Mathematical and statistical models for the control of mosquito-borne diseases

Objective. To summarize the results of research conducted in Costa Rica in which mathematical and statistical methods were implemented to study the transmission dynamics of mosquito-borne diseases. Methods. Three articles with mathematical and statistical analysis on vector-borne diseases in Costa Rica were selected and reviewed. These papers show the value and relevance of using different quantitative methods to understand disease dynamics and support decision-making. Results. The results of these investigations: 1) show the impact on dengue case reports when a second pathogen emerges, such as chikungunya; 2) recover key parameters in Zika dynamics using Bayesian inference; and 3) show the use of machine learning algorithms and climatic variables to forecast the dengue relative risk in five different locations. Conclusions. Mathematical and statistical modeling enables the description of mosquito-borne disease transmission dynamics, providing quantitative information to support prevention/control methods and resource allocation planning

Bayesian inference · Bayesian probability · Biology · Chikungunya · Dengue fever · Geography · Inference · Machine learning · Operations research · Relevance (law · Resource (disambiguation · Statistical analysis · Statistical inference · Statistical model · Statistics · Telecommunications · Transmission (telecommunications · Computer Science · COVID-19 epidemiological studies · Mathematics · Mosquito-borne diseases and control · Zoonotic diseases and public health · Artificial Intelligence · Virology