Distinct weather conditions and human mobility impacts on the SARS-CoV-2 outbreak in Colombia: Application of an artificial neural network approach
Autor(a) principal: | |
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Data de Publicação: | 2021 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1016/j.ijheh.2021.113833 http://hdl.handle.net/11449/222295 |
Resumo: | The coronavirus disease 2019 (COVID-19) is still spreading fast in several tropical countries after more than one year of pandemic. In this scenario, the effects of weather conditions that can influence the spread of the virus are not clearly understood. This study aimed to analyse the influence of meteorological (temperature, wind speed, humidity and specific enthalpy) and human mobility variables in six cities (Barranquilla, Bogota, Cali, Cartagena, Leticia and Medellin) from different biomes in Colombia on the coronavirus dissemination from March 25, 2020, to January 15, 2021. Rank correlation tests and a neural network named self-organising map (SOM) were used to investigate similarities in the dynamics of the disease in the cities and check possible relationships among the variables. Two periods were analysed (quarantine and post-quarantine) for all cities together and individually. The data were classified in seven groups based on city, date and biome using SOM. The virus transmission was most affected by mobility variables, especially in the post-quarantine. The meteorological variables presented different behaviours on the virus transmission in different biogeographical regions. The wind speed was one of the factors connected with the highest contamination rate recorded in Leticia. The highest new daily cases were recorded in Bogota where cold/dry conditions (average temperature <14 °C and absolute humidity >9 g/m3) favoured the contagions. In contrast, Barranquilla, Cartagena and Leticia presented an opposite trend, especially with the absolute humidity >22 g/m3. The results support the implementation of better local control measures based on the particularities of tropical regions. |
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Distinct weather conditions and human mobility impacts on the SARS-CoV-2 outbreak in Colombia: Application of an artificial neural network approachArtificial neural networkBiomeCOVID-19Human mobilityHumidityTemperatureThe coronavirus disease 2019 (COVID-19) is still spreading fast in several tropical countries after more than one year of pandemic. In this scenario, the effects of weather conditions that can influence the spread of the virus are not clearly understood. This study aimed to analyse the influence of meteorological (temperature, wind speed, humidity and specific enthalpy) and human mobility variables in six cities (Barranquilla, Bogota, Cali, Cartagena, Leticia and Medellin) from different biomes in Colombia on the coronavirus dissemination from March 25, 2020, to January 15, 2021. Rank correlation tests and a neural network named self-organising map (SOM) were used to investigate similarities in the dynamics of the disease in the cities and check possible relationships among the variables. Two periods were analysed (quarantine and post-quarantine) for all cities together and individually. The data were classified in seven groups based on city, date and biome using SOM. The virus transmission was most affected by mobility variables, especially in the post-quarantine. The meteorological variables presented different behaviours on the virus transmission in different biogeographical regions. The wind speed was one of the factors connected with the highest contamination rate recorded in Leticia. The highest new daily cases were recorded in Bogota where cold/dry conditions (average temperature <14 °C and absolute humidity >9 g/m3) favoured the contagions. In contrast, Barranquilla, Cartagena and Leticia presented an opposite trend, especially with the absolute humidity >22 g/m3. The results support the implementation of better local control measures based on the particularities of tropical regions.Fundación CeiBAFURTHERMORE grants in publishingFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)São Paulo State University (UNESP) Institute of Science and Technology, Av. Três de Marco, 511, Alto da Boa VistaSchool of Science Department of Earth Sciences EAFIT UniversitySão Paulo State University (UNESP) Institute of Science and Technology, Av. Três de Marco, 511, Alto da Boa VistaFAPESP: 19/06800–5CAPES: 99999.008107/2015–07Universidade Estadual Paulista (UNESP)EAFIT UniversityGómez-Herrera, Santiago [UNESP]Sartori Jeunon Gontijo, Erik [UNESP]Enríquez-Delgado, Sandra M.Rosa, André H. [UNESP]2022-04-28T19:43:45Z2022-04-28T19:43:45Z2021-09-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://dx.doi.org/10.1016/j.ijheh.2021.113833International Journal of Hygiene and Environmental Health, v. 238.1618-131X1438-4639http://hdl.handle.net/11449/22229510.1016/j.ijheh.2021.1138332-s2.0-85113683395Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengInternational Journal of Hygiene and Environmental Healthinfo:eu-repo/semantics/openAccess2022-04-28T19:43:45Zoai:repositorio.unesp.br:11449/222295Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462022-04-28T19:43:45Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Distinct weather conditions and human mobility impacts on the SARS-CoV-2 outbreak in Colombia: Application of an artificial neural network approach |
title |
Distinct weather conditions and human mobility impacts on the SARS-CoV-2 outbreak in Colombia: Application of an artificial neural network approach |
spellingShingle |
Distinct weather conditions and human mobility impacts on the SARS-CoV-2 outbreak in Colombia: Application of an artificial neural network approach Gómez-Herrera, Santiago [UNESP] Artificial neural network Biome COVID-19 Human mobility Humidity Temperature |
title_short |
Distinct weather conditions and human mobility impacts on the SARS-CoV-2 outbreak in Colombia: Application of an artificial neural network approach |
title_full |
Distinct weather conditions and human mobility impacts on the SARS-CoV-2 outbreak in Colombia: Application of an artificial neural network approach |
title_fullStr |
Distinct weather conditions and human mobility impacts on the SARS-CoV-2 outbreak in Colombia: Application of an artificial neural network approach |
title_full_unstemmed |
Distinct weather conditions and human mobility impacts on the SARS-CoV-2 outbreak in Colombia: Application of an artificial neural network approach |
title_sort |
Distinct weather conditions and human mobility impacts on the SARS-CoV-2 outbreak in Colombia: Application of an artificial neural network approach |
author |
Gómez-Herrera, Santiago [UNESP] |
author_facet |
Gómez-Herrera, Santiago [UNESP] Sartori Jeunon Gontijo, Erik [UNESP] Enríquez-Delgado, Sandra M. Rosa, André H. [UNESP] |
author_role |
author |
author2 |
Sartori Jeunon Gontijo, Erik [UNESP] Enríquez-Delgado, Sandra M. Rosa, André H. [UNESP] |
author2_role |
author author author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (UNESP) EAFIT University |
dc.contributor.author.fl_str_mv |
Gómez-Herrera, Santiago [UNESP] Sartori Jeunon Gontijo, Erik [UNESP] Enríquez-Delgado, Sandra M. Rosa, André H. [UNESP] |
dc.subject.por.fl_str_mv |
Artificial neural network Biome COVID-19 Human mobility Humidity Temperature |
topic |
Artificial neural network Biome COVID-19 Human mobility Humidity Temperature |
description |
The coronavirus disease 2019 (COVID-19) is still spreading fast in several tropical countries after more than one year of pandemic. In this scenario, the effects of weather conditions that can influence the spread of the virus are not clearly understood. This study aimed to analyse the influence of meteorological (temperature, wind speed, humidity and specific enthalpy) and human mobility variables in six cities (Barranquilla, Bogota, Cali, Cartagena, Leticia and Medellin) from different biomes in Colombia on the coronavirus dissemination from March 25, 2020, to January 15, 2021. Rank correlation tests and a neural network named self-organising map (SOM) were used to investigate similarities in the dynamics of the disease in the cities and check possible relationships among the variables. Two periods were analysed (quarantine and post-quarantine) for all cities together and individually. The data were classified in seven groups based on city, date and biome using SOM. The virus transmission was most affected by mobility variables, especially in the post-quarantine. The meteorological variables presented different behaviours on the virus transmission in different biogeographical regions. The wind speed was one of the factors connected with the highest contamination rate recorded in Leticia. The highest new daily cases were recorded in Bogota where cold/dry conditions (average temperature <14 °C and absolute humidity >9 g/m3) favoured the contagions. In contrast, Barranquilla, Cartagena and Leticia presented an opposite trend, especially with the absolute humidity >22 g/m3. The results support the implementation of better local control measures based on the particularities of tropical regions. |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021-09-01 2022-04-28T19:43:45Z 2022-04-28T19:43:45Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.1016/j.ijheh.2021.113833 International Journal of Hygiene and Environmental Health, v. 238. 1618-131X 1438-4639 http://hdl.handle.net/11449/222295 10.1016/j.ijheh.2021.113833 2-s2.0-85113683395 |
url |
http://dx.doi.org/10.1016/j.ijheh.2021.113833 http://hdl.handle.net/11449/222295 |
identifier_str_mv |
International Journal of Hygiene and Environmental Health, v. 238. 1618-131X 1438-4639 10.1016/j.ijheh.2021.113833 2-s2.0-85113683395 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
International Journal of Hygiene and Environmental Health |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.source.none.fl_str_mv |
Scopus reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
|
_version_ |
1799964739130884096 |