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. 2020 Jun 26;368(6498):1481-1486.
doi: 10.1126/science.abb8001. Epub 2020 Apr 29.

Changes in contact patterns shape the dynamics of the COVID-19 outbreak in China

Juanjuan Zhang  1 , Maria Litvinova  2 , Yuxia Liang  1 , Yan Wang  1 , Wei Wang  1 , Shanlu Zhao  3 , Qianhui Wu  1 , Stefano Merler  4 , Cécile Viboud  5 , Alessandro Vespignani  6   2 , Marco Ajelli #  7 , Hongjie Yu #  8
Affiliations

Changes in contact patterns shape the dynamics of the COVID-19 outbreak in China

Juanjuan Zhang et al. Science. .

Abstract

Intense nonpharmaceutical interventions were put in place in China to stop transmission of the novel coronavirus disease 2019 (COVID-19). As transmission intensifies in other countries, the interplay between age, contact patterns, social distancing, susceptibility to infection, and COVID-19 dynamics remains unclear. To answer these questions, we analyze contact survey data for Wuhan and Shanghai before and during the outbreak and contact-tracing information from Hunan province. Daily contacts were reduced seven- to eightfold during the COVID-19 social distancing period, with most interactions restricted to the household. We find that children 0 to 14 years of age are less susceptible to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection than adults 15 to 64 years of age (odds ratio 0.34, 95% confidence interval 0.24 to 0.49), whereas individuals more than 65 years of age are more susceptible to infection (odds ratio 1.47, 95% confidence interval 1.12 to 1.92). Based on these data, we built a transmission model to study the impact of social distancing and school closure on transmission. We find that social distancing alone, as implemented in China during the outbreak, is sufficient to control COVID-19. Although proactive school closures cannot interrupt transmission on their own, they can reduce peak incidence by 40 to 60% and delay the epidemic.

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Figures

Fig. 1
Fig. 1. Contact matrices by age.
(A) Baseline period contact matrix for Wuhan (regular weekday only). Each cell of the matrix represents the mean number of contacts that an individual in a given age group has with other individuals, stratified by age groups. The color intensity represents the number of contacts. To construct the matrix, we performed bootstrap sampling with replacement of survey participants weighted by the age distribution of the actual population of Wuhan. Every cell of the matrix represents an average over 100 bootstrapped realizations. (B) Same as (A), but for the outbreak contact matrix for Wuhan. (C) Difference between the baseline period contact matrix and the outbreak contact matrix in Wuhan. (D) Same as (A), but for Shanghai. (E and F) Same as (B) and (C), but for Shanghai.
Fig. 2
Fig. 2. Effect of contact patterns on the epidemic spread.
(A) Estimated R0 during the outbreak (mean and 95% CI), as a function of baseline R0 (i.e., that derived by using the contact matrix estimated for the baseline period). The figure refers to Wuhan and includes both the scenario accounting for the estimated susceptibility to infection by age and the scenario where we assume that all individuals are equally susceptible to infection. The distribution of the transmission rate is estimated through the next-generation matrix approach by using 100 bootstrapped contact matrices for the baseline period to obtain the desired R0 values. We then use the estimated distribution of the transmission rate and the bootstrapped outbreak contact matrices to estimate R0 for the outbreak period. The 95% CIs account for the uncertainty on the distribution of the transmission rate, mixing patterns, and susceptibility to infection by age. (B) Same as (A), but for Shanghai. (C) Infection attack rate 1 year after the initial case of COVID-19 (mean and 95% CI) as a function of the baseline R0. The estimates are made by simulating the SIR transmission model (see SM) using the contact matrix for the baseline period and considering the estimated susceptibility to infection by age and assuming that all individuals are equally susceptible to infection. The 95% CIs account for the uncertainty on the mixing patterns and susceptibility to infection by age. (D) Same as (C), but for Shanghai.
Fig. 3
Fig. 3. Effect of limiting school contacts on the epidemic spread.
(A) Estimated R0 during the outbreak (mean and 95% CI), as a function of baseline R0 (i.e., that derived by using the contact matrix estimated for the baseline period). The figure refers to Shanghai and the scenario accounting for the estimated susceptibility to infection by age. Three contact patterns are considered: (i) as estimated during the COVID-19 outbreak, (ii) as estimated during school vacations (7), and (iii) as estimated for the baseline period, but suppressing all contacts at school. (B) Daily incidence of new SARS-CoV-2 infections (mean and 95% CI), as estimated by the SIR model, assuming age-specific susceptibility to infection (see SM). Three mixing patterns are considered: (i) as estimated for the baseline period, (ii) as estimated during school vacations (7), and (iii) as estimated for the baseline period, but suppressing all contacts at school. The inset shows the infection attack rate 1 year after the introduction of the first COVID-19 case (mean and 95% CI). (C) Same as (A), but assuming equal susceptibility to infection by age. (D) Same as (B), but assuming equal susceptibility to infection by age.
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References

    1. Johns Hopkins University, COVID-19 Dashboard (2020); https://coronavirus.jhu.edu/map.html [accessed 16 April 2020].
    1. Chinese Center for Disease Control and Prevention, Update on COVID-19 as of 24:00 on April 16, 2020 (2020); http://2019ncov.chinacdc.cn/2019-nCoV/ [accessed 17 April 2020].
    1. Zhang J., Litvinova M., Wang W., Wang Y., Deng X., Chen X., Li M., Zheng W., Yi L., Chen X., Wu Q., Liang Y., Wang X., Yang J., Sun K., Longini I. M. Jr.., Halloran M. E., Wu P., Cowling B. J., Merler S., Viboud C., Vespignani A., Ajelli M., Yu H., Evolving epidemiology and transmission dynamics of coronavirus disease 2019 outside Hubei province, China: A descriptive and modelling study. Lancet Infect. Dis. S1473-3099(20)30230-9 (2020). 10.1016/S1473-3099(20)30230-9 - DOI - PMC - PubMed
    1. The Novel Coronavirus Pneumonia Emergency Response Epidemiology Team , The epidemiological characteristics of an outbreak of 2019 novel coronavirus diseases (COVID-19) — China, 2020. China CDC Weekly 2, 113–122 (2020). - PMC - PubMed
    1. Mossong J., Hens N., Jit M., Beutels P., Auranen K., Mikolajczyk R., Massari M., Salmaso S., Tomba G. S., Wallinga J., Heijne J., Sadkowska-Todys M., Rosinska M., Edmunds W. J., Social contacts and mixing patterns relevant to the spread of infectious diseases. PLOS Med. 5, e74 (2008). 10.1371/journal.pmed.0050074 - DOI - PMC - PubMed

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