Modeling non-pharmaceutical interventions in the COVID-19 pandemic with survey-based simulations

Governments around the globe use non-pharmaceutical interventions (NPIs) to curb the spread of coronavirus disease 2019 (COVID-19) cases. Making decisions under uncertainty, they all face the same temporal paradox: estimating the impact of NPIs before they have been implemented. Due to the limited v...

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Autores principales:	Marius Kaffai, Raphael H. Heiberger
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/7a413bec9b4542bc8431f670b8cd1065
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spelling	oai:doaj.org-article:7a413bec9b4542bc8431f670b8cd10652021-11-04T06:49:32ZModeling non-pharmaceutical interventions in the COVID-19 pandemic with survey-based simulations1932-6203https://doaj.org/article/7a413bec9b4542bc8431f670b8cd10652021-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC8553158/?tool=EBIhttps://doaj.org/toc/1932-6203Governments around the globe use non-pharmaceutical interventions (NPIs) to curb the spread of coronavirus disease 2019 (COVID-19) cases. Making decisions under uncertainty, they all face the same temporal paradox: estimating the impact of NPIs before they have been implemented. Due to the limited variance of empirical cases, researchers could so far not disentangle effects of individual NPIs or their impact on different demographic groups. In this paper, we utilize large-scale agent-based simulations in combination with Susceptible-Exposed-Infectious-Recovered (SEIR) models to investigate the spread of COVID-19 for some of the most affected federal states in Germany. In contrast to other studies, we sample agents from a representative survey. Including more realistic demographic attributes that influence agents’ behavior yields accurate predictions of COVID-19 transmissions and allows us to investigate counterfactual what-if scenarios. Results show that quarantining infected people and exploiting industry-specific home office capacities are the most effective NPIs. Disentangling education-related NPIs reveals that each considered institution (kindergarten, school, university) has rather small effects on its own, yet, that combined openings would result in large increases in COVID-19 cases. Representative survey-characteristics of agents also allow us to estimate NPIs’ effects on different age groups. For instance, re-opening schools would cause comparatively few infections among the risk-group of people older than 60 years.Marius KaffaiRaphael H. HeibergerPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 10 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Marius Kaffai Raphael H. Heiberger Modeling non-pharmaceutical interventions in the COVID-19 pandemic with survey-based simulations
description	Governments around the globe use non-pharmaceutical interventions (NPIs) to curb the spread of coronavirus disease 2019 (COVID-19) cases. Making decisions under uncertainty, they all face the same temporal paradox: estimating the impact of NPIs before they have been implemented. Due to the limited variance of empirical cases, researchers could so far not disentangle effects of individual NPIs or their impact on different demographic groups. In this paper, we utilize large-scale agent-based simulations in combination with Susceptible-Exposed-Infectious-Recovered (SEIR) models to investigate the spread of COVID-19 for some of the most affected federal states in Germany. In contrast to other studies, we sample agents from a representative survey. Including more realistic demographic attributes that influence agents’ behavior yields accurate predictions of COVID-19 transmissions and allows us to investigate counterfactual what-if scenarios. Results show that quarantining infected people and exploiting industry-specific home office capacities are the most effective NPIs. Disentangling education-related NPIs reveals that each considered institution (kindergarten, school, university) has rather small effects on its own, yet, that combined openings would result in large increases in COVID-19 cases. Representative survey-characteristics of agents also allow us to estimate NPIs’ effects on different age groups. For instance, re-opening schools would cause comparatively few infections among the risk-group of people older than 60 years.
format	article
author	Marius Kaffai Raphael H. Heiberger
author_facet	Marius Kaffai Raphael H. Heiberger
author_sort	Marius Kaffai
title	Modeling non-pharmaceutical interventions in the COVID-19 pandemic with survey-based simulations
title_short	Modeling non-pharmaceutical interventions in the COVID-19 pandemic with survey-based simulations
title_full	Modeling non-pharmaceutical interventions in the COVID-19 pandemic with survey-based simulations
title_fullStr	Modeling non-pharmaceutical interventions in the COVID-19 pandemic with survey-based simulations
title_full_unstemmed	Modeling non-pharmaceutical interventions in the COVID-19 pandemic with survey-based simulations
title_sort	modeling non-pharmaceutical interventions in the covid-19 pandemic with survey-based simulations
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/7a413bec9b4542bc8431f670b8cd1065
work_keys_str_mv	AT mariuskaffai modelingnonpharmaceuticalinterventionsinthecovid19pandemicwithsurveybasedsimulations AT raphaelhheiberger modelingnonpharmaceuticalinterventionsinthecovid19pandemicwithsurveybasedsimulations
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Modeling non-pharmaceutical interventions in the COVID-19 pandemic with survey-based simulations

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