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Tracking the transmission dynamics of COVID‐19 with a time‐varying coefficient state‐space model

The spread of COVID‐19 has been greatly impacted by regulatory policies and behavior patterns that vary across counties, states, and countries. Population‐level dynamics of COVID‐19 can generally be described using a set of ordinary differential equations, but these deterministic equations are insuf...

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Autores principales: Keller, Joshua P., Zhou, Tianjian, Kaplan, Andee, Anderson, G. Brooke, Zhou, Wen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9111166/
https://www.ncbi.nlm.nih.gov/pubmed/35322455
http://dx.doi.org/10.1002/sim.9382
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author Keller, Joshua P.
Zhou, Tianjian
Kaplan, Andee
Anderson, G. Brooke
Zhou, Wen
author_facet Keller, Joshua P.
Zhou, Tianjian
Kaplan, Andee
Anderson, G. Brooke
Zhou, Wen
author_sort Keller, Joshua P.
collection PubMed
description The spread of COVID‐19 has been greatly impacted by regulatory policies and behavior patterns that vary across counties, states, and countries. Population‐level dynamics of COVID‐19 can generally be described using a set of ordinary differential equations, but these deterministic equations are insufficient for modeling the observed case rates, which can vary due to local testing and case reporting policies and nonhomogeneous behavior among individuals. To assess the impact of population mobility on the spread of COVID‐19, we have developed a novel Bayesian time‐varying coefficient state‐space model for infectious disease transmission. The foundation of this model is a time‐varying coefficient compartment model to recapitulate the dynamics among susceptible, exposed, undetected infectious, detected infectious, undetected removed, hospitalized, detected recovered, and detected deceased individuals. The infectiousness and detection parameters are modeled to vary by time, and the infectiousness component in the model incorporates information on multiple sources of population mobility. Along with this compartment model, a multiplicative process model is introduced to allow for deviation from the deterministic dynamics. We apply this model to observed COVID‐19 cases and deaths in several U.S. states and Colorado counties. We find that population mobility measures are highly correlated with transmission rates and can explain complicated temporal variation in infectiousness in these regions. Additionally, the inferred connections between mobility and epidemiological parameters, varying across locations, have revealed the heterogeneous effects of different policies on the dynamics of COVID‐19.
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spelling pubmed-91111662022-05-17 Tracking the transmission dynamics of COVID‐19 with a time‐varying coefficient state‐space model Keller, Joshua P. Zhou, Tianjian Kaplan, Andee Anderson, G. Brooke Zhou, Wen Stat Med Research Articles The spread of COVID‐19 has been greatly impacted by regulatory policies and behavior patterns that vary across counties, states, and countries. Population‐level dynamics of COVID‐19 can generally be described using a set of ordinary differential equations, but these deterministic equations are insufficient for modeling the observed case rates, which can vary due to local testing and case reporting policies and nonhomogeneous behavior among individuals. To assess the impact of population mobility on the spread of COVID‐19, we have developed a novel Bayesian time‐varying coefficient state‐space model for infectious disease transmission. The foundation of this model is a time‐varying coefficient compartment model to recapitulate the dynamics among susceptible, exposed, undetected infectious, detected infectious, undetected removed, hospitalized, detected recovered, and detected deceased individuals. The infectiousness and detection parameters are modeled to vary by time, and the infectiousness component in the model incorporates information on multiple sources of population mobility. Along with this compartment model, a multiplicative process model is introduced to allow for deviation from the deterministic dynamics. We apply this model to observed COVID‐19 cases and deaths in several U.S. states and Colorado counties. We find that population mobility measures are highly correlated with transmission rates and can explain complicated temporal variation in infectiousness in these regions. Additionally, the inferred connections between mobility and epidemiological parameters, varying across locations, have revealed the heterogeneous effects of different policies on the dynamics of COVID‐19. John Wiley and Sons Inc. 2022-03-23 2022-07-10 /pmc/articles/PMC9111166/ /pubmed/35322455 http://dx.doi.org/10.1002/sim.9382 Text en © 2022 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ (https://creativecommons.org/licenses/by-nc-nd/4.0/) License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made.
spellingShingle Research Articles
Keller, Joshua P.
Zhou, Tianjian
Kaplan, Andee
Anderson, G. Brooke
Zhou, Wen
Tracking the transmission dynamics of COVID‐19 with a time‐varying coefficient state‐space model
title Tracking the transmission dynamics of COVID‐19 with a time‐varying coefficient state‐space model
title_full Tracking the transmission dynamics of COVID‐19 with a time‐varying coefficient state‐space model
title_fullStr Tracking the transmission dynamics of COVID‐19 with a time‐varying coefficient state‐space model
title_full_unstemmed Tracking the transmission dynamics of COVID‐19 with a time‐varying coefficient state‐space model
title_short Tracking the transmission dynamics of COVID‐19 with a time‐varying coefficient state‐space model
title_sort tracking the transmission dynamics of covid‐19 with a time‐varying coefficient state‐space model
topic Research Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9111166/
https://www.ncbi.nlm.nih.gov/pubmed/35322455
http://dx.doi.org/10.1002/sim.9382
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