Using AI and Big Data to Predict and Prevent Disease Outbreaks

Abstract

The recent and quick appearance and dissemination of infectious diseases are one of the biggest challenges to the health system of the population. Traditional methods of surveillance and prediction are generally ineffective at providing timely and accurate predictions and this limits the ability of policymakers and health practitioners to respond in time. The current developments in the field of artificial intelligence (AI) and big data analytics can provide revolutionary possibilities in terms of disease outbreak prediction and prevention. With the help of various datasets, such as electronic health records, social media feeds, genomic data, environmental indicators, and mobility patterns, AI-based models can identify early warning indicators, hotspots, and predict transmission dynamics with better accuracy. The article focuses on the role of AI and large-scale data in epidemiological surveillance, design and practice and cases and examples such as COVID-19 and Ebola. It also addresses such issues as the quality of data, privacy, explain ability of AI models, and the role of moral structures and shared data. The summary of the paper is that, with the right use, AI and big data could help to improve the functionality of warning systems in the future, make resource allocation more efficient, and react to upcoming pandemics more efficiently at the international level.