Aedes aegypti mosquitoes are the vector of diseases such as dengue, zika, chikungunya, and yellow fever among others. All the stages of development, eggs, larvae, pupa, and the adult of the species have its population modulated by meteorological variables, such as precipitation and temperature through affecting the productivity of breeding sites, metabolic processes, and others. Since adult females are responsible for transmitting the virus, the population of females becomes a direct indicator of the risk of infection. For this reason, some ongoing vector surveillance programs are based on adult female capture. In turn, all the stages of development have its population modulated by meteorological variables, such as precipitation and temperature, through productivity of breeding sites, metabolic processes and others. In this work, field data of capture of females was used to evaluate if a population dynamics model of Aedes aegypti under the effect of weather would be able to forecast field population. The nonlinear dynamic system model comprises: (1) four equations for the populations of the stages of development of the mosquito, designed for the ongoing surveillance program; (2) parametric dependencies of the rates of development on mean temperature and weekly accumulated precipitation. The dependencies on temperature and precipitation are modelled with aim of simplicity with the fewer number of parameters as possible. Temperature dependence is modelled based on values of the related literature under the assumption of existence of a optimum temperature for the rates, getting worse for extreme temperatures. The dependence on precipitation which is barely treated in experiments is modelled under the assumption of a monotonic dependence described by a power law with values estimated in orders of magnitude from data in the literature. By comparison with field data of an entomological indicator based on the number of Ae. aegypti females captured by a public health program in the city of Caratinga (Minas Gerais, Brazil), the model showed a significant correlation (R = 0.75). The result shows that the approach, if refined, can provide forecasting for of the population size.
The development of predictive tools for pre-emptive dengue vector control: a study of Aedes aegypti abundance and meteorological variables in North Queensland, Australia
