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Chopping the tail: how preventing superspreading can help to maintain COVID-19 control
Disease transmission is notoriously heterogeneous, and SARS-CoV-2 is no exception. A skewed distribution where few individuals or events are responsible for the majority of transmission can result in explosive, superspreading events, which produce rapid and volatile epidemic dynamics, especially ear...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Cold Spring Harbor Laboratory
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7340192/ https://www.ncbi.nlm.nih.gov/pubmed/32637966 http://dx.doi.org/10.1101/2020.06.30.20143115 |
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author | Kain, Morgan P. Childs, Marissa L. Becker, Alexander D. Mordecai, Erin A. |
author_facet | Kain, Morgan P. Childs, Marissa L. Becker, Alexander D. Mordecai, Erin A. |
author_sort | Kain, Morgan P. |
collection | PubMed |
description | Disease transmission is notoriously heterogeneous, and SARS-CoV-2 is no exception. A skewed distribution where few individuals or events are responsible for the majority of transmission can result in explosive, superspreading events, which produce rapid and volatile epidemic dynamics, especially early or late in epidemics. Anticipating and preventing superspreading events can produce large reductions in overall transmission rates. Here, we present a compartmental (SEIR) epidemiological model framework for estimating transmission parameters from multiple imperfectly observed data streams, including reported cases, deaths, and mobile phone-based mobility that incorporates individual-level heterogeneity in transmission using previous estimates for SARS-CoV-1 and SARS-CoV-2. We parameterize the model for COVID-19 epidemic dynamics by estimating a time-varying transmission rate that incorporates the impact of non-pharmaceutical intervention strategies that change over time, in five epidemiologically distinct settings—Los Angeles and Santa Clara Counties, California; Seattle (King County), Washington; Atlanta (Dekalb and Fulton Counties), Georgia; and Miami (Miami-Dade County), Florida. We find the effective reproduction number [Formula: see text] dropped below 1 rapidly following social distancing orders in mid-March, 2020 and remained there into June in Santa Clara County and Seattle, but climbed above 1 in late May in Los Angeles, Miami, and Atlanta, and has trended upward in all locations since April. With the fitted model, we ask: how does truncating the tail of the individual-level transmission rate distribution affect epidemic dynamics and control? We find interventions that truncate the transmission rate distribution while partially relaxing social distancing are broadly effective, with impacts on epidemic growth on par with the strongest population-wide social distancing observed in April, 2020. Given that social distancing interventions will be needed to maintain epidemic control until a vaccine becomes widely available, “chopping off the tail” to reduce the probability of superspreading events presents a promising option to alleviate the need for extreme general social distancing. |
format | Online Article Text |
id | pubmed-7340192 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Cold Spring Harbor Laboratory |
record_format | MEDLINE/PubMed |
spelling | pubmed-73401922020-07-07 Chopping the tail: how preventing superspreading can help to maintain COVID-19 control Kain, Morgan P. Childs, Marissa L. Becker, Alexander D. Mordecai, Erin A. medRxiv Article Disease transmission is notoriously heterogeneous, and SARS-CoV-2 is no exception. A skewed distribution where few individuals or events are responsible for the majority of transmission can result in explosive, superspreading events, which produce rapid and volatile epidemic dynamics, especially early or late in epidemics. Anticipating and preventing superspreading events can produce large reductions in overall transmission rates. Here, we present a compartmental (SEIR) epidemiological model framework for estimating transmission parameters from multiple imperfectly observed data streams, including reported cases, deaths, and mobile phone-based mobility that incorporates individual-level heterogeneity in transmission using previous estimates for SARS-CoV-1 and SARS-CoV-2. We parameterize the model for COVID-19 epidemic dynamics by estimating a time-varying transmission rate that incorporates the impact of non-pharmaceutical intervention strategies that change over time, in five epidemiologically distinct settings—Los Angeles and Santa Clara Counties, California; Seattle (King County), Washington; Atlanta (Dekalb and Fulton Counties), Georgia; and Miami (Miami-Dade County), Florida. We find the effective reproduction number [Formula: see text] dropped below 1 rapidly following social distancing orders in mid-March, 2020 and remained there into June in Santa Clara County and Seattle, but climbed above 1 in late May in Los Angeles, Miami, and Atlanta, and has trended upward in all locations since April. With the fitted model, we ask: how does truncating the tail of the individual-level transmission rate distribution affect epidemic dynamics and control? We find interventions that truncate the transmission rate distribution while partially relaxing social distancing are broadly effective, with impacts on epidemic growth on par with the strongest population-wide social distancing observed in April, 2020. Given that social distancing interventions will be needed to maintain epidemic control until a vaccine becomes widely available, “chopping off the tail” to reduce the probability of superspreading events presents a promising option to alleviate the need for extreme general social distancing. Cold Spring Harbor Laboratory 2020-07-03 /pmc/articles/PMC7340192/ /pubmed/32637966 http://dx.doi.org/10.1101/2020.06.30.20143115 Text en http://creativecommons.org/licenses/by/4.0/It is made available under a CC-BY 4.0 International license (http://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Kain, Morgan P. Childs, Marissa L. Becker, Alexander D. Mordecai, Erin A. Chopping the tail: how preventing superspreading can help to maintain COVID-19 control |
title | Chopping the tail: how preventing superspreading can help to maintain COVID-19 control |
title_full | Chopping the tail: how preventing superspreading can help to maintain COVID-19 control |
title_fullStr | Chopping the tail: how preventing superspreading can help to maintain COVID-19 control |
title_full_unstemmed | Chopping the tail: how preventing superspreading can help to maintain COVID-19 control |
title_short | Chopping the tail: how preventing superspreading can help to maintain COVID-19 control |
title_sort | chopping the tail: how preventing superspreading can help to maintain covid-19 control |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7340192/ https://www.ncbi.nlm.nih.gov/pubmed/32637966 http://dx.doi.org/10.1101/2020.06.30.20143115 |
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