Title: Class prediction of the prevalent transmission mode of COVID-19 within a geographic area

Authors: Donald Douglas Atsa'am; Ruth Wario

Addresses: Department of Computer Science and Informatics, Faculty of Natural and Agricultural Sciences, University of the Free State, Qwaqwa Campus, South Africa ' Department of Computer Science and Informatics, Faculty of Natural and Agricultural Sciences, University of the Free State, Qwaqwa Campus, South Africa

Abstract: This research developed a multinomial classification model that predicts the prevalent mode of transmission of the coronavirus from person to person within a geographic area, using data from the World Health Organization (WHO). The WHO defines four transmission modes of the coronavirus disease 2019 (COVID-19); namely, community transmission, pending (unknown), sporadic cases, and clusters of cases. The logistic regression was deployed on the COVID-19 dataset to construct a multinomial model that can predict the prevalent transmission mode of coronavirus within a geographic area. The k-fold cross validation was employed to test predictive accuracy of the model, which yielded 73% accuracy. This model can be adopted by local authorities such as regional, state, local government, and cities, to predict the prevalent transmission mode of the virus within their territories. The outcome of the prediction will determine the appropriate strategies to put in place or re-enforced to curtail further transmission.

Keywords: COVID-19; transmission mode; multi-class prediction; predictive model; community transmission.

DOI: 10.1504/IJMEI.2023.129346

International Journal of Medical Engineering and Informatics, 2023 Vol.15 No.2, pp.120 - 130

Received: 03 Dec 2020
Accepted: 28 Feb 2021
Published online: 07 Mar 2023 *

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