Artificial Intelligence Against COVID-19: A Meta-analysis of Current Research

Raza, Khalid

The epidemic of coronavirus disease 2019 (COVID-19) has posed an unprecedented challenge before humankind, with more than 3.2 million confirmed cases and death toll to more than 225 thousand till the end of April, 2020 globally. Currently, one-half of the world is under lockdown and complying with social distancing to arrest its spread. Does the COVID-19 crisis illustrate the necessity for solid artificial intelligence (AI) and machine learning (ML) strategy? AI and ML research groups across the world are extensively working to tackle various aspects of the COVID-19 crisis including epidemiological (e.g. prediction, controlling and forecasting viral dynamics), molecular studies and drug development (e.g. molecular modeling and drug targets identification), medical (e.g. AI-enable diagnostic and treatment), and socio-economical applications (e.g. economical impact forecasting and mitigation). In the last few months, several research papers have been published with AI applications against COVID-19. In this chapter, we performed a meta-analysis of the current state-of-the-art of AI against COVID-19 by using various publication repositories (PubMed, PubMed Central, Scopus, Google Scholar) and preprint servers (bioRxiv, medRxiv, arXiv). This meta-analysis helps the researcher to understand the broad spectrum of AI to combat COVID-19.