SIR-SI model with a Gaussian transmission rate: understanding the dynamics of dengue outbreaks in Lima, Peru

Descripción del Articulo

Introduction: Dengue is transmitted by the Aedes aegypti mosquito as a vector, and a recent outbreak was reported in several districts of Lima, Peru. We conducted a modeling study to explain the transmission dynamics of dengue in three of these districts according to the demographics and climatology...

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Detalles Bibliográficos
Autores: Ramírez Soto, Max Carlos, Bogado Machuca, Juan Vicente, Stalder, Diego H., Champin Michelena, Denisse Cecilie
Formato: artículo
Fecha de Publicación:2023
Institución:Universidad Tecnológica del Perú
Repositorio:UTP-Institucional
Lenguaje:inglés
OAI Identifier:oai:repositorio.utp.edu.pe:20.500.12867/7014
Enlace del recurso:https://hdl.handle.net/20.500.12867/7014
https://doi.org/10.1371/journal. pone.0284263
Nivel de acceso:acceso abierto
Materia:Dengue
Epidemiological models
Mathematical model
Epidemiological surveillance
https://purl.org/pe-repo/ocde/ford#3.03.05
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dc.title.es_PE.fl_str_mv SIR-SI model with a Gaussian transmission rate: understanding the dynamics of dengue outbreaks in Lima, Peru
title SIR-SI model with a Gaussian transmission rate: understanding the dynamics of dengue outbreaks in Lima, Peru
spellingShingle SIR-SI model with a Gaussian transmission rate: understanding the dynamics of dengue outbreaks in Lima, Peru
Ramírez Soto, Max Carlos
Dengue
Epidemiological models
Mathematical model
Epidemiological surveillance
https://purl.org/pe-repo/ocde/ford#3.03.05
title_short SIR-SI model with a Gaussian transmission rate: understanding the dynamics of dengue outbreaks in Lima, Peru
title_full SIR-SI model with a Gaussian transmission rate: understanding the dynamics of dengue outbreaks in Lima, Peru
title_fullStr SIR-SI model with a Gaussian transmission rate: understanding the dynamics of dengue outbreaks in Lima, Peru
title_full_unstemmed SIR-SI model with a Gaussian transmission rate: understanding the dynamics of dengue outbreaks in Lima, Peru
title_sort SIR-SI model with a Gaussian transmission rate: understanding the dynamics of dengue outbreaks in Lima, Peru
author Ramírez Soto, Max Carlos
author_facet Ramírez Soto, Max Carlos
Bogado Machuca, Juan Vicente
Stalder, Diego H.
Champin Michelena, Denisse Cecilie
author_role author
author2 Bogado Machuca, Juan Vicente
Stalder, Diego H.
Champin Michelena, Denisse Cecilie
author2_role author
author
author
dc.contributor.author.fl_str_mv Ramírez Soto, Max Carlos
Bogado Machuca, Juan Vicente
Stalder, Diego H.
Champin Michelena, Denisse Cecilie
dc.subject.es_PE.fl_str_mv Dengue
Epidemiological models
Mathematical model
Epidemiological surveillance
topic Dengue
Epidemiological models
Mathematical model
Epidemiological surveillance
https://purl.org/pe-repo/ocde/ford#3.03.05
dc.subject.ocde.es_PE.fl_str_mv https://purl.org/pe-repo/ocde/ford#3.03.05
description Introduction: Dengue is transmitted by the Aedes aegypti mosquito as a vector, and a recent outbreak was reported in several districts of Lima, Peru. We conducted a modeling study to explain the transmission dynamics of dengue in three of these districts according to the demographics and climatology. Methodology: We used the weekly distribution of dengue cases in the Comas, Lurigancho, and Puente Piedra districts, as well as the temperature data to investigate the transmission dynamics. We used maximum likelihood minimization and the human susceptible-infected-recovered and vector susceptible-infected (SIR-SI) model with a Gaussian function for the infectious rate to consider external non-modeled variables. Results/principal findings: We found that the adjusted SIR-SI model with the Gaussian transmission rate (for modelling the exogenous variables) captured the behavior of the dengue outbreak in the selected districts. The model explained that the transmission behavior had a strong dependence on the weather, cultural, and demographic variables while other variables determined the start of the outbreak. Conclusion/significance: The experimental results showed good agreement with the data and model results when a Bayesian-Gaussian transmission rate was employed. The effect of weather was also observed, and a strong qualitative relationship was obtained between the transmission rate and computed effective reproduction number Rt.
publishDate 2023
dc.date.accessioned.none.fl_str_mv 2023-05-26T16:05:38Z
dc.date.available.none.fl_str_mv 2023-05-26T16:05:38Z
dc.date.issued.fl_str_mv 2023
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dc.identifier.uri.none.fl_str_mv https://hdl.handle.net/20.500.12867/7014
dc.identifier.journal.es_PE.fl_str_mv Plos One
dc.identifier.doi.none.fl_str_mv https://doi.org/10.1371/journal. pone.0284263
identifier_str_mv 1932-6203
Plos One
url https://hdl.handle.net/20.500.12867/7014
https://doi.org/10.1371/journal. pone.0284263
dc.language.iso.es_PE.fl_str_mv eng
language eng
dc.relation.ispartofseries.none.fl_str_mv Plos One;vol. 18, n° 4
dc.rights.es_PE.fl_str_mv info:eu-repo/semantics/openAccess
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dc.publisher.es_PE.fl_str_mv Public Library of Science
dc.publisher.country.es_PE.fl_str_mv US
dc.source.es_PE.fl_str_mv Repositorio Institucional - UTP
Universidad Tecnológica del Perú
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spelling Ramírez Soto, Max CarlosBogado Machuca, Juan VicenteStalder, Diego H.Champin Michelena, Denisse Cecilie2023-05-26T16:05:38Z2023-05-26T16:05:38Z20231932-6203https://hdl.handle.net/20.500.12867/7014Plos Onehttps://doi.org/10.1371/journal. pone.0284263Introduction: Dengue is transmitted by the Aedes aegypti mosquito as a vector, and a recent outbreak was reported in several districts of Lima, Peru. We conducted a modeling study to explain the transmission dynamics of dengue in three of these districts according to the demographics and climatology. Methodology: We used the weekly distribution of dengue cases in the Comas, Lurigancho, and Puente Piedra districts, as well as the temperature data to investigate the transmission dynamics. We used maximum likelihood minimization and the human susceptible-infected-recovered and vector susceptible-infected (SIR-SI) model with a Gaussian function for the infectious rate to consider external non-modeled variables. Results/principal findings: We found that the adjusted SIR-SI model with the Gaussian transmission rate (for modelling the exogenous variables) captured the behavior of the dengue outbreak in the selected districts. The model explained that the transmission behavior had a strong dependence on the weather, cultural, and demographic variables while other variables determined the start of the outbreak. Conclusion/significance: The experimental results showed good agreement with the data and model results when a Bayesian-Gaussian transmission rate was employed. 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