Role of AI and ML in Epidemics and Pandemics

Abstract

The healthcare industry, as well as business and society, have been revolutionized by Artificial Intelligence (AI) and Machine Learning (ML). Currently, microbiology, biochemistry, genetics, structural biology, and immunological concepts have all seen significant advances. In contrast, the fields of bioinformatics have seen considerable expansion in order to handle this massive data influx. The field of bioinformatics, which tries to use computational methods for a better understanding of biological sciences, sits at the crossroads of data science and wet lab. Several innovative databases and computational techniques have been proposed in this sector to advance immunology research, with many of them relying on artificial intelligence and machine learning to anticipate complicated immune system activities, such as epitope identification for lymphocytes. Models based on machine learning skilled on specific proteins have provided inexpensive and quick-to-implement strategies for the discovery of effective viral treatments in the recent decade. Given a target biomolecule, these models can predict inhibitor candidates using structural data. The emergence of the coronavirus COVID-19 has resulted in significant network data traffic and resource optimization demands, rendering standard network designs incapable of dealing calmly with COVID-19's consequences. Researchers are encouraged by the use of Machine Learning (ML) and Artificial Intelligence (AI) in previous epidemics, which offers a novel strategy to combating the latest COVID-19 pandemic.

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