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

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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
Descripción
Sumario: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.
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