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.

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.

• 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