Artificial intelligence for forecasting climate-driven vector-borne disease outbreaks
1 Department of Supply Chain Management, Marketing, and Management, Raj Soin College of Business, Wright State University, United States.
2 Department of Computer Science and Engineering, Ladoke Akintola University of Technology, Ogbomoso, Nigeria.
3 Department of Environmental Health and Management, University of New Haven, West Haven Connecticut.
4 Department of Nursing Science, Madonna University, Nigeria.
5 Department of Pure and Applied Chemistry, Ladoke Akintola University of Technology, Ogbomoso, Oyo State, Nigeria.
6 Department of Mathematics, Tai Solarin University of Education, Ijagun, Ogun State, Nigeria.
7 Department of Biomedical Engineering, Bells University of Technology, Nigeria.
Review
International Journal of Biological and Pharmaceutical Sciences Archive, 2025, 09(02), 130-143.
Article DOI: 10.53771/ijbpsa.2025.9.2.0048
Publication history:
Received on 16 April 2025; revised on 29 May 2025; accepted on 01 June 2025
Abstract:
Climate change is intensifying the global burden of vector-borne diseases (VBDs) such as malaria, dengue, Zika, and West Nile virus by altering vector habitats, expanding transmission zones, and increasing outbreak frequency. This review examines how Artificial Intelligence (AI) is transforming the prediction and management of climate-driven VBD outbreaks. It begins by outlining the ecological impact of rising temperatures, shifting precipitation patterns, and extreme weather events on vector populations. The paper then explores AI’s role in public health surveillance, focusing on machine learning and deep learning models—including Random Forests, LSTMs, and CNNs—that integrate climate, environmental, and epidemiological data to improve forecasting accuracy. Real-world applications demonstrate AI’s capacity to outperform traditional models by identifying disease hotspots and enabling timely, targeted interventions. The review also highlights how AI-assisted simulations can project future VBD risks under various climate scenarios, supporting proactive planning and resource allocation. Further, it emphasizes the need for interdisciplinary collaboration and policy frameworks to ensure the ethical, equitable, and transparent use of AI in health systems. Challenges such as data quality, model interpretability, and regional disparities are discussed, along with emerging trends like federated learning and real-time AI dashboards. Ultimately, this paper underscores the potential of AI to enhance global health resilience by enabling adaptive, climate-smart approaches to infectious disease surveillance and control.
Keywords:
Artificial Intelligence; Vector-Borne Diseases; Climate Change; Disease Prediction; Machine Learning; Public Health Surveillance; Epidemiological Modeling
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Copyright © 2025 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0