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

• Published in: PloS one Vol. 18; no. 4; p. e0284263
• Main Authors: Ramírez-Soto, Max Carlos, Machuca, Juan Vicente Bogado, Stalder, Diego H, Champin, Denisse, Mártinez-Fernández, Maria G, Schaerer, Christian E
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 13.04.2023; Public Library of Science (PLoS)
• Subjects: Aedes; Aedes aegypti; Analysis; Animals; Bayes Theorem; Bayesian analysis; Biology and Life Sciences; Bivalvia; Climatology; Correlation analysis; Demographic variables; Demography; Dengue; Dengue - epidemiology; Dengue fever; Dengue Virus; Diagnosis; Disease Outbreaks; Disease prevention; Disease transmission; Distribution; Earth Sciences; Epidemics; Epidemiology; Evaluation; Gaussian processes; Humans; Medicine and Health Sciences; Modelling; Mosquito Vectors; Mosquitoes; Outbreaks; Parameter estimation; People and places; Peru - epidemiology; Piedra; Population; Rain; Temperature data; Temperature effects; Time series; Vector-borne diseases; Weather; Weather effects; Peru
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0284263

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. 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. 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. 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.