Fusing time-varying mosquito data and continuous mosquito population dynamics models - Mancuso, Marina; Martinez, Kaitlyn M.; Manore, Carrie A.; Milner, Fabio A.; Barnard, Martha; Godinez, Humberto (Frontiers in Applied Mathematics and Statistics, 2023)
Climate change is arguably one of the most pressing issues affecting the world today, and requires the fusion of disparate data streams to accurately model its impacts. Mosquito populations respond to temperature and precipitation in a nonlinear way, making predicting climate impacts on mosquito-borne diseases an ongoing challenge. Data-driven approaches for accurately modeling mosquito populations are needed for predicting mosquito-borne disease risk under different climate scenarios. In particular, many current models for disease transmission are continuous and autonomous, while mosquito data (whether from traps or output of highly detailed models) is discrete and varies both within and between seasons. This study fits a non-autonomous logistic model with periodic net growth rate and carrying capacity parameters for 15 years of daily mosquito time-series data from the Greater Toronto Area of Canada. The resulting parameters obtained from an optimization framework accurately capture the inter-annual and intra-seasonal variability of mosquito populations within a single geographic region and can be easily integrated into a larger disease transmission model. A variance-based sensitivity analysis demonstrates the influence each parameter has on the peak magnitude and timing of the mosquito season. This method addresses the ongoing challenges of data and model fusion by serving as a link between a discrete population time series and continuous differential equations for mosquito-borne epidemiology models