A review of artificial intelligence for predicting climate driven infectious disease outbreaks to enhance global health resilience

Abstract The impact of climate change on infectious disease outbreaks demands sophisticated techniques of prediction to protect global health. This review focuses on the impact of artificial intelligence on the prediction of disease outbreaks influenced by climatic factors, showcasing its potential on diverse data sets. While traditional forecasting models have restricted capabilities due to fixed parameters and linear relationships, machine learning models such as support vector machine and random forest, deep learning models such convolutional neural network, long-short term memory and transformers as well as the hybrid models are found to be much more efficient with their supremacy proven over traditional models through applications in vector-borne, water-borne, and zoonotic diseases through capturing real-time analytics and non-linear climate-disease interaction. Despite these breakthroughs, the challenges of sparsity of data in low-resource areas, lack of model transparency, and ethically deemed biased algorithms pose great challenges. The review recommends the necessity of data governance, explanatory frameworks, and cross-disciplinary collaboration to counter these constraints, and proposes the utilization of federated learning, with quantum and edge computing, to formulate global health resilience pathways. Therefore, researchers are encouraged to adopt artificial intelligence techniques in predictive modeling, owing to their potential to transform proactive public health policy; however, its success is contingent on socially equitable implementation, trust from the relevant stakeholders, and climate policy integration.