Mechanistic Modeling of Mosquito Habitats for Climate-Informed Dengue Forecasting.

The mosquito-borne disease dengue is sensitive to climate, in part because of the influence climate has on breeding habitats of dengue's mosquito vectors. Dengue risk assessment models currently leverage climate-dengue associations, yet what remain understudied are the pathways that yield different statistical relationships in different locations. We hypothesize that elucidating the mechanisms by which spatiotemporal variability in climate influences dengue incidence will improve dengue dynamics predictions across climatically distinct locations and beyond dengue's well-known seasonal cycles. We test this hypothesis by investigating a key pathway in the climate-dengue process chain: climate impacts on breeding habitats. We have implemented a mechanistic modeling pipeline that simulates climatic influence on habitat water dynamics and thereby on relative population size of the vector. We use this modeling pipeline, driven by meteorological data, to simulate monthly populations for three climatically distinct cities in Sri Lanka. We find that simulated vector abundance is plausibly associated with climate conditions and that climate drivers of vector abundance vary among locations. Moreover, tercile-tercile comparisons of dengue incidence against model variables indicate that risk assessments based on predicted vector abundance perform similarly to those based on meteorology alone-the signal of weather variability and its relationship to dengue propagates through the modeling pipeline. These results justify future testing of this modeling pipeline within a dengue risk assessment framework, where its process-based structure may be leveraged to guide proactive dengue control efforts in high-risk years and to simulate impacts of future climate conditions on dengue dynamics.