A model to predict the population size of the dengue fever vector based on rainfall data

Abstract: According to the World Health Organization, dengue fever is the most important mosquito-borne disease of humans, and it is currently estimated that there may be 50 - 100 million yearly dengue infections worldwide. For the purpose to provide new techniques to public health policies in course, we introduce a predictive non-linear population dynamics model to describe the population size of four stages of the development of \emph{Aedes aegypti}, having the coefficients set to be dependent on the rainfall index data. In spite of the population dynamics of the \emph{Ae. aegypti} be mainly ruled by the rainfall regime, most models are dedicated exclusively to effects of temperature and only few models are dedicated to influence of rainfall. Vector control actions are also implemented in many periods of the year in order to compare relative efficiency of public health policies. The analysis of equilibrium and stability was performed. Field rainfall time series data from the City of Lavras (Minas Gerais, Brazil) was used for the model evaluation. The model was validated in a comparison with experimental mosquito abundance data acquired by field health agents. We evaluated and validated an entomological conjecture that claims that control actions should be performed during the dry season, instead of the common procedure adopted by vector control programs, in which those are mainly applied in the rainy season.