Changes in contact patterns shape the dynamics of the COVID-19 outbreak in China
The coronavirus 2019 (COVID-19) pandemic has brought tighter restrictions on the daily lives of millions of people, but we do not yet understand what measures are the most effective. Zhang et al. modeled virus transmission in Wuhan, China, in February 2020, investigating the effects of interventions...
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| Published in | Science (American Association for the Advancement of Science) Vol. 368; no. 6498; pp. 1481 - 1486 |
|---|---|
| Main Authors | , , , , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
The American Association for the Advancement of Science
26.06.2020
American Association for the Advancement of Science |
| Subjects | |
| Online Access | Get full text |
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| Abstract | The coronavirus 2019 (COVID-19) pandemic has brought tighter restrictions on the daily lives of millions of people, but we do not yet understand what measures are the most effective. Zhang
et al.
modeled virus transmission in Wuhan, China, in February 2020, investigating the effects of interventions ranging from patient management to social isolation. Age-mixing patterns were estimated by contact surveys conducted in Wuhan and Shanghai at the beginning of February 2020. Once people reduced their average daily contacts from 14 to 20 down to 2, transmission rapidly fell below the epidemic threshold. The model also showed that preemptive school closures helped to reduce transmission, although alone they would not prevent a COVID-19 outbreak. Limiting human mixing to within households appeared to be the most effective measure.
Science
, this issue p.
1481
Reducing social contacts on the scale achieved in Wuhan and Shanghai in early 2020 could be sufficient to bring a COVID-19 outbreak under control.
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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| AbstractList | 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.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. The coronavirus 2019 (COVID-19) pandemic has brought tighter restrictions on the daily lives of millions of people, but we do not yet understand what measures are the most effective. Zhang et al. modeled virus transmission in Wuhan, China, in February 2020, investigating the effects of interventions ranging from patient management to social isolation. Age-mixing patterns were estimated by contact surveys conducted in Wuhan and Shanghai at the beginning of February 2020. Once people reduced their average daily contacts from 14 to 20 down to 2, transmission rapidly fell below the epidemic threshold. The model also showed that preemptive school closures helped to reduce transmission, although alone they would not prevent a COVID-19 outbreak. Limiting human mixing to within households appeared to be the most effective measure. Science , this issue p. 1481 Reducing social contacts on the scale achieved in Wuhan and Shanghai in early 2020 could be sufficient to bring a COVID-19 outbreak under control. 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. Who and what next?The coronavirus 2019 (COVID-19) pandemic has brought tighter restrictions on the daily lives of millions of people, but we do not yet understand what measures are the most effective. Zhang et al. modeled virus transmission in Wuhan, China, in February 2020, investigating the effects of interventions ranging from patient management to social isolation. Age-mixing patterns were estimated by contact surveys conducted in Wuhan and Shanghai at the beginning of February 2020. Once people reduced their average daily contacts from 14 to 20 down to 2, transmission rapidly fell below the epidemic threshold. The model also showed that preemptive school closures helped to reduce transmission, although alone they would not prevent a COVID-19 outbreak. Limiting human mixing to within households appeared to be the most effective measure.Science, this issue p. 1481Intense 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. 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. The coronavirus 2019 (COVID-19) pandemic has brought tighter restrictions on the daily lives of millions of people, but we do not yet understand what measures are the most effective. Zhang et al. modeled virus transmission in Wuhan, China, in February 2020, investigating the effects of interventions ranging from patient management to social isolation. Age-mixing patterns were estimated by contact surveys conducted in Wuhan and Shanghai at the beginning of February 2020. Once people reduced their average daily contacts from 14 to 20 down to 2, transmission rapidly fell below the epidemic threshold. The model also showed that preemptive school closures helped to reduce transmission, although alone they would not prevent a COVID-19 outbreak. Limiting human mixing to within households appeared to be the most effective measure. Science , this issue p. 1481 Reducing social contacts on the scale achieved in Wuhan and Shanghai in early 2020 could be sufficient to bring a COVID-19 outbreak under control. 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. |
| Author | Viboud, Cécile Ajelli, Marco Yu, Hongjie Litvinova, Maria Wang, Wei Merler, Stefano Wu, Qianhui Liang, Yuxia Zhao, Shanlu Vespignani, Alessandro Zhang, Juanjuan Wang, Yan |
| Author_xml | – sequence: 1 givenname: Juanjuan orcidid: 0000-0002-8600-754X surname: Zhang fullname: Zhang, Juanjuan organization: School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China – sequence: 2 givenname: Maria orcidid: 0000-0001-6393-1943 surname: Litvinova fullname: Litvinova, Maria organization: ISI Foundation, Turin, Italy – sequence: 3 givenname: Yuxia orcidid: 0000-0003-1713-0832 surname: Liang fullname: Liang, Yuxia organization: School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China – sequence: 4 givenname: Yan surname: Wang fullname: Wang, Yan organization: School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China – sequence: 5 givenname: Wei surname: Wang fullname: Wang, Wei organization: School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China – sequence: 6 givenname: Shanlu surname: Zhao fullname: Zhao, Shanlu organization: Hunan Provincial Center for Disease Control and Prevention, Changsha, China – sequence: 7 givenname: Qianhui surname: Wu fullname: Wu, Qianhui organization: School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China – sequence: 8 givenname: Stefano surname: Merler fullname: Merler, Stefano organization: Bruno Kessler Foundation, Trento, Italy – sequence: 9 givenname: Cécile orcidid: 0000-0003-3243-4711 surname: Viboud fullname: Viboud, Cécile organization: Division of International Epidemiology and Population Studies, Fogarty International Center, National Institutes of Health, Bethesda, MD, USA – sequence: 10 givenname: Alessandro orcidid: 0000-0003-3419-4205 surname: Vespignani fullname: Vespignani, Alessandro organization: ISI Foundation, Turin, Italy., Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, MA, USA – sequence: 11 givenname: Marco orcidid: 0000-0003-1753-4749 surname: Ajelli fullname: Ajelli, Marco organization: Bruno Kessler Foundation, Trento, Italy – sequence: 12 givenname: Hongjie orcidid: 0000-0002-6335-5648 surname: Yu fullname: Yu, Hongjie organization: School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/32350060$$D View this record in MEDLINE/PubMed |
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| Snippet | The coronavirus 2019 (COVID-19) pandemic has brought tighter restrictions on the daily lives of millions of people, but we do not yet understand what measures... Intense nonpharmaceutical interventions were put in place in China to stop transmission of the novel coronavirus disease 2019 (COVID-19). As transmission... Who and what next?The coronavirus 2019 (COVID-19) pandemic has brought tighter restrictions on the daily lives of millions of people, but we do not yet... |
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| SubjectTerms | Adolescent Adult Age Age Factors Aged Behavior Betacoronavirus Child Child, Preschool China - epidemiology Closures Communicable Disease Control Confidence intervals Contact Tracing Coronaviridae Coronavirus Infections - epidemiology Coronavirus Infections - prevention & control Coronavirus Infections - transmission Coronaviruses COVID-19 Disease control Disease Outbreaks Disease Susceptibility Disease transmission Epidemics Epidemiology Female Households Humans Infant Infections Male Medicine Middle Aged Models, Theoretical Outbreaks Pandemics Pandemics - prevention & control Pneumonia, Viral - epidemiology Pneumonia, Viral - prevention & control Pneumonia, Viral - transmission Polls & surveys Preempting Respiratory diseases SARS-CoV-2 School Closing School closures Schools Severe acute respiratory syndrome coronavirus 2 Social distancing Social interactions Viral diseases Viruses Workplace Young Adult |
| Title | Changes in contact patterns shape the dynamics of the COVID-19 outbreak in China |
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