Mosquito Dynamics & Epidemic Risk Modeling

Context

Vector‑borne disease risk varies with climate and habitat; field counts and cases are sparse and uncertain.

Goal

Forecast vector abundance and epidemic potential with transparent, data‑informed models.

Modeling stack

• SEI‑style compartments for vectors and hosts; optional agent‑based extensions for hotspots.
• Bayesian parameter inference with priors on temperature/rainfall response; posterior predictive checks.

Env covariates ─► rate functions ─► SEI compartments ─► R₀, risk maps
                                 └► agent hotspots (optional)

Data & validation

• Merge entomological surveys, weather reanalysis, and reported cases.
• Calibrate on historical windows; test on hold‑outs and new seasons; quantify uncertainty.

Results

• Weekly risk maps with credible intervals; sensitivity shows temperature/rainfall as leading drivers.

Deliverables

• Reproducible notebooks, map visualizations, and comparison against baseline statistical models.