Use of artificial intelligence for predicting infectious disease

ABSTRACT

Infectious diseases threaten the lives of the entire global population. Some diseases such as SARS and COVID-19 trigger pandemics, as spread from country to country, with severe adverse effects on the medical system, such as shortages in medical professionals and equipment, financial burden, and death. Therefore, it is crucial to predict and respond to the spread of infectious diseases. In this chapter, we reviewed the research related to the prediction models of the spread of infectious diseases, based on various methodologies. Studies that adopt conventional mathematical models, such SIR, SEIR, and agent-based models are considered. In addition, an analysis centered on artificial intelligence, big data, and machine learning methodologies was carried out. Decision-makers should arrive at decisions by considering limitations of modeling infectious diseases. In particular, the internal structure of deep learning is a black box;hence, it difficult to interpret the results. Modelers should transparently provide data collection, coding, and modeling processes, as well as provide information on model uncertainty to help decision-makers create policy decisions. Furthermore, to make scientific and rational decisions based on evidence, considering the geographic information system interpersonal interactions, national, and social environments, decision-makers should refer to epidemiologic data and modeling results. © 2022 Elsevier Inc. All rights reserved.