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Viral transmissibility of SARS-CoV-2 accelerates in the winter, similarly to influenza epidemics

  • Author Footnotes
    1 S Inaida and RE Paul were equally contributed to this work.
    Shinako Inaida
    Correspondence
    Address correspondence to Shinako Inaida, PhD, Graduate School of Medicine, Kyoto University, Kyoto, Japan.
    Footnotes
    1 S Inaida and RE Paul were equally contributed to this work.
    Affiliations
    Graduate School of Medicine, Kyoto University, Kyoto, Japan

    Department of Environmental Medicine and Behavioral Science, Faculty of Medicine, Kindai University
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  • Author Footnotes
    1 S Inaida and RE Paul were equally contributed to this work.
    Richard E. Paul
    Footnotes
    1 S Inaida and RE Paul were equally contributed to this work.
    Affiliations
    Pasteur Kyoto International Joint Research Unit for Integrative Vaccinomics, Kyoto, Japan
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  • Shigeo Matsuno
    Affiliations
    Biomedical Science Association, Tokyo, Japan
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  • Author Footnotes
    1 S Inaida and RE Paul were equally contributed to this work.

      Highlights

      • COVID-19 epidemiological curves presented the same seasonal profiles as influenza.
      • Cases increased exponentially in winter, but less than linearly in spring/summer.
      • Results suggest underlying seasonality, an important factor for epidemic control.
      The transmissibility of SARS-CoV-2 is anticipated to increase in the winter because of increased viral survival in cold damp air and thus would exacerbate viral spread in community. Analysis to capture the seasonal trend is needed to be prepared for future epidemics. We compared regression models for the 5-week case prior to each epidemic peak week for both the COVID-19 and influenza epidemics in winter and summer. The weekly case increase ratio was compared, using non-paired t tests between seasons. In order to test the robustness of seasonal transmission patterns, the normalized weekly case numbers of COVID-19 and influenza case rates of all seasons were assessed in a combined quadratic regression analysis. In winter, the weekly case increase ratio accelerated before epidemic peaks, similarly, for both COVID-19 and influenza. The quadratic regression models of weekly cases were observed to be convex curves in the winter and concave curves in the spring/summer for both COVID-19 and influenza. A significant increase of case increase ratio (3.19 [95%CI:0.01-6.37, P = .049]) of the COVID-19 and influenza epidemics was observed in winter as compared to spring/summer before the epidemic peak. The epidemic of COVID-19 was found to mirror that of influenza, suggesting a strong underlying seasonal transmissibility. Influenza epidemics can potentially be a useful reference for the COVID-19 epidemics.

      Key Words

      Background

      COVID-19 has spread worldwide within a few months and resulted in millions of deaths globally.
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      As previously observed for the 2009 influenza pandemic (Flu 09 pdm), a novel invasive respiratory virus in a naïve population, initial epidemic waves occurred randomly throughout the year and dependent upon time of arrival. This non-seasonality was observed for COVID-19 in Japan, where the first epidemic wave occurring in March was followed by subsequent epidemics in July, December, April before the largest epidemic occurring around the Olympic Games, which started on July 22, 2021.
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      However, the extent to which these factors impact on the initial weeks of the epidemic trajectory and smother any seasonality in transmissibility is less certain.

      National Epidemiological Surveillance of Infectious Diseases (NESID). Accessed June 10, 2022. https://www.niid.go.jp/niid/en/data.html.

      Herein, we compare the weekly trend of infection and the case increase ratio of infection during the epidemics occurring in winter, spring, and summer, by using the COVID-19 and influenza patient data in Tokyo. We assess the extent to which seasonality in disease incidence can be observed for both viral respiratory infections. The surveillance data of COVID-19 included all cases occurring between February 2020 and August 2021 and the surveillance of influenza included influenza (influenza like illness) cases diagnosed at sentinel clinics between 2005 and 2006 to 2010 and 2011 epidemic seasons.

      COVID-19 surveillance report in Tokyo. Accessed June 10, 2022. https://stopcovid19.metro.tokyo.lg.jp/en/reference

      Methods

      We used the data of national surveillance of infectious diseases in Japan for COVID-19 and influenza. Weekly epidemiological features before epidemic peak were analyzed for COVID-19 and influenza.

      COVID-19 data

      We used the COVID-19 surveillance data from Tokyo between first week of February 2020 and second week of August 2021. The data included sex, age group (10-year age group bins) of all individuals who had PCR-confirmed the COVID-19 positive infections (irrespective of whether they were symptomatic). In addition, the number of symptomatic cases by clinical onset date were downloaded from the open data source on the web. In Tokyo, vaccination for the COVID-19 was started in April 2021 and the coverage was 34.2% as of second August 2021.
      • Guan WJ
      • Ni ZY
      • Hu Y
      • et al.
      Clinical characteristics of coronavirus disease 2019 in China.
      An estimated infection date was taken to be 4 days prior to the date of onset of symptoms, being the average incubation period.

      Tokyo Prefectural Government—announcement for the covid-19 [in Japanese]. Accessed June 10, 2022. https://www.bousai.metro.tokyo.lg.jp/1007617/index.html

      In Japan, the PCR laboratory testing was conducted by either local health offices or the prefectural governments or hospitals, by nasal or throat swabs for patients who presented with fever, or continuous coughing, or feeling unwell. Active surveillance with PCR testing was carried out by local health office for the people who had been in contact with an infected person, or who participated at a same public event or had been present in the same place as the infected person. Thus, family members living with an infected person were tested and participants of the events where there had been a cluster of patients were encouraged to visit the COVID-19 designated call center of either central or local health offices. These centers arranged PCR testing through the designated hospital near the resident. Except for those who were tracked in the aforementioned circle of close contact with the infected person, the surveillance with PCR testing was based on symptoms shown by patients. When the result of PCR testing arrived, the local health office conducted interviews with the PCR-positive patients to obtain personal information (sex and age group) and symptom onset date. We describe the baseline information of the patients for the study period (based on the PCR-confirmed date) and depict the number of weekly infections calculated by the estimated infection date. The social interventions which were implemented during the study period were reviewed from the website of Tokyo Prefectural government. Whole virus genome sequence was conducted at the National Institute of Infectious Diseases and the result was reported on the web site (NIID).
      • Sekizuka T
      • Itokawa K
      • Hashino M
      • et al.
      A genome epidemiological study of the covid-19 introduction into Japan.
      ,
      • Taniguchi K
      • Hashimoto S
      • Kawado M
      • et al.
      Overview of infectious disease surveillance system in Japan, 1999–2005.

      Influenza sentinel data

      We used the national sentinel influenza surveillance data for Tokyo.

      Infectious Diseases Weekly Report (IDWR) in Japan. Accessed June 10, 2022. https://www.niid.go.jp/niid/en/idwr-e.html.

      The sentinel influenza surveillance in Tokyo is conducted over 300 sentinel clinics that monitor the average of the weekly number of influenza cases per sentinel site. The data included the 2005-2006 [Flu 06] to the 2010-2011 [Flu 11] influenza epidemics in the winter and the novel 2009 swine influenza A(H1N1) pandemic [Flu 09 pdm] in the summer. The influenza surveillance data were used from the open source on the website.

      GenStat for Windows. 20th ed., VSN International Ltd., Hemel Hempstead, UK.

      RT-PCR testing of epidemic virus subtypes and strains was conducted for about 10 % of all cases and the results reported on the web site of NIID.

      Infectious Diseases Weekly Report (IDWR) in Japan. Accessed June 10, 2022. https://www.niid.go.jp/niid/en/idwr-e.html.

      ,

      GenStat for Windows. 20th ed., VSN International Ltd., Hemel Hempstead, UK.

      Seasonal trends in infectivity

      To assess the time-series trend of the increase in the number of infections, a quadratic regression model was fitted for the weekly number of infections for the 5-week period prior to each epidemic peak week for both the COVID-19 and influenza epidemics. This 5-week period was used as representative of the growth phase of the epidemic. We calculated the weekly case increase ratio (the ratio between the number of cases of the current week and the number of cases of the previous week) for the 5-week period. The quadratic regression models and case increase ratio were then compared between the winter, spring and summer epidemics. In order to analyze epidemic impact just before peaking, we compared the seasonal weekly case increase ratio for the accumulated number of cases between the third week and the fifth weeks of the study period, using non-paired t-tests. We also observed the trend for the first 5 weeks at the start of the epidemic, starting with the first weekly increase in number of cases.
      To assess the statistical significance and the robustness of the association of season and virus with cases over time, the weekly COVID-19 case numbers and influenza case rates were normalized for each time period of 5 weeks to yield values between 0 and 1 using the following formula: (xi-min(x))/(max(x)-min(x)) where xi is the number of cases or case rates at month i. These normalized values were then used together from all time periods in a combined quadratic regression analysis. Firstly, the regression used all data from the Winter and Spring/Summer periods with season and virus (The COVID-19 or Influenza) fitted as co-variables. Then the seasons were analysed separately. The epidemics, using the normalized values, were tested for autocorrelation and as there was no significant autocorrelation, model sensitivity was assessed through a permutation test. The permutation test used is the classical permutation test, which is an exact test, in which the distribution of the test statistic under the null hypothesis is obtained by calculating all possible values of the test statistic under all possible rearrangements of the observed data points. Permutation tests are, therefore, a form of resampling with replacement. The fit of the quadratic model was compared to that using generalized linear models. All statistical analyses were performed in Genstat version 20.
      • Davis G.W.
      • Griesemer R.A.
      • Shadduck J.A.
      • Farrell R.L.
      Effect of relative humidity on dynamic aerosols of adenovirus 12.

      Results

      COVID-19 case and predominant virus strain

      There was a total of 286,868 PCR-positive cases. Among the age groups, the cases in the 20s age group were the highest (29.08%) followed by 30s age group (19.83%), 40s age group (15.67%), and 50s age group (12.18%) (Table 1). Of these PCR positive infections, 219,545 (76.5%) were symptomatic (excluding positive samples of patients whose onset date was unspecified [22.4%]).
      Table 1The proportion of the COVID-19 and influenza case by age group
      Age groupProportion (%) within totalcase (Male/Female)
      COVID-19
      Based on results between February 1, 2020 and August 15, 2021 for Tokyo. The results also included antigen testing but the majority of testing conducted was PCR testing.
      Influenza
      Based on results between 2005-2006 and 2010-2011 epidemic seasons for Tokyo the sentinel influenza surveillance in Tokyo is conducted over 300 sentinel clinics that monitor the average of the weekly number of influenza cases per sentinel site. The majority of sentinel clinics for surveillance of influenza were pediatric clinics and thus the surveillance data consisted of predominantly children data.34
      Total286,868

      (56.1/43.9)
      1196.82

      (50.9/49.1)
      <10

      10s

      20s

      30s

      40s

      50s

      60s

      70s

      ≳80s
      3.41(1.77/1.64)

      6.73(3.55/3.18)

      29.08(15.38/13.70)

      19.83(11.88/7.95)

      15.67(9.73/5.95)

      12.18(7.16/5.02)

      5.40(3.19/2.21)

      3.88(2.03/1.85)

      3.79(1.43/2.36)
      52.44 (27.76/24.68)

      24.21(12.96/11.25)

      6.58(3.12/3.46)

      7.91(3.12/4.79)

      5.27(2.31/2.96)

      2.0(0.93/1.07)

      0.95(0.41/0.54)

      0.45(0.19/0.26)

      0.18 (0.08/0.10)
      low asterisk Based on results between February 1, 2020 and August 15, 2021 for Tokyo. The results also included antigen testing but the majority of testing conducted was PCR testing.
      Based on results between 2005-2006 and 2010-2011 epidemic seasons for Tokyo the sentinel influenza surveillance in Tokyo is conducted over 300 sentinel clinics that monitor the average of the weekly number of influenza cases per sentinel site. The majority of sentinel clinics for surveillance of influenza were pediatric clinics and thus the surveillance data consisted of predominantly children data.

      COVID-19 surveillance report in Tokyo. Accessed June 10, 2022. https://stopcovid19.metro.tokyo.lg.jp/en/reference

      The 5 epidemic waves occurred in winter, spring, and summer (period A-E in Fig 1). The epidemics in summer, which were the second and the fifth epidemic waves, occurred at a similar time; started increasing in June and decreased from August.
      Fig 1
      Fig 1Weekly SARS-CoV-2 infection and predominant virus strain in Tokyo.
      During the first 4 epidemic waves (period A to D in Fig 1), the number of infections decreased during the social interventions. By contrast, the fifth epidemic wave (period E in Fig 1) that occurred around the Olympic Games continued to increase in the number of infections despite interventions being implemented. (Fig 1) This intervention included encouraging people to stay in, stop selling alcoholic drinks at restaurants and close shops earlier than 8 pm. The peak of the epidemic was the highest in the fifth epidemic wave and the second highest peak was in the third epidemic wave that occurred around December 2020. The predominant virus strains were, B.1.1. in the period A, B.1.1.284 in the period B, B.1.1.214 in the period C, B.1.1.7 (Alpha variant) in the period E, and B.1.617.2 (Delta variant) in the period E.

      Influenza case and predominant virus subtype and strains

      There was an average total of 1196.82 cases per sentinel site. Among the age groups, the number of cases in the under 10 years of age group was the highest (52.44%) followed by 10s age group (24.21%), 30s age group (7.91%), and 20s age group (6.58%) (Table 1). The majority of the seasonal epidemics occurred in January, although the Flu 07 was delayed until February. The predominant virus subtype and strain varied among A(H1N1), A(H1N1)pdm09, A(H3N2), and B lineage, each season. (Supplementary Fig S1)

      Seasonal trends in weekly case increase ratio

      The weekly COVID-19 and influenza infections increased the case increase ratio toward the peak in the winter (Fig. 2A-ⅰ, ⅱ, 2B-ⅰ, ⅱ, and S1) as compared with the infections in the summer and spring (Fig. 2A-ⅲ, iv and 2B-ⅲ, iv, and S1). The increase ratios of the accumulated number of cases between the third and the fifth weeks compared with the number of cases in the third week in the winter were: 11.6 [95%CI:9.5-14.1] (COVID-19, period A), 4.1 [95%CI:3.9-4.3] (COVID-19, period C), 7.2 [95%CI:6.9-7.5] (Flu 06), 3.8 [95%CI:3.7-4.0] (Flu 07), 4.5 [95%CI:4.2-4.7] (Flu 08), 4.5 [95%CI:4.3-4.7] (Flu 09 seasonal), 9.0 [95%CI:8.6-9.4] (Flu 11). Conversely, in the summer and spring, the increase ratios of the accumulated number of cases between the third and the fifth weeks compared with the number of cases in the third week were: 3.6 [95%CI:3.4-3.9] (COVID-19, period B), 3.6 [95%CI:3.5-3.8] (COVID-19, period D), 3.7 [95%CI:3.6-3.8] (COVID-19, period E), 3.6 [95%CI:3.4-3.7] (Flu 09 pdm). The average of these case increase ratios between the third and the fifth weeks were, 6.8 [95%CI:3.6-10.0] in the winter, and 3.6 [95%CI:3.5-3.7] in the summer and spring (excluding the Flu 07 season when the epidemic was delayed for approximately one month). Using non-paired t test, a significant increase of case increase ratio (3.19 [95%CI:0.01-6.37, P < .049]) of the COVID-19 and influenza epidemics in the winter was observed as compared with the spring/summer epidemics. Broadly, of particular note were the similar overall convex trends for the winter seasonal influenza and COVID-19 epidemic profiles and the concave trends for the spring/summer swine influenza and COVID-19 epidemics. By contrast, during the initial 5 weeks of the epidemics, all COVID-19 and influenza epidemic waves were convex (Supplementary Fig S2).
      A quadratic regression was then fitted to all the data combined. In the combined season analysis, the cases in the winter season were found to be significantly different from the summer season (t = 3.08, P = .003) but there was no difference between the 2 viruses (t = 1.00, P = .322). The linear component of the quadratic regression was not significant, (t = -0.18, P = .858), whereas the quadratic component was significantly positive (ie, convex) (t = 3.12, P = .003). The model explained 81.5% of the variance. In the Spring/Summer only analysis, there was again no significant association of virus type (t = -0.2, P = .848) and both the linear and quadratic components were significant (Linear: t = 6.85, P < .001; quadratic: t = -2.24, P = .039); the quadratic component was here concave (Fig 3 A). The model explained 96.8% of the variance. In the Winter only analysis, virus type was again not significant (t = 1.26, P = .216) and both the linear and quadratic components were significant (Linear: t = -2.32, P = .027; quadratic: t = 4.50, P ≤ .001); the shape here is distinctly convex (Fig 3 B). The model explained 80.1% of the variance. The permutation test confirmed the model fits, with the probability for the model being 0.001 as determined from 999 random permutations. The quadratic models fitted the data better than generalized linear models that explained 96.5% and 69% of the variance in summer and winter respectively.
      Figure 3. Normalized case number per week preceding the epidemic peak for A. Summer epidemics and B. Winter epidemics.

      Discussion

      We found a more rapid increase of both COVID-19 and influenza cases in the weeks preceding the epidemic peaks in the winter as compared with the summer and spring. The result suggests that, as for seasonal influenza, the transmissibility of SARS-CoV-2 increases in the winter. The lower summer case increase ratios in the summer also suggest that less clement conditions diminish viral transmissibility. Thus, despite the novelty of SARS-CoV-2, the virus is exhibiting the same seasonal tendencies as influenza. All viruses causing upper respiratory tract infections show seasonality to some extent. At the least seasonal end of the spectrum are the non-enveloped viruses, including the rhinoviruses and adenoviruses, which are present throughout the year. These viruses do, however, survive better at higher humidity and lower temperatures.
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      • Van Berwaer R
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      The SARS-CoV-2 and other human coronavirus spike proteins are fine-tuned towards temperature and proteases of the human airways.
      The similarity of the seasonal epidemic trajectories is all the more remarkable given the different basic reproductive numbers (R0) of seasonal influenza (median 1.28), pandemic 2009 influenza (1.46), The SARS-CoV-2 ancestral variant (2.5) and the delta variant (5.08).
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      Current Situation of Infection in Japan.
      The higher R0 of the SARS-CoV-2 variants would be expected to alter the rate of the acceleration to the peak, but not necessarily the shape (convex or concave). Our estimated case increase ratio can be similarly interpreted for R0 and this can clearly be seen for the Delta variant during the Olympics in Fig 1, Fig 2Aand Fig 1, Fig 2A, where there is a rapid increase in total case numbers, but which still displays a concave trajectory.
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      Despite its higher transmissibility, however, the infection rate tailed off as with the other non-winter epidemics. The same such epidemic situation occurring in winter would therefore be predicted to lead to a much higher case rate.
      At the start of each epidemic, all the COVID-19 and influenza epidemic curves were convex. This suggests that at the very beginning of the epidemics the attack rate is relatively high regardless of seasonality. This could reflect household or workplace level transmission and/or case clusters. Then, in the following weeks, transmission would spread to the community level and thus be more affected by seasonality, hence leading to the convex curve in the winter and the concave curve in the summer.
      One of the major constraints to interpreting our data and therefore a limitation to our study, is comparing influenza data that predominantly come from children with COVID-19 data that come from an older population. Behavioral differences in children vs the adult population can strongly impact age-specific case rates. Schools provide a fertile ground for the spread of classical pediatric diseases, including influenza, but until recently the role played by children in transmission of SARS-CoV-2 has been considered relatively minor.
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      The tendency for pathogens to generate clusters varies significantly according to the pathogen in question, suggesting that both human behavior and the nature of the infection itself are important. For the severe acute respiratory syndrome coronavirus (SARS-CoV), the clustering parameter k was estimated to be 0.16 and the Middle East respiratory syndrome coronavirus (MERS-CoV 2012) 0.25, indicating clustering.
      • Endo A
      • Abbott S
      • Kucharski AJ
      • Funk S
      Estimating the overdispersion in COVID-19 transmission using outbreak sizes outside China.
      Estimates of SARS-CoV-2 are currently uncertain, but may be of the same order of magnitude, with an estimated 10% of the cases contributing to 80% of the spread.
      • Yabe T
      • Tsubouchi K
      • Fujiwara N
      • et al.
      Non-compulsory measures sufficiently reduced human mobility in Tokyo during the COVID-19 epidemic.
      Influenza cases, by contrast, are less aggregated in space. However, despite these limitations in data comparisons among different age groups with associated behavioral differences, the nature of the 2 viruses does generate the same seasonal patterns.
      A second potential confounder for all these trends might have been the influence from non-pharmaceutical interventions (NPIs) that have been implemented from time to time. However, in Tokyo, except for the first epidemic wave, the practice of NPIs was very limited and that was only for restaurants and shops. Thus, any effect of NPIs might have been limited as compared to other cities in the world where rigorous NPIs were applied. Indeed, the justice system does not permit mandatory lockdowns and thus the government could only announce non-compulsory remote working requests. Analysis of the first epidemic wave did identify a significant decrease in mobility, using mobile phone data.
      • Altman DG.
      London: Practical Statistics for Medical Research.
      However, calculations of R suggested that the value was already low (∼0.3) prior to the announcement, thus rendering even such low-level restriction requests superfluous.
      Insofar as the epidemiological data of COVID-19 are limited and given the apparent similarity in seasonality with influenza, it would seem reasonable to suggest that SARS-CoV-2 will eventually join our current seasonal respiratory viruses. Such predictability would enable improved public health planning for testing, intervention and hospital space. Although the Delta variant behaved as predicted despite its high transmissibility, the very recent emergence of the O-micron variant raises another challenge, especially in the light of its apparent capacity to evade the current vaccine-induced immune response. While Omicron variant can evade the vaccine-induced immune responses, this lineage is less pathogenic, possibly indicating adaption of the virus to its human host and thus one step away of becoming an endemic seasonal virus. Continued study of the epidemics is needed to extend upon the results presented here.
      Fig 2A
      Fig 2AQuadratic regression model among seasons for COVID-19 and Influenza in Tokyo.
      Fig 2B
      Fig 2BWeekly case increase ratio among seasons for COVID-19 and Influenza in Tokyo.
      Fig 3
      Fig 3Normalized case number per week preceding the epidemic peak for (A) Summer epidemics and (B) Winter epidemics. Plotted are the numbers from the fitted quadratic regression. Case numbers were normalized for each epidemic to enable analyses across epidemics of differing magnitudes (ie, for SARS-Cov-2 vs Influenza).

      Patient and public involvement statement

      No patient and public were involved in this study.

      Appendix. SUPPLEMENTARY MATERIALS

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