Framework for Integrating Climate Data and Health Outcomes to Improve Mortality Risk Prediction Systems - Onyekachi Stephanie Oparah,; Funmi Eko Ezeh,; Glory Iyanuoluwa Olatunji,; Opeoluwa Oluwanifemi Ajayi, (International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2024)
Climate variability exerts profound effects on population health, with temperature extremes, air pollution, and shifting rainfall patterns significantly influencing morbidity and mortality risks. Traditional health surveillance systems often underrepresent the role of environmental determinants, limiting their predictive capacity for anticipating adverse health outcomes. Developing a framework that integrates climate data with health outcomes offers a promising pathway for advancing mortality risk prediction systems. Such a framework recognizes that environmental exposures—such as heatwaves, vector-breeding conditions, and deteriorating air quality—interact with demographic and health system vulnerabilities to amplify risks of premature death. The proposed framework emphasizes the incorporation of diverse climate datasets, including satellite observations, meteorological records, and long-term climate projections, alongside clinical and epidemiological data. By aligning these data streams, the framework enhances the capacity to identify at-risk populations and quantify exposure–response relationships with greater precision. Advanced analytical techniques, including machine learning, spatiotemporal modeling, and system dynamics, further enable the detection of nonlinear interactions and lagged effects that are often overlooked in conventional models. Importantly, the integration of climate information supports early warning systems by providing timely risk assessments that can inform public health planning and rapid response measures. The application of this integrative framework is particularly valuable for vulnerable regions where climate-sensitive diseases, resource constraints, and limited health infrastructure converge. Beyond forecasting, it supports targeted interventions such as heat action plans, pollution advisories, and resource allocation strategies designed to reduce preventable deaths. Ultimately, embedding climate data within health risk prediction systems shifts the paradigm from reactive to proactive public health, fostering resilience against both acute and chronic climate-related health challenges. This framework represents a critical step toward building comprehensive, data-driven strategies to mitigate mortality risks in the era of accelerating climate change.
Keywords: Climate data; health outcomes; mortality risk prediction; predictive modeling; epidemiology; climate-sensitive diseases; environmental exposure; data integration; public health; spatiotemporal analysis