The Lassa fever cases and mortality in Nigeria: Quantile Regression versus Machine Learning Models
Introduction. Lassa fever (LF) is caused by the Lassa fever virus (LFV). It is endemic in West Africa, of which 25% 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 pol...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
PAGEPress Publications
2024-01-01
|
| Series: | Journal of Public Health in Africa |
| Subjects: | |
| Online Access: | https://www.publichealthinafrica.org/jphia/article/view/2712 |
| _version_ | 1797353345382350848 |
|---|---|
| author | Timothy Samson Olukemi Aromolaran Tosin Akingbade |
| author_facet | Timothy Samson Olukemi Aromolaran Tosin Akingbade |
| author_sort | Timothy Samson |
| collection | DOAJ |
| description |
Introduction. Lassa fever (LF) is caused by the Lassa fever virus (LFV). It is endemic in West Africa, of which 25% 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.
Objectives. The objective of this study is to compare the performance of quantile regression models with that of Machine Learning models.
Methods. 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.
Results. 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 versus 11.615), while for mortality, the medium Gaussian SVM (RMSE =1.6441 versus 1.8352) outperformed QRM.
Conclusion. 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.
|
| first_indexed | 2024-03-08T13:29:47Z |
| format | Article |
| id | doaj.art-c08375b3b27b47588b3778cde266e191 |
| institution | Directory Open Access Journal |
| issn | 2038-9922 2038-9930 |
| language | English |
| last_indexed | 2024-03-08T13:29:47Z |
| publishDate | 2024-01-01 |
| publisher | PAGEPress Publications |
| record_format | Article |
| series | Journal of Public Health in Africa |
| spelling | doaj.art-c08375b3b27b47588b3778cde266e1912024-01-17T10:40:22ZengPAGEPress PublicationsJournal of Public Health in Africa2038-99222038-99302024-01-01141210.4081/jphia.2023.2712The Lassa fever cases and mortality in Nigeria: Quantile Regression versus Machine Learning ModelsTimothy Samson0Olukemi Aromolaran1Tosin Akingbade2Bowen University, Iwo, NigeriaBowen University, Iwo, NigeriaBowen University, Iwo, Nigeria Introduction. Lassa fever (LF) is caused by the Lassa fever virus (LFV). It is endemic in West Africa, of which 25% 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. Objectives. The objective of this study is to compare the performance of quantile regression models with that of Machine Learning models. Methods. 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. Results. 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 versus 11.615), while for mortality, the medium Gaussian SVM (RMSE =1.6441 versus 1.8352) outperformed QRM. Conclusion. 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. https://www.publichealthinafrica.org/jphia/article/view/2712Lassa fever, Quantile regression model, Machine learning model, confirmed cases, mortality |
| spellingShingle | Timothy Samson Olukemi Aromolaran Tosin Akingbade The Lassa fever cases and mortality in Nigeria: Quantile Regression versus Machine Learning Models Journal of Public Health in Africa Lassa fever, Quantile regression model, Machine learning model, confirmed cases, mortality |
| title | The Lassa fever cases and mortality in Nigeria: Quantile Regression versus Machine Learning Models |
| title_full | The Lassa fever cases and mortality in Nigeria: Quantile Regression versus Machine Learning Models |
| title_fullStr | The Lassa fever cases and mortality in Nigeria: Quantile Regression versus Machine Learning Models |
| title_full_unstemmed | The Lassa fever cases and mortality in Nigeria: Quantile Regression versus Machine Learning Models |
| title_short | The Lassa fever cases and mortality in Nigeria: Quantile Regression versus Machine Learning Models |
| title_sort | lassa fever cases and mortality in nigeria quantile regression versus machine learning models |
| topic | Lassa fever, Quantile regression model, Machine learning model, confirmed cases, mortality |
| url | https://www.publichealthinafrica.org/jphia/article/view/2712 |
| work_keys_str_mv | AT timothysamson thelassafevercasesandmortalityinnigeriaquantileregressionversusmachinelearningmodels AT olukemiaromolaran thelassafevercasesandmortalityinnigeriaquantileregressionversusmachinelearningmodels AT tosinakingbade thelassafevercasesandmortalityinnigeriaquantileregressionversusmachinelearningmodels AT timothysamson lassafevercasesandmortalityinnigeriaquantileregressionversusmachinelearningmodels AT olukemiaromolaran lassafevercasesandmortalityinnigeriaquantileregressionversusmachinelearningmodels AT tosinakingbade lassafevercasesandmortalityinnigeriaquantileregressionversusmachinelearningmodels |