Title: Empirical assessment of COVID-19 infections and information diffusion: a data science approach

Authors: Isaac Kofi Nti; Adebayo Felix Adekoya; Owusu Nyarko-Boateng; Ponnadurai Ramasami

Addresses: Department of Computer Science and Informatics, University of Energy and Natural Resources, P.O. Box 214, Sunyani, Ghana ' Department of Computer Science and Informatics, University of Energy and Natural Resources, P.O. Box 214, Sunyani, Ghana ' Department of Computer Science and Informatics, University of Energy and Natural Resources, P.O. Box 214, Sunyani, Ghana ' Computational Chemistry Group, Department of Chemistry, Faculty of Science, University of Mauritius, Réduit 80837, Mauritius; Department of Chemistry, College of Science Engineering and Technology, University of South Africa, South Africa

Abstract: The spread of the novel coronavirus disease, SARS-CoV-19 (COVID-19), has affected human activities everywhere, resulting in fear and panic among all age groups. Hence, this study implements a novel data science process to empirically model the daily reported cases and Google search queries in 14 countries. We observed a strong positive association (0.79-0.96) among reported cases of COVID-19 in the 14 countries. Furthermore, there is an inverse correlation of -0.18 to -0.62 between information diffusion on the virus and reported cases (new cases and deaths). Our outcome shows that contagious diseases are highly predictable using historical records from other countries and information spread on the disease.

Keywords: data analytics; machine learning; data science; coronavirus; SARS-CoV-2; COVID-19; Google search engine; infections.

DOI: 10.1504/IJMEI.2024.136961

International Journal of Medical Engineering and Informatics, 2024 Vol.16 No.2, pp.97 - 114

Received: 01 Jun 2021
Accepted: 07 Nov 2021
Published online: 01 Mar 2024 *

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