AI for science: Predicting infectious diseases

Abstract

The global health landscape has been persistently challenged by the emergence and re-emergence of infectious diseases. Traditional epidemiological models, rooted in the early 20th century, have provided foundational insights into disease dynamics. However, the intricate web of modern global interactions and the exponential growth of available data demand more advanced predictive tools. This is where AI for Science (AI4S) comes into play, offering a transformative approach by integrating artificial intelligence (AI) into infectious disease prediction. This paper elucidates the pivotal role of AI4S in enhancing and, in some instances, superseding traditional epidemiological methodologies. By harnessing AI's capabilities, AI4S facilitates real-time monitoring, sophisticated data integration, and predictive modeling with enhanced precision. The comparative analysis highlights the stark contrast between conventional models and the innovative strategies enabled by AI4S. In essence, AI4S represents a paradigm shift in infectious disease research. It addresses the limitations of traditional models and paves the way for a more proactive and informed response to future outbreaks. As we navigate the complexities of global health challenges, AI4S stands as a beacon, signifying the next phase of evolution in disease prediction, characterized by increased accuracy, adaptability, and efficiency.

Keywords

• AI for science (AI4S)
• Data integration
• Global health challenges
• Infectious disease prediction
• Predictive modeling
• Real-time monitoring