The Impact of Climate Change on the Global Spread of Vector-Borne Diseases and Public Health Preparedness

Abstract

This study examines the impact of climate change on the global spread of vector-borne diseases (VBDs) and the corresponding public health preparedness needed to address these emerging health threats. The increasing temperatures, changing precipitation patterns, and shifting ecosystems due to climate change are expanding the habitats of disease vectors such as mosquitoes and ticks, thereby altering the distribution and transmission dynamics of diseases like malaria, dengue, Zika virus, and Lyme disease. The findings of this research indicate that AI-driven predictive models, including machine learning algorithms such as XGBoost and RNN, significantly enhance demand forecasting accuracy, which can aid in predicting disease outbreaks and optimizing resource allocation. The inventory optimization models developed in this study highlight the potential for AI to reduce healthcare costs by improving the efficiency of resource distribution in response to outbreaks. Furthermore, the study identifies that AI can reduce environmental impact and improve sustainability within supply chains, contributing to both economic and ecological resilience. The integration of climate data and health surveillance systems has proven to be a valuable strategy for enhancing public health response capabilities. However, significant challenges remain in terms of data quality, infrastructure, and the integration of AI solutions into existing healthcare systems. These barriers, along with ethical concerns related to the environmental costs of AI, need to be addressed for the successful application of AI in public health preparedness for VBDs. This study underscores the importance of a multidisciplinary approach to mitigate the impact of climate change on vector-borne diseases, with a focus on improving public health systems' adaptive capacity.