Abstract
Researchers around the world have recently used data science and deep and machine learning to assess and combat the coronavirus disease 2019 (COVID-19). The results from this study have presented research on COVID-19 that applied data science, big data, machine learning, deep learning, artificial intelligence, and mathematic and statistical modeling between January 2020 and April 2020 by different researchers across disciplines from different countries of the world. It was noted that the prominent studies used various terms and keywords in COVID-19-related studies include 2019-nCoV, China, COVID-19, epidemic, remdesivir, SARS-CoV-2, coronavirus, epidemiology, infection 2019-nCoV, SARS coronavirus, angiotensin-converting enzyme 2, animal reservoir, cross-species transmission, and human-to-human transmission in COVID-19 studies between January and April 2020. The result reveals the relevance and percentage, as well as the distribution, of data science and modeling techniques used in COVID-19 research before 2020 and during the year 2020 (January to April 2020). More so, author keywords, both keywords, total articles, total citations, and h-index were identified. “Model” has the highest frequency with 19 papers, 11 total citations, and an h-index of 2; on the other hand, it appeared on number one on both keywords with 37 papers, 47 citations, and an h-index of 4. Bibliometrics generally applies Price's law to estimate authors' influence and output in a particular field of study, which can also determine the highest and lowest occurrence of important key terms.