Abstract
AbstractThe climate crisis will have an increasingly profound effect on the global distribution and burden of infectious diseases. Climate-sensitive diseases can serve as critical case studies for assessing public health priorities in the face of epidemics. Preliminary results denote that machine learning-based predictive modeling measures can be successfully applied to understanding environmental disease transmission dynamics. Ultimately, machine learning models can be trained to detect climate-sensitive diseases early, diseases which might represent serious threats to human health, food safety, and economies. We explore how machine learning can serve as a tool for better understanding climate-sensitive diseases, taking dengue dynamics along the Godavari River of coastal India as our case study. We hypothesize that a climate-driven predictive model with controlled calibration can help us understand several of the most critical relationships and climate characteristics of climate-sensitive disease dynamics.
Publisher
Cold Spring Harbor Laboratory