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

In: Decision Sciences for COVID-19

Author

Listed:
  • Ijegwa David Acheme

    (Edo State University Uzairue)

  • Olufunke Rebecca Vincent

    (Federal University of Agriculture)

  • Olaniyi Mathew Olayiwola

    (Federal University of Agriculture)

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.

Suggested Citation

  • Ijegwa David Acheme & Olufunke Rebecca Vincent & Olaniyi Mathew Olayiwola, 2022. "Data Science Models for Short-Term Forecast of COVID-19 Spread in Nigeria," International Series in Operations Research & Management Science, in: Said Ali Hassan & Ali Wagdy Mohamed & Khalid Abdulaziz Alnowibet (ed.), Decision Sciences for COVID-19, chapter 0, pages 343-363, Springer.
    • Handle: RePEc:spr:isochp:978-3-030-87019-5_20
    DOI: 10.1007/978-3-030-87019-5_20
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