Relative humidity predicts day-to-day variations in COVID-19 cases in the city of Buenos Aires

Autores
Pineda Rojas, Andrea Laura; Cordo, Sandra Myriam; Saurral, Ramiro Ignacio; Jimenez, Jose L.; Marr, Linsey C.; Kropff, Emilio
Año de publicación
2021
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Possible links between the transmission of COVID-19 and meteorology have been investigated by comparing positive cases across geographical regions or seasons. Little is known, however, about the degree to which environmental conditions modulate the daily dynamics of COVID-19 spread at a given location. One reason for this is that individual waves of the disease typically rise and decay too sharply, making it hard to isolate the contribution of meteorological cycles. To overcome this shortage, we here present a case study of the first wave of the outbreak in the city of Buenos Aires, which had a slow evolution of the caseload extending along most of 2020. We found that humidity plays a prominent role in modulating the variation of COVID-19 positive cases through a negative-slope linear relationship, with an optimal lag of 9 days between the meteorological observation and the positive case report. This relationship is specific to winter months, when relative humidity predicts up to half of the variance in positive case count. Our results provide a tool to anticipate possible local surges in COVID-19 cases after events of low humidity. More generally, they add to accumulating evidence pointing to dry air as a facilitator of COVID-19 transmission.
Fil: Pineda Rojas, Andrea Laura. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Centro de Investigaciones del Mar y la Atmósfera. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Centro de Investigaciones del Mar y la Atmósfera; Argentina
Fil: Cordo, Sandra Myriam. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales; Argentina
Fil: Saurral, Ramiro Ignacio. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Centro de Investigaciones del Mar y la Atmósfera. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Centro de Investigaciones del Mar y la Atmósfera; Argentina. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Ciencias de la Atmósfera y los Océanos; Argentina
Fil: Jimenez, Jose L.. State University of Colorado at Boulder; Estados Unidos
Fil: Marr, Linsey C.. Virginia Tech University; Estados Unidos
Fil: Kropff, Emilio. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Bioquímicas de Buenos Aires. Fundación Instituto Leloir. Instituto de Investigaciones Bioquímicas de Buenos Aires; Argentina
Materia
METEOROLOGY
RELATIVE HUMIDITY
AIRBORN TRANSMISSION
COVID-19
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/138480
Acceder
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repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Relative humidity predicts day-to-day variations in COVID-19 cases in the city of Buenos AiresPineda Rojas, Andrea LauraCordo, Sandra MyriamSaurral, Ramiro IgnacioJimenez, Jose L.Marr, Linsey C.Kropff, EmilioMETEOROLOGYRELATIVE HUMIDITYAIRBORN TRANSMISSIONCOVID-19https://purl.org/becyt/ford/3.5https://purl.org/becyt/ford/3Possible links between the transmission of COVID-19 and meteorology have been investigated by comparing positive cases across geographical regions or seasons. Little is known, however, about the degree to which environmental conditions modulate the daily dynamics of COVID-19 spread at a given location. One reason for this is that individual waves of the disease typically rise and decay too sharply, making it hard to isolate the contribution of meteorological cycles. To overcome this shortage, we here present a case study of the first wave of the outbreak in the city of Buenos Aires, which had a slow evolution of the caseload extending along most of 2020. We found that humidity plays a prominent role in modulating the variation of COVID-19 positive cases through a negative-slope linear relationship, with an optimal lag of 9 days between the meteorological observation and the positive case report. This relationship is specific to winter months, when relative humidity predicts up to half of the variance in positive case count. Our results provide a tool to anticipate possible local surges in COVID-19 cases after events of low humidity. More generally, they add to accumulating evidence pointing to dry air as a facilitator of COVID-19 transmission.Fil: Pineda Rojas, Andrea Laura. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Centro de Investigaciones del Mar y la Atmósfera. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Centro de Investigaciones del Mar y la Atmósfera; ArgentinaFil: Cordo, Sandra Myriam. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales; ArgentinaFil: Saurral, Ramiro Ignacio. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Centro de Investigaciones del Mar y la Atmósfera. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Centro de Investigaciones del Mar y la Atmósfera; Argentina. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Ciencias de la Atmósfera y los Océanos; ArgentinaFil: Jimenez, Jose L.. State University of Colorado at Boulder; Estados UnidosFil: Marr, Linsey C.. Virginia Tech University; Estados UnidosFil: Kropff, Emilio. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Bioquímicas de Buenos Aires. Fundación Instituto Leloir. Instituto de Investigaciones Bioquímicas de Buenos Aires; ArgentinaAmerican Chemical Society2021-07-30info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/138480Pineda Rojas, Andrea Laura; Cordo, Sandra Myriam; Saurral, Ramiro Ignacio; Jimenez, Jose L.; Marr, Linsey C.; et al.; Relative humidity predicts day-to-day variations in COVID-19 cases in the city of Buenos Aires; American Chemical Society; Environmental Science & Technology; 55; 16; 30-7-2021; 11176–111820013-936XCONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://pubs.acs.org/doi/10.1021/acs.est.1c02711info:eu-repo/semantics/altIdentifier/doi/10.1021/acs.est.1c02711info:eu-repo/semantics/altIdentifier/url/https://www.medrxiv.org/content/10.1101/2021.01.29.21250789v2.full.pdf+htmlinfo:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T09:59:56Zoai:ri.conicet.gov.ar:11336/138480instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-29 09:59:57.04CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Relative humidity predicts day-to-day variations in COVID-19 cases in the city of Buenos Aires
title Relative humidity predicts day-to-day variations in COVID-19 cases in the city of Buenos Aires
spellingShingle Relative humidity predicts day-to-day variations in COVID-19 cases in the city of Buenos Aires
Pineda Rojas, Andrea Laura
METEOROLOGY
RELATIVE HUMIDITY
AIRBORN TRANSMISSION
COVID-19
title_short Relative humidity predicts day-to-day variations in COVID-19 cases in the city of Buenos Aires
title_full Relative humidity predicts day-to-day variations in COVID-19 cases in the city of Buenos Aires
title_fullStr Relative humidity predicts day-to-day variations in COVID-19 cases in the city of Buenos Aires
title_full_unstemmed Relative humidity predicts day-to-day variations in COVID-19 cases in the city of Buenos Aires
title_sort Relative humidity predicts day-to-day variations in COVID-19 cases in the city of Buenos Aires
dc.creator.none.fl_str_mv Pineda Rojas, Andrea Laura
Cordo, Sandra Myriam
Saurral, Ramiro Ignacio
Jimenez, Jose L.
Marr, Linsey C.
Kropff, Emilio
author Pineda Rojas, Andrea Laura
author_facet Pineda Rojas, Andrea Laura
Cordo, Sandra Myriam
Saurral, Ramiro Ignacio
Jimenez, Jose L.
Marr, Linsey C.
Kropff, Emilio
author_role author
author2 Cordo, Sandra Myriam
Saurral, Ramiro Ignacio
Jimenez, Jose L.
Marr, Linsey C.
Kropff, Emilio
author2_role author
author
author
author
author
dc.subject.none.fl_str_mv METEOROLOGY
RELATIVE HUMIDITY
AIRBORN TRANSMISSION
COVID-19
topic METEOROLOGY
RELATIVE HUMIDITY
AIRBORN TRANSMISSION
COVID-19
purl_subject.fl_str_mv https://purl.org/becyt/ford/3.5
https://purl.org/becyt/ford/3
dc.description.none.fl_txt_mv Possible links between the transmission of COVID-19 and meteorology have been investigated by comparing positive cases across geographical regions or seasons. Little is known, however, about the degree to which environmental conditions modulate the daily dynamics of COVID-19 spread at a given location. One reason for this is that individual waves of the disease typically rise and decay too sharply, making it hard to isolate the contribution of meteorological cycles. To overcome this shortage, we here present a case study of the first wave of the outbreak in the city of Buenos Aires, which had a slow evolution of the caseload extending along most of 2020. We found that humidity plays a prominent role in modulating the variation of COVID-19 positive cases through a negative-slope linear relationship, with an optimal lag of 9 days between the meteorological observation and the positive case report. This relationship is specific to winter months, when relative humidity predicts up to half of the variance in positive case count. Our results provide a tool to anticipate possible local surges in COVID-19 cases after events of low humidity. More generally, they add to accumulating evidence pointing to dry air as a facilitator of COVID-19 transmission.
Fil: Pineda Rojas, Andrea Laura. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Centro de Investigaciones del Mar y la Atmósfera. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Centro de Investigaciones del Mar y la Atmósfera; Argentina
Fil: Cordo, Sandra Myriam. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales; Argentina
Fil: Saurral, Ramiro Ignacio. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Centro de Investigaciones del Mar y la Atmósfera. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Centro de Investigaciones del Mar y la Atmósfera; Argentina. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Ciencias de la Atmósfera y los Océanos; Argentina
Fil: Jimenez, Jose L.. State University of Colorado at Boulder; Estados Unidos
Fil: Marr, Linsey C.. Virginia Tech University; Estados Unidos
Fil: Kropff, Emilio. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Bioquímicas de Buenos Aires. Fundación Instituto Leloir. Instituto de Investigaciones Bioquímicas de Buenos Aires; Argentina
description Possible links between the transmission of COVID-19 and meteorology have been investigated by comparing positive cases across geographical regions or seasons. Little is known, however, about the degree to which environmental conditions modulate the daily dynamics of COVID-19 spread at a given location. One reason for this is that individual waves of the disease typically rise and decay too sharply, making it hard to isolate the contribution of meteorological cycles. To overcome this shortage, we here present a case study of the first wave of the outbreak in the city of Buenos Aires, which had a slow evolution of the caseload extending along most of 2020. We found that humidity plays a prominent role in modulating the variation of COVID-19 positive cases through a negative-slope linear relationship, with an optimal lag of 9 days between the meteorological observation and the positive case report. This relationship is specific to winter months, when relative humidity predicts up to half of the variance in positive case count. Our results provide a tool to anticipate possible local surges in COVID-19 cases after events of low humidity. More generally, they add to accumulating evidence pointing to dry air as a facilitator of COVID-19 transmission.
publishDate 2021
dc.date.none.fl_str_mv 2021-07-30
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/138480
Pineda Rojas, Andrea Laura; Cordo, Sandra Myriam; Saurral, Ramiro Ignacio; Jimenez, Jose L.; Marr, Linsey C.; et al.; Relative humidity predicts day-to-day variations in COVID-19 cases in the city of Buenos Aires; American Chemical Society; Environmental Science & Technology; 55; 16; 30-7-2021; 11176–11182
0013-936X
CONICET Digital
CONICET
url http://hdl.handle.net/11336/138480
identifier_str_mv Pineda Rojas, Andrea Laura; Cordo, Sandra Myriam; Saurral, Ramiro Ignacio; Jimenez, Jose L.; Marr, Linsey C.; et al.; Relative humidity predicts day-to-day variations in COVID-19 cases in the city of Buenos Aires; American Chemical Society; Environmental Science & Technology; 55; 16; 30-7-2021; 11176–11182
0013-936X
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://pubs.acs.org/doi/10.1021/acs.est.1c02711
info:eu-repo/semantics/altIdentifier/doi/10.1021/acs.est.1c02711
info:eu-repo/semantics/altIdentifier/url/https://www.medrxiv.org/content/10.1101/2021.01.29.21250789v2.full.pdf+html
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv American Chemical Society
publisher.none.fl_str_mv American Chemical Society
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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