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

IF 0.6 Q4 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH
Journal of Public Health in Africa Pub Date : 2024-01-01 eCollection Date: 2023-12-27 DOI:10.4081/jphia.2024.2712
T K Samson, O Aromolaran, T Akingbade
{"title":"Lassa fever cases and mortality in Nigeria: Quantile Regression vs. Machine Learning Models.","authors":"T K Samson, O Aromolaran, T Akingbade","doi":"10.4081/jphia.2024.2712","DOIUrl":null,"url":null,"abstract":"<p><p>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.</p>","PeriodicalId":44723,"journal":{"name":"Journal of Public Health in Africa","volume":"14 12","pages":"2712"},"PeriodicalIF":0.6000,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10801397/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Public Health in Africa","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4081/jphia.2024.2712","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2023/12/27 0:00:00","PubModel":"eCollection","JCR":"Q4","JCRName":"PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH","Score":null,"Total":0}
引用次数: 0

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.

尼日利亚的拉沙热病例和死亡率:Quantile Regression vs. Machine Learning Models.
拉沙热(LF)由拉沙热病毒(LFV)引起。它在西非流行,其中尼日利亚占感染病例的%。这种疾病主要影响育龄人群,因此,正确了解这种疾病的动态将有助于制定政策,遏制拉沙热的蔓延。本研究的目的是比较量化回归模型与机器学习模型在以下方面的性能。研究人员从尼日利亚疾病控制中心(NCDC)获取了 2018 年 1 月 7 日至 2022 年 12 月 17 日期间有关 LF 疑似病例、确诊病例和死亡病例的数据。获得的数据被拟合到 25%、50% 和 75% 的量子回归模型(QRM)以及机器学习模型中。响应变量为尼日利亚拉沙热确诊病例和死亡率,自变量为确诊病例总数、周、月和年。结果显示,拉沙热每月平均确诊病例数(56 例)和死亡率(9 例)最高的月份出现在二月份。尼日利亚每年第一季度报告的确诊病例和死亡病例最多。结果还显示,就确诊病例而言,50% 的定量回归优于最佳多变量回归模型,即高斯-母5/2 GPR(RMSE=10.3393 对 11.615),而就死亡率而言,中等高斯 SVM(RMSE=1.6441 对 1.8352)优于定量回归模型。50% 的定量回归模型更好地捕捉了尼日利亚 LF 确诊病例的动态变化,而中等高斯 SVM 则更好地捕捉了尼日利亚 LF 的死亡率。在选定的特征中,发现确诊病例是导致死亡率的最重要特征,这意味着随着拉沙热确诊病例的增加,其死亡率也会显著增加。因此,有必要采取更好的干预措施,帮助遏制因确诊病例增加而导致的拉沙热死亡率。此外,还需要促进良好的社区卫生,包括阻止啮齿动物进入住宅,将食物放在防鼠容器中以避免污染,从而帮助遏制拉沙热在尼日利亚的传播。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
Journal of Public Health in Africa
Journal of Public Health in Africa PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH-
自引率
0.00%
发文量
82
审稿时长
10 weeks
期刊介绍: The Journal of Public Health in Africa (JPHiA) is a peer-reviewed, academic journal that focuses on health issues in the African continent. The journal editors seek high quality original articles on public health related issues, reviews, comments and more. The aim of the journal is to move public health discourse from the background to the forefront. The success of Africa’s struggle against disease depends on public health approaches.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信