Original Article

Lassa fever cases and mortality in Nigeria: Quantile Regression vs. Machine Learning Models

Timothy Samson, Olukemi Aromolaran, Tosin Akingbade
Journal of Public Health in Africa | Vol 14, No 12 | a22 | DOI: https://doi.org/10.4081/jphia.2023.2712 | © 2024 Timothy Samson, Olukemi Aromolaran, Tosin Akingbade | This work is licensed under CC Attribution 4.0
Submitted: 12 March 2024 | Published: 30 December 2023

About the author(s)

Timothy Samson, College of Agriculture, Engineering and Science
Olukemi Aromolaran, College of Agriculture, Engineering and Science, Bowen University, Iwo, Nigeria
Tosin Akingbade, College of Agriculture, Engineering and Science

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Abstract

Lassa fever (LF) is caused by the Lassa fever virus (LFV). It is endemic in West Africa, of which % of the infections are ascribed to Nigeria. This disease affects mostly the productive age and hence a proper understanding of the dynamics of this disease will help in formulating policies that would help in curbing the spread of LF. The objective of this study is to compare the performance of quantile regression models with that of Machine Learning models in. data between between 7th January 2018 2018 and 17th december, 2022 on suspected cases, confirmed cases and deaths resulting from LF were retrieved from the Nigeria Centre for disease Control (NCDC). The data obtained were fitted to quantile regression models (QRM) at 25, 50 and 75% as well as to Machine learning models. The response variable being confirmed cases and mortality due to Lassa fever in Nigeria while the independent variables were total confirmed cases, the week, month and year. Result showed that the highest monthly mean confirmed cases (56) and mortality (9) from LF were reported in February. The first quarter of the year reported the highest cases of both confirmed cases and deaths in Nigeria. Result also revealed that for the confirmed cases, quantile regression at 50% outperformed the best of the MLM, Gaussian‑matern5/2 GPR (RMSE=10.3393 vs. 11.615), while for mortality, the medium Gaussian SVM (RMSE=1.6441 vs. 1.8352) outperformed QRM. Quantile regression model at 50% better captured the dynamics of the confirmed cases of LF in Nigeria while the medium Gaussian SVM better captured the mortality of LF in Nigeria. Among the features selected, confirmed cases was found to be the most important feature that drive its mortality with the implication that as the confirmed cases of Lassa fever increases, is a significant increase in its mortality. This therefore necessitates a need for a better intervention measures that will help curb Lassa fever mortality as a result of the increase in the confirmed cases. There is also a need for promotion of good community hygiene which could include; discouraging rodents from entering homes and putting food in rodent proof containers to avoid contamination to help hart the spread of Lassa fever in Nigeria.

Keywords

Lassa fever; Quantile regression model; Machine learning model; confirmed cases; mortality; Nigeria

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