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A data-driven semi-parametric model of SARS-CoV-2 transmission in the United States
To support decision-making and policy for managing epidemics of emerging pathogens, we present a model for inference and scenario analysis of SARS-CoV-2 transmission in the USA. The stochastic SEIR-type model includes compartments for latent, asymptomatic, detected and undetected symptomatic individ...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10659176/ https://www.ncbi.nlm.nih.gov/pubmed/37939201 http://dx.doi.org/10.1371/journal.pcbi.1011610 |
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author | Drake, John M. Handel, Andreas Marty, Éric O’Dea, Eamon B. O’Sullivan, Tierney Righi, Giovanni Tredennick, Andrew T. |
author_facet | Drake, John M. Handel, Andreas Marty, Éric O’Dea, Eamon B. O’Sullivan, Tierney Righi, Giovanni Tredennick, Andrew T. |
author_sort | Drake, John M. |
collection | PubMed |
description | To support decision-making and policy for managing epidemics of emerging pathogens, we present a model for inference and scenario analysis of SARS-CoV-2 transmission in the USA. The stochastic SEIR-type model includes compartments for latent, asymptomatic, detected and undetected symptomatic individuals, and hospitalized cases, and features realistic interval distributions for presymptomatic and symptomatic periods, time varying rates of case detection, diagnosis, and mortality. The model accounts for the effects on transmission of human mobility using anonymized mobility data collected from cellular devices, and of difficult to quantify environmental and behavioral factors using a latent process. The baseline transmission rate is the product of a human mobility metric obtained from data and this fitted latent process. We fit the model to incident case and death reports for each state in the USA and Washington D.C., using likelihood Maximization by Iterated particle Filtering (MIF). Observations (daily case and death reports) are modeled as arising from a negative binomial reporting process. We estimate time-varying transmission rate, parameters of a sigmoidal time-varying fraction of hospitalized cases that result in death, extra-demographic process noise, two dispersion parameters of the observation process, and the initial sizes of the latent, asymptomatic, and symptomatic classes. In a retrospective analysis covering March–December 2020, we show how mobility and transmission strength became decoupled across two distinct phases of the pandemic. The decoupling demonstrates the need for flexible, semi-parametric approaches for modeling infectious disease dynamics in real-time. |
format | Online Article Text |
id | pubmed-10659176 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-106591762023-11-08 A data-driven semi-parametric model of SARS-CoV-2 transmission in the United States Drake, John M. Handel, Andreas Marty, Éric O’Dea, Eamon B. O’Sullivan, Tierney Righi, Giovanni Tredennick, Andrew T. PLoS Comput Biol Research Article To support decision-making and policy for managing epidemics of emerging pathogens, we present a model for inference and scenario analysis of SARS-CoV-2 transmission in the USA. The stochastic SEIR-type model includes compartments for latent, asymptomatic, detected and undetected symptomatic individuals, and hospitalized cases, and features realistic interval distributions for presymptomatic and symptomatic periods, time varying rates of case detection, diagnosis, and mortality. The model accounts for the effects on transmission of human mobility using anonymized mobility data collected from cellular devices, and of difficult to quantify environmental and behavioral factors using a latent process. The baseline transmission rate is the product of a human mobility metric obtained from data and this fitted latent process. We fit the model to incident case and death reports for each state in the USA and Washington D.C., using likelihood Maximization by Iterated particle Filtering (MIF). Observations (daily case and death reports) are modeled as arising from a negative binomial reporting process. We estimate time-varying transmission rate, parameters of a sigmoidal time-varying fraction of hospitalized cases that result in death, extra-demographic process noise, two dispersion parameters of the observation process, and the initial sizes of the latent, asymptomatic, and symptomatic classes. In a retrospective analysis covering March–December 2020, we show how mobility and transmission strength became decoupled across two distinct phases of the pandemic. The decoupling demonstrates the need for flexible, semi-parametric approaches for modeling infectious disease dynamics in real-time. Public Library of Science 2023-11-08 /pmc/articles/PMC10659176/ /pubmed/37939201 http://dx.doi.org/10.1371/journal.pcbi.1011610 Text en © 2023 Drake et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Drake, John M. Handel, Andreas Marty, Éric O’Dea, Eamon B. O’Sullivan, Tierney Righi, Giovanni Tredennick, Andrew T. A data-driven semi-parametric model of SARS-CoV-2 transmission in the United States |
title | A data-driven semi-parametric model of SARS-CoV-2 transmission in the United States |
title_full | A data-driven semi-parametric model of SARS-CoV-2 transmission in the United States |
title_fullStr | A data-driven semi-parametric model of SARS-CoV-2 transmission in the United States |
title_full_unstemmed | A data-driven semi-parametric model of SARS-CoV-2 transmission in the United States |
title_short | A data-driven semi-parametric model of SARS-CoV-2 transmission in the United States |
title_sort | data-driven semi-parametric model of sars-cov-2 transmission in the united states |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10659176/ https://www.ncbi.nlm.nih.gov/pubmed/37939201 http://dx.doi.org/10.1371/journal.pcbi.1011610 |
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