Influence of urban climate in the city of Campo Grande, MS, on the number of registered cases of dengue: a case study via a Poisson regression model

Abstract

In the 1980s, the first dengue fever cases were recorded in Brazil. Since then, the cases have been occurring continuously, and currently, dengue fever is one of the country's main health problems. Since the proliferation of the mosquito that transmits the disease depends on environmental variables, such as temperature and rainfall, to complete its life cycle, it is of interest to understand the relationships between climate and dengue cases. In order to contribute to the dengue surveillance system in the city of Campo Grande, MS, Brazil, this article proposes a statistical model to identify the climate variables that may be related to the number of dengue fever cases. Once the variables have been identified, the fitted model allows us to make projections and develop simulations of different sceneries. In this way, it can assist in decision-making regarding the implementation of measures to combat and/or control the transmitting mosquito. In addition, we developed a study to verify the existence of lag periods, i.e., if the number of dengue cases recorded in a month depends on the values recorded for the environmental variables in the previous month or of the current month.

Keywords:
dengue fever; Poisson distribution; Poisson regression model; prediction