and mixing . Nature Communications 2021 12 : 1 [Internet]. 2021 Mar 8 [cited 2021 Oct 7];12(1):1–8. Available from: https://www.nature.com/articles/s41467-021-21776-2 8. ↵ Prem K , van Zandvoort K , Klepac P , Eggo RM , Davies NG , Cook AR , et al. Projecting contact matrices in 177 geographical regions: An update and comparison with empirical data for the COVID-19 era . PLoS Comput Biol [Internet ]. 2021 Jul 1 [cited 2023 Apr 6]; 17 ( 7 ): e1009098 . Available from: https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1009098 OpenUrl 9. ↵ Nomura S , Tanoue Y , Yoneoka D , Gilmour S , Kawashima T , Eguchi A , et al. Mobility Patterns in Different Age Groups in Japan during the COVID-19 Pandemic: a Small Area Time Series Analysis through March 2021 . Journal of Urban Health [Internet ]. 2021 Oct 1 [cited 2023 Apr 5]; 98 ( 5 ): 635 – 41 . Available from: https://link.springer.com/article/10.1007/s11524-021-00566-7 OpenUrl 10. ↵ Nagata S , Nakaya T , Adachi Y , Inamori T , Nakamura K , Arima D , et al. Mobility Change and COVID-19 in Japan: Mobile Data Analysis of Locations of Infection . J Epidemiol [Internet ]. 2021 Jun 5 [cited 2023 Apr 5]; 31 ( 6 ): 387 . Available from: /pmc/articles/PMC8126677/ OpenUrl 11. ↵ Mossong J , Hens N , Jit M , Beutels P , Auranen K , Mikolajczyk R , et al. Social Contacts and Mixing Patterns Relevant to the Spread of Infectious Diseases . PLoS Med [Internet ]. 2008 Mar [cited 2021 Oct 4]; 5 ( 3 ): e74 . Available from: https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.0050074 OpenUrl 12. ↵ Ibuka Y , Ohkusa Y , Sugawara T , Chapman GB , Yamin D , Atkins KE , et al. Social contacts, vaccination decisions and influenza in Japan . J Epidemiol Community Health (1978). 2016 ; 70 ( 2 ): 162 – 7 . OpenUrl Abstract / FREE Full Text 13. ↵ Munasinghe L , Asai Y , Nishiura H . Quantifying heterogeneous contact patterns in Japan: a social contact survey . Theor Biol Med Model . 2019 ; 16 ( 1 ): 1 – 10 . OpenUrl CrossRef PubMed 14. ↵ Liu CY , Berlin J , Kiti MC , Del Fava E , Grow A , Zagheni E , et al. Rapid Review of Social Contact Patterns during the COVID-19 Pandemic . Epidemiology . 2021 ; 32 ( 6 ): 781 – 91 . OpenUrl CrossRef PubMed 15. ↵ National Statistics Center . National Population Estimates as of 1 October 2021. Portal Site of Official Statistics of Japan . 人口推計 各年10月1日現在人口 年次 2021年 | ファイル | 統計データを探す | 政府統計の総合窓口 [Internet]. 2021 [cited 2023 Apr 17]. Available from: https://www.e-stat.go.jp/stat-search/files?page=1&layout=datalist&toukei=00200524&tstat=000000090001&cycle=7&year=20210&month=0&tclass1=000001011679&tclass2val=0 16. ↵ Government of Japan . Basic Response Policy | Office for COVID-19 and Other Emerging Infectious Disease Control, Cabinet Secretariat 基本的対処方針に基づく対応|内閣官房新型コロナウイルス等感染症対策推進室 [Internet] . 2023 [cited 2023 Jul 6]. Available from: https://corona.go.jp/emergency/ 17. ↵ Jarvis CI , Van Zandvoort K , Gimma A , Prem K , Auzenbergs M , O’Reilly K , et al. Quantifying the impact of physical distance measures on the transmission of COVID-19 in the UK . BMC Med [Internet ]. 2020 ; 18 ( 1 ): 124 . Available from : doi: 10.1186/s12916-020-01597-8 OpenUrl CrossRef PubMed 18. Jarvis CI , Gimma A , van Zandvoort K , Wong KLM , Abbas K , Villabona-Arenas CJ , et al. The impact of local and national restrictions in response to COVID-19 on social contacts in England: a longitudinal natural experiment . BMC Med . 2021 ; 19 ( 1 ). 19. ↵ Gimma A , Munday JD , Wong KLM , Coletti P , van Zandvoort K , Prem K , et al. Changes in social contacts in England during the COVID-19 pandemic between March 2020 and March 2021 as measured by the CoMix survey: A repeated cross-sectional study . PLoS Med [Internet ]. 2022 Mar 1 [cited 2022 Oct 24]; 19 ( 3 ): e1003907 . Available from: https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1003907 OpenUrl 20. ↵ National Institute of Infectious Diseases Japan . Update on the COVID-19 Vaccine (05 August 2021) 新型コロナワクチンについて(2021年8月5日現在) [Internet]. 2021 [cited 2023 Apr 13]. Available from: https://www.niid.go.jp/niid/ja/diseases/ka/corona-virus/2019-ncov/2484-idsc/10569-covid19-53.html 21. ↵ Digital Agency of Japan . COVID-19 Vaccination Coverage Status of Japan 新型コロナワクチンの接種状況 | デジタル庁 [Internet]. 2023 [cited 2023 Aug 24]. Available from: https://info.vrs.digital.go.jp/dashboard 22. ↵ Ministry of Health Labor and Welfare . Report on COVID-19 Serosurvey of Japan 新型コロナウイルス感染症に関する抗体保有状況調査について|厚生労働省 [Internet]. 2023 [cited 2023 Jul 6]. Available from: https://www.mhlw.go.jp/stf/seisakunitsuite/bunya/0000121431_00132.html 23. ↵ Tsuzuki S , Asai Y , Ibuka Y , Nakaya T , Ohmagari N , Hens N , et al. Social contact patterns in Japan in the COVID-19 pandemic during and after the Tokyo Olympic Games . J Glob Health [Internet ]. 2022 Dec 3 [cited 2023 Jun 15]; 12 : 05047 . Available from: /pmc/articles/PMC9718444/ OpenUrl 24. ↵ Yabe T , Tsubouchi K , Fujiwara N , Wada T , Sekimoto Y , Ukkusuri S V . Non-compulsory measures sufficiently reduced human mobility in Tokyo during the COVID-19 epidemic . Sci Rep [Internet ]. 2020 Dec 1 [cited 2023 Jun 15];10(1). Available from: https://pubmed.ncbi.nlm.nih.gov/33093497/ 25. ↵ Veneti L , Robberstad B , Steens A , Forland F , Winje BA , Vestrheim DF , et al. Social contact patterns during the early COVID-19 pandemic in Norway: insights from a panel study, April to September 2020 . medRxiv [Internet]. 2023 Nov 19; Available from : doi: 10.1101/2023.11.18.23298731 OpenUrl Abstract / FREE Full Text 26. ↵ Bosetti P , Huynh BT , Abdou AY , Sanchez M , Eisenhauer C , Courtejoie N , et al. Lockdown impact on age-specific contact patterns and behaviours, France, April 2020 . Eurosurveillance [Internet]. 2021 Dec 12 [cited 2023 Apr 18]; 26 ( 48 ): 1 . Available from: /pmc/articles/PMC8641071/ OpenUrl 27. ↵ Tomori DV , Rübsamen N , Berger T , Scholz S , Walde J , Wittenberg I , et al. Individual social contact data and population mobility data as early markers of SARS-CoV-2 transmission dynamics during the first wave in Germany—an analysis based on the COVIMOD study . BMC Med [Internet ]. 2021 Dec 14; 19 ( 1 ): 271 . Available from: https://bmcmedicine.biomedcentral.com/articles/10.1186/s12916-021-02139-6 OpenUrl 28. ↵ Coletti P , Wambua J , Gimma A , Willem L , Vercruysse S , Vanhoutte B , et al. CoMix: comparing mixing patterns in the Belgian population during and after lockdown . Sci Rep . 2020 Dec 1; 10 ( 1 ). 29. ↵ Feehan DM , Mahmud AS . Quantifying population contact patterns in the United States during the COVID-19 pandemic . Nature Communications 2021 12 : 1 [Internet]. 2021 Feb 9 [cited 2022 Oct 24];12(1): 1 – 9 . Available from: https://www.nature.com/articles/s41467-021-20990-2 OpenUrl CrossRef PubMed 30. ↵ Zhang J , Litvinova M , Liang Y , Zheng W , Shi H , Vespignani A , et al. The impact of relaxing interventions on human contact patterns and SARS-CoV-2 transmission in China . Sci Adv [Internet ]. 2021 May 7 [cited 2021 May 10]; 7 ( 19 ): eabe2584 . Available from: https://advances.sciencemag.org/lookup/doi/10.1126/sciadv.abe2584 OpenUrl 31. ↵ Wong KLM , Gimma A , Coletti P , Paolotti D , Tizzani M , Cattuto C , et al. Social contact patterns during the COVID-19 pandemic in 21 European countries - evidence from a two-year study . BMC Infect Dis [Internet ]. 2023 Apr 26 [cited 2023 May 24]; 23 ( 1 ): 268 . Available from: https://pubmed.ncbi.nlm.nih.gov/37101123/ OpenUrl 32. ↵ Ministry of Internal Affairs and Communications . Part 1: Special Feature: Digitally Supporting Lifestyles and the Economy 総務省|令和3年版 情報通信白書|テレワークの実施状況 [Internet]. 2021 [cited 2023 Jul 6]. Available from: https://www.soumu.go.jp/johotsusintokei/whitepaper/ja/r03/html/nd123410.html 33. ↵ Ministry of Internal Affairs and Communications . Results of the 2020 Survey on Communications Usage Trends 総務省|報道資料|令和元年通信利用動向調査の結果 [Internet]. 2020 [cited 2023 Jul 6]. Available from: https://www.soumu.go.jp/menu_news/s-news/01tsushin02_02000148.html 34. ↵ Abe K , Kawachi I . Deaths in Nursing Homes During the COVID-19 Pandemic— Lessons from Japan . JAMA Health Forum [Internet ]. 2021 Feb 1 [cited 2023 Apr 21]; 2 ( 2 ): e210054 – e210054 . Available from: https://jamanetwork.com/journals/jama-health-forum/fullarticle/2776556 OpenUrl 35. ↵ Werner RM , Hoffman AK , Coe NB . Long-Term Care Policy after Covid-19 — Solving the Nursing Home Crisis . New England Journal of Medicine [Internet ]. 2020 Sep 3 [cited 2023 Apr 21]; 383 ( 10 ): 903 – 5 . Available from: https://www.nejm.org/doi/10.1056/NEJMp2014811 OpenUrl 36. ↵ Giddings R , Krutikov M , Palmer T , Fuller C , Azmi B , Shrotri M , et al. Changes in COVID-19 outbreak severity and duration in long-term care facilities following vaccine introduction, England, November 2020 to June 2021 . Eurosurveillance [Internet] . 2021 Nov 18 [cited 2023 Apr 21]; 26 ( 46 ): 2100995 . Available from: https://www.eurosurveillance.org/content/10.2807/1560-7917.ES.2021.26.46.2100995 OpenUrl 37. ↵ Saeedi R , Ahmadi E , Hassanvand MS , Mohasel MA , Yousefzadeh S , Safari M . Implemented indoor airborne transmission mitigation strategies during COVID-19: a systematic review . J Environ Health Sci Eng [Internet ]. 2023 Jun 1 [cited 2023 Jul 6]; 21 ( 1 ): 11 – 20 . Available from: https://link.springer.com/article/10.1007/s40201-023-00847-0 OpenUrl 38. ↵ Wambua J , Loedy N , Jarvis CI , Wong KLM , Faes C , Grah R , et al. The influence of COVID-19 risk perception and vaccination status on the number of social contacts across Europe: insights from the CoMix study . BMC Public Health . 2023 Dec 1; 23 ( 1 ). 39. United Nations . World Population Prospects - Population Division - United Nations [Internet] . 2022 [cited 2023 Jul 24]. Available from: https://population.un.org/wpp/Download/Standard/MostUsed/ View the discussion thread. Back to top Previous Next Posted April 21, 2025. Download PDF Data/Code Email Thank you for your interest in spreading the word about medRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. You are going to email the following Continuing to be Cautious: Japanese Contact Patterns during the COVID-19 Pandemic and their Association with Public Health Recommendations Message Subject (Your Name) has forwarded a page to you from medRxiv Message Body (Your Name) thought you would like to see this page from the medRxiv website. Your Personal Message CAPTCHA This question is for testing whether or not you are a human visitor and to prevent automated spam submissions. Share Continuing to be Cautious: Japanese Contact Patterns during the COVID-19 Pandemic and their Association with Public Health Recommendations Tomoka Nakamura , Ryo Kinoshita , Akira Endo , Katherine E. Atkins , Hitoshi Oshitani , Yoko Ibuka , Motoi Suzuki , Koya Ariyoshi , Kathleen M. O’Reilly medRxiv 2025.04.17.25326019; doi: https://doi.org/10.1101/2025.04.17.25326019 Share This Article: Copy Citation Tools Continuing to be Cautious: Japanese Contact Patterns during the COVID-19 Pandemic and their Association with Public Health Recommendations Tomoka Nakamura , Ryo Kinoshita , Akira Endo , Katherine E. Atkins , Hitoshi Oshitani , Yoko Ibuka , Motoi Suzuki , Koya Ariyoshi , Kathleen M. O’Reilly medRxiv 2025.04.17.25326019; doi: https://doi.org/10.1101/2025.04.17.25326019 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Area Infectious Diseases (except HIV/AIDS) Subject Areas All Articles Addiction Medicine (567) Allergy and Immunology (863) Anesthesia (297) Cardiovascular Medicine (4411) Dentistry and Oral Medicine (443) Dermatology (380) Emergency Medicine (606) Endocrinology (including Diabetes Mellitus and Metabolic Disease) (1505) Epidemiology (15205) Forensic Medicine (30) Gastroenterology (1119) Genetic and Genomic Medicine (6574) Geriatric Medicine (666) Health Economics (994) Health Informatics (4511) Health Policy (1365) Health Systems and Quality Improvement (1608) Hematology (537) HIV/AIDS (1263) Infectious Diseases (except HIV/AIDS) (15903) Intensive Care and Critical Care Medicine (1103) Medical Education (620) Medical Ethics (144) Nephrology (666) Neurology (6573) Nursing (345) Nutrition (998) Obstetrics and Gynecology (1139) Occupational and Environmental Health (954) Oncology (3319) Ophthalmology (968) Orthopedics (369) Otolaryngology (420) Pain Medicine (435) Palliative Medicine (129) Pathology (662) Pediatrics (1689) Pharmacology and Therapeutics (691) Primary Care Research (710) Psychiatry and Clinical Psychology (5422) Public and Global Health (9205) Radiology and Imaging (2191) Rehabilitation Medicine and Physical Therapy (1367) Respiratory Medicine (1191) Rheumatology (593) Sexual and Reproductive Health (709) Sports Medicine (529) Surgery (709) Toxicology (99) Transplantation (288) Urology (265) (function(){function c(){var b=a.contentDocument||a.contentWindow.document;if(b){var d=b.createElement('script');d.innerHTML="window.__CF$cv$params={r:'9fead29d48590db4',t:'MTc3OTI3NDY2OQ=='};var a=document.createElement('script');a.src='/cdn-cgi/challenge-platform/scripts/jsd/main.js';document.getElementsByTagName('head')[0].appendChild(a);";b.getElementsByTagName('head')[0].appendChild(d)}}if(document.body){var a=document.createElement('iframe');a.height=1;a.width=1;a.style.position='absolute';a.style.top=0;a.style.left=0;a.style.border='none';a.style.visibility='hidden';document.body.appendChild(a);if('loading'!==document.readyState)c();else if(window.addEventListener)document.addEventListener('DOMContentLoaded',c);else{var e=document.onreadystatechange||function(){};document.onreadystatechange=function(b){e(b);'loading'!==document.readyState&&(document.onreadystatechange=e,c())}}}})();