Models of Mosquito Abundance

This talk will focus on recent work aimed at modeling and forecasting mosquito abundance in the natural environment. More specifically, we will start with a discrete-time microscopic-level stochastic model that follows the life stages of Aedes aegypti mosquitoes, from egg to adulthood. This model produces time-series of gravid female abundance as functions of local weather data consisting of temperature, precipitation, and relative humidity. We will then address the question of training an artificial neural network to obtain similar predictions in a fraction of the computational time and discuss how the resulting model may be used as a forecasting tool. Finally, we will consider how dispersal affects the number of mosquitoes caught in gravid traps and how this information may be used to inform the modeling of mosquito abundance in terms of partial differential equations.                                                                                                                       
The following colleagues have contributed to this work: Roberto Barrera (CDC, Puerto Rico), Heidi Brown (Public Health, U. of Arizona), Sean Current (Computer Science, Ohio State), Adrienne Kinney (Applied Mathematics, U. of Arizona), and Lidia Mrad (Mathematics, Mount Holyoke).

Bio
https://sites.arizona.edu/lega/