COVID-19 Analysis, Prediction, and Misconceptions: A Computational Machine Learning Model as a New Paradigm in Scientific Research

Balachandran Krishnan, Sujatha Arun Kokatnoor, Vandana Reddy, Boppuru Rudra Prathap

Source Title: Handbook of Research on the Global View of Open Access and Scholarly Communications

ISBN13: 9781799898054|ISBN10: 1799898059|ISBN13 Softcover: 9781799898061|EISBN13: 9781799898078

DOI: 10.4018/978-1-7998-9805-4.ch010

MLA

Krishnan, Balachandran, et al. "COVID-19 Analysis, Prediction, and Misconceptions: A Computational Machine Learning Model as a New Paradigm in Scientific Research." Handbook of Research on the Global View of Open Access and Scholarly Communications, edited by Daniel Gelaw Alemneh, IGI Global, 2022, pp. 178-213. https://doi.org/10.4018/978-1-7998-9805-4.ch010

APA

Krishnan, B., Kokatnoor, S. A., Reddy, V., & Prathap, B. R. (2022). COVID-19 Analysis, Prediction, and Misconceptions: A Computational Machine Learning Model as a New Paradigm in Scientific Research. In D. Alemneh (Ed.), Handbook of Research on the Global View of Open Access and Scholarly Communications (pp. 178-213). IGI Global. https://doi.org/10.4018/978-1-7998-9805-4.ch010

Chicago

Krishnan, Balachandran, et al. "COVID-19 Analysis, Prediction, and Misconceptions: A Computational Machine Learning Model as a New Paradigm in Scientific Research." In Handbook of Research on the Global View of Open Access and Scholarly Communications, edited by Daniel Gelaw Alemneh, 178-213. Hershey, PA: IGI Global, 2022. https://doi.org/10.4018/978-1-7998-9805-4.ch010

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Abstract

COVID-19 is an infectious disease of the newly discovered coronavirus (CoV). The importance and value of open access (OA) resources are critical in the context of the COVID-19 epidemic. OA aided in the development of a vaccine and informed public health actions necessary to stop the virus from spreading. Many publishers implicitly acknowledged that OA was vital to promote science in the fight against the disease. Accordingly, publishers have committed to OA publication and scholarly communication of disease-related scientific research. This chapter covers three issues based on the modeling of the CoV dataset. First, an exploratory data analysis is done to detect the hidden facts and the relevant information patterns about the affected, recovered, death cases caused by the CoV and the vaccination details. Second, a predictive model is developed using machine learning techniques to effectively predict the number of COVID-19 positive cases in India. In the last step, a hybrid computational model is developed to identify the misconceptions that are spread through social media networks.