AI-Enabled Early Warning Systems for Climate-Linked Biological Risks

Abstract

Climate change's accelerated biological risk to human health, agriculture, wildlife, and ecosystems has altered pathogen ecology, vector dynamics, and species interactions. This chapter examined AI-enabled predictive analytics systems for early warning of climate-related biological risk, using evidence from climate science, epidemiology, ecology, and computational models. The findings show that Machine Learning and Deep Learning frameworks with Earth Observing Technology and real-time sensing have improved lead time, spatial resolution, and risk characterization for non-linear climate conditions. The platforms improved predictions of vector-borne disease risk, zoonotic spillover risks, agricultural pest management, and ecosystem hazards. However, challenges remain in data availability, model transferability, uncertainty communication, and system integration. This chapter discusses gaps and directions for developing scalable, explainable, and policy-relevant AI-driven predictive analytics systems amid rapid climate changes.