Data Science Models for Short-Term Forecast of COVID-19 Spread in Nigeria

Ijegwa David Acheme (), Olufunke Rebecca Vincent () and Olaniyi Mathew Olayiwola ()
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Ijegwa David Acheme: Edo State University Uzairue
Olufunke Rebecca Vincent: Federal University of Agriculture
Olaniyi Mathew Olayiwola: Federal University of Agriculture

Chapter Chapter 20 in Decision Sciences for COVID-19, 2022, pp 343-363 from Springer

Abstract: Abstract The study presents data science models for a real-time forecast of COVID-19 size and spread in Nigeria. Firstly, an exploratory and comparative study of the disease spread in Nigeria and some other African nations are carried out. Then variants of support vector machine (SVM) using the Gaussian kernel and regression machine learning models suitable for modeling count data variables are built to estimate a 15-day prediction of infection cases. The data science models built in this research give a short-term forecast of the disease’s spread which is useful in better understanding the spread patterns of the disease as well as enabling future preparedness and better management of the disease by the government and relevant authorities. The research outcome can therefore serve as an effective decision support system. This work can also serve as an alternative to the mathematical-based epidemiological models for the forecast of COVID-19 spread because of their inherent advantages of learning from historical datasets and generalizing with new sets of data which promises better results.

Keywords: Coronavirus spread; COVID-19 spread; Machine learning; Poisson regression; Support vector (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:spr:isochp:978-3-030-87019-5_20

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DOI: 10.1007/978-3-030-87019-5_20

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