Macro-level drivers of SARS-CoV-2 transmission

Background: Many questions remain unanswered about how SARS-CoV-2 transmission is influenced by aspects of the economy, environment, and health. A better understanding of how these factors interact can help us to design early health prevention and control strategies, and develop better predictive mo...

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Detalles Bibliográficos
Autor: Watts, Matthew|||0000-0003-0364-9406
Tipo de recurso: artículo
Fecha de publicación:2022
País:España
Institución:Universitat Autònoma de Barcelona
Repositorio:Dipòsit Digital de Documents de la UAB
Idioma:inglés
OAI Identifier:oai:ddd.uab.cat:268187
Acceso en línea:https://ddd.uab.cat/record/268187
https://dx.doi.org/urn:doi:10.1016/j.sste.2022.100539
Access Level:acceso abierto
Palabra clave:SARS-CoV-2
COVID-19
Epidemic-growth
Doubling-time
United States
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spelling Macro-level drivers of SARS-CoV-2 transmissionA data-driven analysis of factors contributing to epidemic growth during the first wave of outbreaks in the United StatesWatts, Matthew|||0000-0003-0364-9406SARS-CoV-2COVID-19Epidemic-growthDoubling-timeUnited StatesBackground: Many questions remain unanswered about how SARS-CoV-2 transmission is influenced by aspects of the economy, environment, and health. A better understanding of how these factors interact can help us to design early health prevention and control strategies, and develop better predictive models for public health risk management of SARS-CoV-2. This study examines the associations between COVID-19 epidemic growth and macro-level determinants of transmission such as demographic, socio-economic, climate and health factors, during the first wave of outbreaks in the United States. Methods: A spatial-temporal data-set was created from a variety of relevant data sources. A unique data-driven study design was implemented to assess the relationship between COVID-19 infection and death epidemic doubling times and explanatory variables using a Generalized Additive Model (GAM). Results: The main factors associated with infection doubling times are higher population density, home overcrowding, manufacturing, and recreation industries. Poverty was also an important predictor of faster epidemic growth perhaps because of factors associated with in-work poverty-related conditions, although poverty is also a predictor of poor population health which is likely driving infection and death reporting. Air pollution and diabetes were other important drivers of infection reporting. Warmer temperatures are associated with slower epidemic growth, which is most likely explained by human behaviors associated with warmer locations i.e. ventilating homes and workplaces, and socializing outdoors. The main factors associated with death doubling times were population density, poverty, older age, diabetes, and air pollution. Temperature was also slightly significant slowing death doubling times. Conclusions: Such findings help underpin current understanding of the disease epidemiology and also supports current policy and advice recommending ventilation of homes, work-spaces, and schools, along with social distancing and mask-wearing. Given the strong associations between doubling times and the stringency index, it is likely that those states that responded to the virus more quickly by implementing a range of measures such as school closing, workplace closing, restrictions on gatherings, close public transport, restrictions on internal movement, international travel controls, and public information campaigns, did have some success slowing the spread of the virus. 22022-01-0120222022-01-01Articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://ddd.uab.cat/record/268187https://dx.doi.org/urn:doi:10.1016/j.sste.2022.100539reponame:Dipòsit Digital de Documents de la UABinstname:Universitat Autònoma de BarcelonaInglésengopen accesshttp://purl.org/coar/access_right/c_abf2Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original.https://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:ddd.uab.cat:2681872026-06-06T12:50:31Z
dc.title.none.fl_str_mv Macro-level drivers of SARS-CoV-2 transmission
A data-driven analysis of factors contributing to epidemic growth during the first wave of outbreaks in the United States
title Macro-level drivers of SARS-CoV-2 transmission
spellingShingle Macro-level drivers of SARS-CoV-2 transmission
Watts, Matthew|||0000-0003-0364-9406
SARS-CoV-2
COVID-19
Epidemic-growth
Doubling-time
United States
title_short Macro-level drivers of SARS-CoV-2 transmission
title_full Macro-level drivers of SARS-CoV-2 transmission
title_fullStr Macro-level drivers of SARS-CoV-2 transmission
title_full_unstemmed Macro-level drivers of SARS-CoV-2 transmission
title_sort Macro-level drivers of SARS-CoV-2 transmission
dc.creator.none.fl_str_mv Watts, Matthew|||0000-0003-0364-9406
author Watts, Matthew|||0000-0003-0364-9406
author_facet Watts, Matthew|||0000-0003-0364-9406
author_role author
dc.subject.none.fl_str_mv SARS-CoV-2
COVID-19
Epidemic-growth
Doubling-time
United States
topic SARS-CoV-2
COVID-19
Epidemic-growth
Doubling-time
United States
description Background: Many questions remain unanswered about how SARS-CoV-2 transmission is influenced by aspects of the economy, environment, and health. A better understanding of how these factors interact can help us to design early health prevention and control strategies, and develop better predictive models for public health risk management of SARS-CoV-2. This study examines the associations between COVID-19 epidemic growth and macro-level determinants of transmission such as demographic, socio-economic, climate and health factors, during the first wave of outbreaks in the United States. Methods: A spatial-temporal data-set was created from a variety of relevant data sources. A unique data-driven study design was implemented to assess the relationship between COVID-19 infection and death epidemic doubling times and explanatory variables using a Generalized Additive Model (GAM). Results: The main factors associated with infection doubling times are higher population density, home overcrowding, manufacturing, and recreation industries. Poverty was also an important predictor of faster epidemic growth perhaps because of factors associated with in-work poverty-related conditions, although poverty is also a predictor of poor population health which is likely driving infection and death reporting. Air pollution and diabetes were other important drivers of infection reporting. Warmer temperatures are associated with slower epidemic growth, which is most likely explained by human behaviors associated with warmer locations i.e. ventilating homes and workplaces, and socializing outdoors. The main factors associated with death doubling times were population density, poverty, older age, diabetes, and air pollution. Temperature was also slightly significant slowing death doubling times. Conclusions: Such findings help underpin current understanding of the disease epidemiology and also supports current policy and advice recommending ventilation of homes, work-spaces, and schools, along with social distancing and mask-wearing. Given the strong associations between doubling times and the stringency index, it is likely that those states that responded to the virus more quickly by implementing a range of measures such as school closing, workplace closing, restrictions on gatherings, close public transport, restrictions on internal movement, international travel controls, and public information campaigns, did have some success slowing the spread of the virus.
publishDate 2022
dc.date.none.fl_str_mv 2
2022-01-01
2022
2022-01-01
dc.type.none.fl_str_mv Article
http://purl.org/coar/resource_type/c_6501
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://ddd.uab.cat/record/268187
https://dx.doi.org/urn:doi:10.1016/j.sste.2022.100539
url https://ddd.uab.cat/record/268187
https://dx.doi.org/urn:doi:10.1016/j.sste.2022.100539
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
https://creativecommons.org/licenses/by/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
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eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:Dipòsit Digital de Documents de la UAB
instname:Universitat Autònoma de Barcelona
instname_str Universitat Autònoma de Barcelona
reponame_str Dipòsit Digital de Documents de la UAB
collection Dipòsit Digital de Documents de la UAB
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