Abstract

The effect of the COVID-19 pandemic has prompted a large number of studies targeted at understanding, monitoring, and containing the disease. However, it is still unclear whether the studies performed so far have filled existing knowledge gaps. We used computational intelligence (CI)/Machine Learning (ML) technologies and alliance areas to analyse this massive amount of information at scale. This chapter assesses the scholarly progress and prominent research domains in the use of CI/ML technologies in COVID-19 research, focusing on the specific literature on computational intelligence and related fields that have been employed for “diagnosis and treatment” of COVID-19 patients.The “Web of Science” database was used to retrieve all existing and highly cited papers published up to November 2020. Based on bibliometric indicators, a search query (“Computational Intelligence or Neural Networks or Fuzzy Systems or Evolutionary Computation & Diagnosis or Treatment & Coronavirus or Corona Virus or COVID-19”) was used to retrieve the data sets. The growth of research publications, elements of research activities, publication patterns, and research focus tendencies were computed using ‘Biblioshiny’ software and data visualization software ‘VOS viewer.’ Further, bibliometric/scientometrics techniques were incorporated to know the most productive countries, most preferred sources & their impact, three-field plot, and the most cited papers. This analysis provides a comprehensive overview of the “COVID-19” and CI-related research, helping researchers, policymakers, and practitioners better understand COVID-19 related CI research and its possible practical impact. Future CI / ML Studies should be committed to filling the gap between CI / ML research.