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Lassa fever cases and mortality in Nigeria: Quantile Regression vs. Machine Learning Models
T K Samson1, O Aromolaran2, T Akingbade1
1Statistics Programme, College of Agriculture, Engineering and Science.
Journal of Public Health in Africa
|January 23, 2024
Summary
Lassa fever (LF) modeling in Nigeria shows quantile regression best predicts confirmed cases, while machine learning excels at predicting mortality. This highlights the need for targeted interventions to curb Lassa fever spread and deaths.
Area of Science:
- Epidemiology and Public Health
- Biostatistics and Data Modeling
- Infectious Disease Dynamics
Background:
- Lassa fever (LF), caused by the Lassa fever virus (LFV), is endemic in West Africa, with Nigeria accounting for a significant proportion of infections.
- Understanding LF transmission dynamics is crucial for effective public health policy, particularly as it affects the productive age group.
- The Nigeria Centre for Disease Control (NCDC) provides vital data on LF cases and mortality.
Purpose of the Study:
- To compare the predictive performance of quantile regression models (QRM) against Machine Learning models (MLM) for Lassa fever dynamics in Nigeria.
- To identify the most effective modeling approach for predicting confirmed Lassa fever cases and mortality.
- To determine key factors influencing Lassa fever mortality.
Main Methods:
- Utilized Lassa fever data (suspected cases, confirmed cases, deaths) from Nigeria Centre for Disease Control (NCDC) spanning January 2018 to December 2022.
- Fitted data to quantile regression models (QRM) at 25%, 50%, and 75% quantiles and various machine learning models (MLM).
- Response variables: confirmed cases and mortality; Independent variables: total confirmed cases, week, month, and year.
Main Results:
- Highest monthly mean confirmed cases (56) and mortality (9) occurred in February; the first quarter showed peak cases and deaths.
- For confirmed cases, QRM at 50% outperformed the best MLM (Gaussian-matern5/2 GPR) with lower RMSE (10.3393 vs. 11.615).
- For mortality, the medium Gaussian SVM (MLM) outperformed QRM with lower RMSE (1.6441 vs. 1.8352). Confirmed cases were the most significant predictor of mortality.
Conclusions:
- Quantile regression at 50% effectively captured confirmed Lassa fever case dynamics in Nigeria.
- Medium Gaussian SVM demonstrated superior performance in modeling Lassa fever mortality.
- Increased confirmed cases significantly drive Lassa fever mortality, necessitating enhanced intervention strategies and community hygiene promotion to prevent rodent contamination.
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