Investigation of the COVID-19 Research —A Big Data Approach

Abstract

The outbreak of novel coronavirus disease in 2019 (COVID-19) has drawn researchers’ attention to find the causes and facts of it in hope of preventing its spread and saving patients’ life. However, there are still lack of researches investigating this problem from a big data perspective. This paper tries to tackle this severe threat from a big data perspective to reveal the unknown facts and research trends concealed in the academic publications and to compare our findings with the traditional statistical methods. We downloaded 16, 560 publications from Web of Science and classified the most frequently mentioned keywords in the abstracts into seven different aspects. Then the cluster and strategic diagram methods were used to identify the core and mature research topics and trend. We found that although the vulnerable had been paid appropriate attention by researchers, undeveloped countries had not in this health catastrophe; lung was the most fragile organ to be infected and CT and RT-PCR were the most favorite diagnostic methods; and clinical and modelling methods were the most preferably used by researchers as medical and non-medical research tools etc. Strategic diagram revealed that instead of fever, respiratory distress and pulmonary symptoms/disorders were the most mature diagnosable symptoms. Our findings showed that this simple method proves itself as being applicable in bringing to light some unknown facts hidden behind the haphazard research data and revealing the future research trends.