Distinct weather conditions and human mobility impacts on the SARS-CoV-2 outbreak in Colombia: Application of an artificial neural network approach

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Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorEAFIT University-
Autor(es): dc.creatorGómez-Herrera, Santiago [UNESP]-
Autor(es): dc.creatorSartori Jeunon Gontijo, Erik [UNESP]-
Autor(es): dc.creatorEnríquez-Delgado, Sandra M.-
Autor(es): dc.creatorRosa, André H. [UNESP]-
Data de aceite: dc.date.accessioned2022-08-04T22:12:03Z-
Data de disponibilização: dc.date.available2022-08-04T22:12:03Z-
Data de envio: dc.date.issued2022-04-28-
Data de envio: dc.date.issued2022-04-28-
Data de envio: dc.date.issued2021-09-01-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1016/j.ijheh.2021.113833-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/222295-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/222295-
Descrição: dc.descriptionThe 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.-
Descrição: dc.descriptionFundación CeiBA-
Descrição: dc.descriptionFURTHERMORE grants in publishing-
Descrição: dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)-
Descrição: dc.descriptionCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)-
Descrição: dc.descriptionSão Paulo State University (UNESP) Institute of Science and Technology, Av. Três de Marco, 511, Alto da Boa Vista-
Descrição: dc.descriptionSchool of Science Department of Earth Sciences EAFIT University-
Descrição: dc.descriptionSão Paulo State University (UNESP) Institute of Science and Technology, Av. Três de Marco, 511, Alto da Boa Vista-
Descrição: dc.descriptionFAPESP: 19/06800–5-
Descrição: dc.descriptionCAPES: 99999.008107/2015–07-
Idioma: dc.languageen-
Relação: dc.relationInternational Journal of Hygiene and Environmental Health-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectArtificial neural network-
Palavras-chave: dc.subjectBiome-
Palavras-chave: dc.subjectCOVID-19-
Palavras-chave: dc.subjectHuman mobility-
Palavras-chave: dc.subjectHumidity-
Palavras-chave: dc.subjectTemperature-
Título: dc.titleDistinct weather conditions and human mobility impacts on the SARS-CoV-2 outbreak in Colombia: Application of an artificial neural network approach-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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