2026· Dutse Journal of Pure and Applied Sciences· Vol 12, pp. 65-79· 0 citations
TL;DR
A framework that serves as a proof-of-concept for the prediction of typhoid-malaria coinfection using machine learning, with diagnostic testing selected as the most reliable predictor is developed and it can be concluded that the validation of the model should be done with clinical data.
Abstract
Nigeria has a high percentage of burden of both typhoid fever and malaria, with climate change and population dynamics potentially influencing disease co-infection patterns. Emerging evidence suggest that climatic change may have influence in the coinfection dynamics of these diseases. However, a comprehensive epidemiological data on the pattern of coinfection is limited and this invariably affects the evidence-based interventions, the objective of this study is to develop and validate a predictive model that can serve as a framework for the coinfection of malaria and typhoid patterns in Nigeria using the effect of climate change, demographic factors and clinical presentations as contributing variables. An epidemiological informed datasets was generated synthetically using (n=10,000 cases) incorporating a probability distribution prevalence of the disease that were derived from literature on the disease prevalence, symptoms, demographic characteristics across the states in Nigeria. A random forest model classification algorithm was implemented in R programming language environment with the performance metrics evaluated and cross-validated correlation analysis and t-test was also examined to get the relationships between the predictors and the coinfection status. The random forest predictive model achieved an accuracy of 94.47Typhoid test result showed the strongest positive correlation with a co-infection rate of r = 0.533, followed by malaria test results with a co-infection rate of r = 0.361. Temperature as a climatic predictor showed a weak positive correlation of r = 0.040.The analyzed states showed that Borno, Gombe, and Osun have the highest co-infection rates of 65.8%, 65.6%, and 65.5% respectively, this study has developed a framework that serves as a proof-of-concept for the prediction of typhoid-malaria coinfection using machine learning, with diagnostic testing selected as the most reliable predictor, it can be concluded that the validation of the model should be done with clinical data.
Malaria elimination is shaped by complex interactions among climatic, environmental, socioeconomic, demographic, health-system, and intervention-related factors. However most studies examine only subsets of these drivers, limiting understanding of their combined influence on epidemiological risks. In this study, we integrated 25 years of data from 44 African countries on malaria burden and control, climate, environmental and land-use conditions, socioeconomic and demographic characteristics, and health-system capacity within a unified geospatial, explainable machine-learning, and forecasting framework to characterize spatiotemporal patterns of malaria, quantify the relative contributions of key determinants, and generate 10-year Africa-wide and country-specific forecasts of malaria incidence and mortality rates per 1,000 people at risk. We identified and mapped malaria incidence and mortality hotspots using the Getis-Ord Gi* statistic. Our analyses showed that both incidence and mortality burden remained highly heterogeneous across Africa, with persistent hotspots concentrated in the West and Central Africa. The explainable machine-learning model, that achieved high predictive performance (i.e., XGBoost for incidence, holdout R$^2$ = 0.92; Random Forest for mortality, holdout R$^2$ = 0.91), identified lower availability of hospital beds (per 1,000 people), higher mortality rate attributed to unsafe WASH (per 100,000), and lower percentage (%) of people using handwashing facilities as the top three most influential determinants of higher risk of infection across Africa whereas higher mortality rate attributed to unsafe WASH (per 100,000), lower % of people using at least basic sanitation services, and access to electricity (%) were associated with worse mortality outcomes. Forecasting models also demonstrated strong predictive accuracy (Naive persistence and Elastic Net, holdout R$^2$ = 0.98 for incidence and 0.97 for mortality). Assuming current intervention and structural conditions persist, Africa-wide malaria incidence was projected to remain broadly stable, with a modest upward trend by 2035, whereas mortality was projected to decline initially and subsequently remain relatively unchanged. However, substantial country-level heterogeneity showed both emerging transmission hotspots and persistently high-burden countries. this suggests there is a need for sustained control and accelerated elimination efforts. Overall, this study demonstrates that integrating geospatial analysis, explainable machine learning, and forecasting provides a robust framework for understanding malaria dynamics, identifying the key determinants of burden, anticipating future trends, and supporting geographically targeted malaria control across Africa. Beyond malaria, our analyses can be applied as a generalizable approach for infectious disease surveillance, early-warning systems, hotspot detection, resource prioritization, and precision public health using large-scale longitudinal health data.
This study investigated the temporal trends of malaria, tuberculosis, and cholera in Nigeria from 2010 to 2023 using epidemiological and statistical modelling approaches and provides evidence-based recommendations for targeted disease control strategies in Nigeria.
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