Targeted prevention strategy: Exploring the interaction effect of environmental and social factors on infectious diseases.

Abstract

Human disease and health issues are globally significant and closely related to environmental and social factors. However, the interaction effects of such factors on diseases are unclear, which has resulted in a lack of targeted prevention strategies. By taking infectious diseases in China as an example, this study uses an interpretable machine learning method to analyze the impact of environmental and social factors on disease, including industrial SO₂ emissions, sanitary toilet coverage rate, and sunshine duration. The modeling results confirm the existence of a nonlinear relationship between infectious diseases incidence and each of the potential factors. That is, increased SO₂ emissions can increase infectious diseases incidence, whereas broad sanitary toilet coverage can reduce such risk. This study examines the interaction of the driving factors and reveals that variation in the sunshine duration can affect the impact of SO₂ emissions on infectious diseases incidence. This study proposes the use of multilevel risk trigger points (RTPs) to develop early warning and targeted regulation measures and classifies the points as primary, secondary, and tertiary. For example, for Henan Province, the RTPs of SO₂ emissions are 291,031, 897,579, and 1,381,342 tons, whereas those for Shandong are 362,802, 1,177,650, and 1,658,118 tons. At the tertiary RTP level, SO₂ emissions can significantly increase infectious disease incidence, which has prompted policymakers to implement pollution reduction and disease prevention measures. This study clarifies the role and interaction effects of environmental and social factors on infectious diseases to aid in precise disease prevention and environmental health management.