Fusing time-varying mosquito data and continuous mosquito population dynamics models

Climate change is arguably one of the most pressing issues affecting world today and requires fusion disparate data streams to accurately model its impacts. Mosquito populations respond temperature precipitation in a nonlinear way, making predicting climate impacts on mosquito-borne diseases an ongoing challenge. Data-driven approaches for modeling mosquito are needed disease risk under scenarios. Many current models transmission continuous autonomous, while discrete varies both within between seasons. This study uses optimization framework fit non-autonomous logistic with periodic net growth rate carrying capacity parameters 15 years daily time-series from Greater Toronto Area Canada. The resulting capture inter-annual intra-seasonal variability single geographic region, variance-based sensitivity analysis highlights influence each parameter has peak magnitude timing season. method can easily extend other regions be integrated into larger model. addresses challenges by serving as link differential equations epidemiology models.