SPATIOTEMPORAL STATISTICAL MODELING OF CLIMATE-SENSITIVE VECTOR-BORNE DISEASE TRANSMISSION

We develop a spatiotemporal statistical framework that explains how climate variability and institutional capacity jointly shape vector borne disease transmission dynamics in Ghana. Using the Global Climate Vector Disease Surveillance Dataset covering 2020 to 2025, we integrate environmental monitoring records with epidemiological surveillance indicators to estimate the Climate Vector Transmission Dynamics Model. The empirical structure links temperature fluctuation, rainfall patterns, and humidity levels with multidimensional transmission outcomes while incorporating public health system capacity as a moderating mechanism. Results show that rising temperature anomalies, precipitation variability, and atmospheric humidity significantly increase disease incidence, transmission intensity, outbreak frequency, and spatial spread. The analysis also reveals that stronger surveillance coverage, trained epidemiological workforce, and faster response systems reduce the magnitude of climate driven transmission risks. We uncover a structural mechanism where environmental signals generate cumulative ecological pressure on vectors while institutional readiness stabilizes outbreak expansion. This framework advances environmental epidemiology by integrating climate drivers and governance capacity within a unified analytical structure. The findings support climate informed disease forecasting, surveillance planning, and resilient health system governance across climate sensitive regions.