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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.
摘要：
拉沙热（LF）是由拉沙热病毒（LFV）引起的。它是西非特有的，其中%的感染归因于尼日利亚。这种疾病主要影响生产年龄，因此对这种疾病的动态的正确理解将有助于制定有助于遏制LF传播的政策。这项研究的目的是比较分位数回归模型与中机器学习模型的性能。2018年1月7日至12月17日之间的数据，2022年疑似病例，从尼日利亚疾病控制中心(NCDC)检索到由LF导致的确诊病例和死亡。将获得的数据拟合到25％、50％和75％的分位数回归模型（QRM）以及机器学习模型。响应变量为尼日利亚拉沙热确诊病例和死亡率，而独立变量为总确诊病例。本周,月份和年份。结果显示，2月份报告的LF每月平均确诊病例（56）和死亡率（9）最高。今年第一季度，尼日利亚的确诊病例和死亡病例最高。结果还显示，对于确诊病例，50%的分位数回归优于传销中的最佳，高斯-材料5/2GPR（RMSE=10.3393与11.615),而对于死亡率，中高斯SVM(RMSE=1.6441vs.1.8352）跑赢QRM。分位数回归模型在50％更好地捕获了尼日利亚LF确诊病例的动态，而中等高斯SVM更好地捕获了尼日利亚LF的死亡率。在选定的功能中，确诊病例被发现是导致其死亡的最重要特征，这意味着随着拉沙热确诊病例的增加，是其死亡率的显著增加。因此，有必要采取更好的干预措施，以帮助遏制因确诊病例增加而导致的拉沙热死亡率。还需要促进良好的社区卫生，其中可能包括：阻止啮齿动物进入房屋并将食物放入防鼠容器中，以避免污染，以帮助遏制拉沙热在尼日利亚的传播。
