Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Drake, John M., Handel, Andreas, Marty, Éric, O’Dea, Eamon B., O’Sullivan, Tierney, Righi, Giovanni, Tredennick, Andrew T.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2023
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
_version_ 1785137541092999168
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
work_keys_str_mv AT drakejohnm adatadrivensemiparametricmodelofsarscov2transmissionintheunitedstates
AT handelandreas adatadrivensemiparametricmodelofsarscov2transmissionintheunitedstates
AT martyeric adatadrivensemiparametricmodelofsarscov2transmissionintheunitedstates
AT odeaeamonb adatadrivensemiparametricmodelofsarscov2transmissionintheunitedstates
AT osullivantierney adatadrivensemiparametricmodelofsarscov2transmissionintheunitedstates
AT righigiovanni adatadrivensemiparametricmodelofsarscov2transmissionintheunitedstates
AT tredennickandrewt adatadrivensemiparametricmodelofsarscov2transmissionintheunitedstates
AT drakejohnm datadrivensemiparametricmodelofsarscov2transmissionintheunitedstates
AT handelandreas datadrivensemiparametricmodelofsarscov2transmissionintheunitedstates
AT martyeric datadrivensemiparametricmodelofsarscov2transmissionintheunitedstates
AT odeaeamonb datadrivensemiparametricmodelofsarscov2transmissionintheunitedstates
AT osullivantierney datadrivensemiparametricmodelofsarscov2transmissionintheunitedstates
AT righigiovanni datadrivensemiparametricmodelofsarscov2transmissionintheunitedstates
AT tredennickandrewt datadrivensemiparametricmodelofsarscov2transmissionintheunitedstates