{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,27]],"date-time":"2026-09-27T12:12:37Z","timestamp":1790511157363,"version":"4.1.0"},"update-to":[{"DOI":"10.1371\/journal.pcbi.1008684","type":"new_version","label":"New version","source":"publisher","updated":{"date-parts":[[2021,2,16]],"date-time":"2021-02-16T00:00:00Z","timestamp":1613433600000}}],"reference-count":87,"publisher":"Public Library of Science (PLoS)","issue":"2","license":[{"start":{"date-parts":[[2021,2,3]],"date-time":"2021-02-03T00:00:00Z","timestamp":1612310400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["DP5OD019851"],"award-info":[{"award-number":["DP5OD019851"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R01AI146129"],"award-info":[{"award-number":["R01AI146129"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R01AI101229"],"award-info":[{"award-number":["R01AI101229"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["www.ploscompbiol.org"],"crossmark-restriction":false},"short-container-title":["PLoS Comput Biol"],"abstract":"<jats:p>In the absence of pharmaceutical interventions, social distancing is being used worldwide to curb the spread of COVID-19. The impact of these measures has been inconsistent, with some regions rapidly nearing disease elimination and others seeing delayed peaks or nearly flat epidemic curves. Here we build a stochastic epidemic model to examine the effects of COVID-19 clinical progression and transmission network structure on the outcomes of social distancing interventions. Our simulations show that long delays between the adoption of control measures and observed declines in cases, hospitalizations, and deaths occur in many scenarios. We find that the strength of within-household transmission is a critical determinant of success, governing the timing and size of the epidemic peak, the rate of decline, individual risks of infection, and the success of partial relaxation measures. The structure of residual external connections, driven by workforce participation and essential businesses, interacts to determine outcomes. We suggest limited conditions under which the formation of household \u201cbubbles\u201d can be safe. These findings can improve future predictions of the timescale and efficacy of interventions needed to control second waves of COVID-19 as well as other similar outbreaks, and highlight the need for better quantification and control of household transmission.<\/jats:p>","DOI":"10.1371\/journal.pcbi.1008684","type":"journal-article","created":{"date-parts":[[2021,2,3]],"date-time":"2021-02-03T14:36:56Z","timestamp":1612363016000},"page":"e1008684","update-policy":"https:\/\/doi.org\/10.1371\/journal.pcbi.corrections_policy","source":"Crossref","is-referenced-by-count":90,"title":["Dynamics of COVID-19 under social distancing measures are driven by transmission network structure"],"prefix":"10.1371","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1726-6633","authenticated-orcid":true,"given":"Anjalika","family":"Nande","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ben","family":"Adlam","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Justin","family":"Sheen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4661-3764","authenticated-orcid":true,"given":"Michael Z.","family":"Levy","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6583-3623","authenticated-orcid":true,"given":"Alison L.","family":"Hill","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2021,2,3]]},"reference":[{"key":"pcbi.1008684.ref001","doi-asserted-by":"crossref","first-page":"1123","DOI":"10.1007\/s10654-020-00698-1","article-title":"Assessing the age specificity of infection fatality rates for COVID-19: systematic review, meta-analysis, and public policy implications","volume":"35","author":"AT Levin","year":"2020","journal-title":"Eur J Epidemiol"},{"key":"pcbi.1008684.ref002","doi-asserted-by":"crossref","unstructured":"Tognotti E. Lessons from the History of Quarantine, from Plague to Influenza A\u2014Volume 19, Number 2\u2014February 2013\u2014Emerging Infectious Diseases journal\u2014CDC. doi: 10.3201\/eid1902.120312","DOI":"10.3201\/eid1902.120312"},{"key":"pcbi.1008684.ref003","doi-asserted-by":"crossref","first-page":"644","DOI":"10.1001\/jama.298.6.644","article-title":"Nonpharmaceutical interventions implemented by US cities during the 1918\u20131919 influenza pandemic","volume":"298","author":"H Markel","year":"2007","journal-title":"JAMA"},{"key":"pcbi.1008684.ref004","article-title":"Impact of non-pharmaceutical interventions (NPIs) to reduce COVID- 19 mortality and healthcare demand.","volume":"20","author":"NM Ferguson","year":"2020"},{"key":"pcbi.1008684.ref005","article-title":"Projecting the transmission dynamics of SARS-CoV-2 through the postpandemic period","author":"SM Kissler","year":"2020","journal-title":"Science"},{"key":"pcbi.1008684.ref006","doi-asserted-by":"crossref","first-page":"e375","DOI":"10.1016\/S2468-2667(20)30133-X","article-title":"Effects of non-pharmaceutical interventions on COVID-19 cases, deaths, and demand for hospital services in the UK: a modelling study","volume":"5","author":"NG Davies","year":"2020","journal-title":"Lancet Public Health."},{"key":"pcbi.1008684.ref007","first-page":"2020","article-title":"Initial Simulation of SARS-CoV2 Spread and Intervention Effects in the Continental US.","author":"S Pei","year":"2020","journal-title":"medRxiv."},{"key":"pcbi.1008684.ref008","article-title":"Association of Public Health Interventions With the Epidemiology of the COVID-19 Outbreak in Wuhan, China.","author":"A Pan","year":"2020","journal-title":"JAMA"},{"key":"pcbi.1008684.ref009","doi-asserted-by":"crossref","first-page":"638","DOI":"10.1126\/science.abb6105","article-title":"An investigation of transmission control measures during the first 50 days of the COVID-19 epidemic in China.","volume":"368","author":"H Tian","year":"2020","journal-title":"Science."},{"key":"pcbi.1008684.ref010","doi-asserted-by":"crossref","first-page":"e279","DOI":"10.1016\/S2468-2667(20)30090-6","article-title":"Impact assessment of non-pharmaceutical interventions against coronavirus disease 2019 and influenza in Hong Kong: an observational study.","volume":"5","author":"BJ Cowling","year":"2020","journal-title":"Lancet Public Health."},{"key":"pcbi.1008684.ref011","article-title":"Estimating the effects of non-pharmaceutical interventions on COVID-19 in Europe.","author":"S Flaxman","year":"2020","journal-title":"Nature"},{"key":"pcbi.1008684.ref012","first-page":"2020","article-title":"Multi-level modeling of early COVID-19 epidemic dynamics in French regions and estimation of the lockdown impact on infection rate.","author":"M Prague","year":"2020","journal-title":"medRxiv."},{"key":"pcbi.1008684.ref013","unstructured":"Imperial College COVID-19 Response Team. 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