COVID-Scraper: An Open-Source Toolset for Automatically Scraping and Processing Global Multi-Scale Spatiotemporal COVID-19 Records

Lan, Hai (ORCID:0000000241194388); Sha, Dexuan (ORCID:0000000161616050); Malarvizhi, Anusha Srirenganathan; Liu, Yi (ORCID:0000000291742004); Li, Yun; Meister, Nadine; Liu, Qian (ORCID:0000000338764877); Wang, Zifu; Yang, Jingchao; Yang, Chaowei Phil (ORCID:0000000177684066)

doi:10.1109/ACCESS.2021.3085682

While the COVID-19 pandemic morphs into less malignant forms, the virus has spawned a series of poorly understood, post-infection symptoms with staggering ramifications, i. e., long COVID (LC). This bibliometric study profiles the rapidly growing LC research domain [5,243 articles from PubMed and Web of Science (WoS)] to make its knowledge content more accessible. The article addresses What? Where? Who? and When? questions. A 13-topic Concept Grid presents bottom-up topic clusters. We break out those topics with other data fields, including disciplinary concentrations, topical details, and information on research “players” (countries, institutions, and authors) engaging in those topics. We provide access to results via a Dashboard website. We find a strongly growing, multidisciplinary LC research domain. That domain appears tightly connected based on shared research knowledge. However, we also observe notable concentrations of research activity in different disciplines. Data trends over 3 years of LC research suggest heightened attention to psychological and neurodegenerative symptoms, fatigue, and pulmonary involvement.

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