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Integrating psychosocial variables and societal diversity in epidemic models for predicting COVID-19 transmission dynamics
During the current COVID-19 pandemic, governments must make decisions based on a variety of information including estimations of infection spread, health care capacity, economic and psychosocial considerations. The disparate validity of current short-term forecasts of these factors is a major challe...
Autores principales: | , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9931295/ https://www.ncbi.nlm.nih.gov/pubmed/36812584 http://dx.doi.org/10.1371/journal.pdig.0000098 |
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author | Jirsa, Viktor K. Petkoski, Spase Wang, Huifang Woodman, Marmaduke Fousek, Jan Betsch, Cornelia Felgendreff, Lisa Bohm, Robert Lilleholt, Lau Zettler, Ingo Faber, Sarah Shen, Kelly Mcintosh, Anthony Randal |
author_facet | Jirsa, Viktor K. Petkoski, Spase Wang, Huifang Woodman, Marmaduke Fousek, Jan Betsch, Cornelia Felgendreff, Lisa Bohm, Robert Lilleholt, Lau Zettler, Ingo Faber, Sarah Shen, Kelly Mcintosh, Anthony Randal |
author_sort | Jirsa, Viktor K. |
collection | PubMed |
description | During the current COVID-19 pandemic, governments must make decisions based on a variety of information including estimations of infection spread, health care capacity, economic and psychosocial considerations. The disparate validity of current short-term forecasts of these factors is a major challenge to governments. By causally linking an established epidemiological spread model with dynamically evolving psychosocial variables, using Bayesian inference we estimate the strength and direction of these interactions for German and Danish data of disease spread, human mobility, and psychosocial factors based on the serial cross-sectional COVID-19 Snapshot Monitoring (COSMO; N = 16,981). We demonstrate that the strength of cumulative influence of psychosocial variables on infection rates is of a similar magnitude as the influence of physical distancing. We further show that the efficacy of political interventions to contain the disease strongly depends on societal diversity, in particular group-specific sensitivity to affective risk perception. As a consequence, the model may assist in quantifying the effect and timing of interventions, forecasting future scenarios, and differentiating the impact on diverse groups as a function of their societal organization. Importantly, the careful handling of societal factors, including support to the more vulnerable groups, adds another direct instrument to the battery of political interventions fighting epidemic spread. |
format | Online Article Text |
id | pubmed-9931295 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-99312952023-02-16 Integrating psychosocial variables and societal diversity in epidemic models for predicting COVID-19 transmission dynamics Jirsa, Viktor K. Petkoski, Spase Wang, Huifang Woodman, Marmaduke Fousek, Jan Betsch, Cornelia Felgendreff, Lisa Bohm, Robert Lilleholt, Lau Zettler, Ingo Faber, Sarah Shen, Kelly Mcintosh, Anthony Randal PLOS Digit Health Research Article During the current COVID-19 pandemic, governments must make decisions based on a variety of information including estimations of infection spread, health care capacity, economic and psychosocial considerations. The disparate validity of current short-term forecasts of these factors is a major challenge to governments. By causally linking an established epidemiological spread model with dynamically evolving psychosocial variables, using Bayesian inference we estimate the strength and direction of these interactions for German and Danish data of disease spread, human mobility, and psychosocial factors based on the serial cross-sectional COVID-19 Snapshot Monitoring (COSMO; N = 16,981). We demonstrate that the strength of cumulative influence of psychosocial variables on infection rates is of a similar magnitude as the influence of physical distancing. We further show that the efficacy of political interventions to contain the disease strongly depends on societal diversity, in particular group-specific sensitivity to affective risk perception. As a consequence, the model may assist in quantifying the effect and timing of interventions, forecasting future scenarios, and differentiating the impact on diverse groups as a function of their societal organization. Importantly, the careful handling of societal factors, including support to the more vulnerable groups, adds another direct instrument to the battery of political interventions fighting epidemic spread. Public Library of Science 2022-08-31 /pmc/articles/PMC9931295/ /pubmed/36812584 http://dx.doi.org/10.1371/journal.pdig.0000098 Text en © 2022 Jirsa 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 Jirsa, Viktor K. Petkoski, Spase Wang, Huifang Woodman, Marmaduke Fousek, Jan Betsch, Cornelia Felgendreff, Lisa Bohm, Robert Lilleholt, Lau Zettler, Ingo Faber, Sarah Shen, Kelly Mcintosh, Anthony Randal Integrating psychosocial variables and societal diversity in epidemic models for predicting COVID-19 transmission dynamics |
title | Integrating psychosocial variables and societal diversity in epidemic models for predicting COVID-19 transmission dynamics |
title_full | Integrating psychosocial variables and societal diversity in epidemic models for predicting COVID-19 transmission dynamics |
title_fullStr | Integrating psychosocial variables and societal diversity in epidemic models for predicting COVID-19 transmission dynamics |
title_full_unstemmed | Integrating psychosocial variables and societal diversity in epidemic models for predicting COVID-19 transmission dynamics |
title_short | Integrating psychosocial variables and societal diversity in epidemic models for predicting COVID-19 transmission dynamics |
title_sort | integrating psychosocial variables and societal diversity in epidemic models for predicting covid-19 transmission dynamics |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9931295/ https://www.ncbi.nlm.nih.gov/pubmed/36812584 http://dx.doi.org/10.1371/journal.pdig.0000098 |
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