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Data-driven approach to COVID-19 infection forecast for Nigeria using negative binomial regression model

Olisah, Chollette C.; Ilori, Olusoji O.; Adelaja, Kunle; Usip, Patience U.; Uzoechi, Lazarus O.; Adeyanju, Ibrahim A.; Odumuyiwa, Victor T.

Authors

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Dr. Chollette Olisah Chollette.Olisah@uwe.ac.uk
Research Fellow in Computer Vision and Machine Learning

Olusoji O. Ilori

Kunle Adelaja

Patience U. Usip

Lazarus O. Uzoechi

Ibrahim A. Adeyanju

Victor T. Odumuyiwa



Abstract

COVID-19: the new wave of a global pandemic, is bringing about an increasing number of scientific efforts aimed at enabling governments to make informed decisions. In this paper, we explore the negative binomial regression model from the family of generalized linear models for the prediction of the future infection pattern of COVID-19 in Nigeria. We approached the prediction from a new perspective that is inspired by transfer learning and feature engineering approaches widely adopted in machine learning. We trained the model to learn COVID-19 pattern cues of other countries such as South Africa, Senegal, Slovenia, Australia, Belgium, and Israel with sufficient and recorded infection cases and test count as baseline data; and created additional features to increase the model’s predictive power. With a testing capacity of 2000 persons per day in Nigeria, the cumulative infection counts for 30-04-2020, 15-05-2020, and 22-05-2020 were predicted to rise to 3044, 5622, and 7254 respectively.

Citation

Olisah, C. C., Ilori, O. O., Adelaja, K., Usip, P. U., Uzoechi, L. O., Adeyanju, I. A., & Odumuyiwa, V. T. (2021). Data-driven approach to COVID-19 infection forecast for Nigeria using negative binomial regression model. In Data Science for COVID-19 (583-596). Elsevier. https://doi.org/10.1016/b978-0-12-824536-1.00002-2

Online Publication Date May 21, 2021
Publication Date 2021
Deposit Date Jun 29, 2021
Pages 583-596
Book Title Data Science for COVID-19
Chapter Number 31
ISBN 9780128245361
DOI https://doi.org/10.1016/b978-0-12-824536-1.00002-2
Public URL https://uwe-repository.worktribe.com/output/7499119