Nov 2026· Computer Science and Information Technology· 0 citations· 25 references
TL;DR
The review points out four persistent gaps: the lack of attention-augmented RNNs for malaria forecasting in Southern Africa, limited integration of health-facility infrastructure features with climate predictors, inadequate handling of missing data in African satellite-derived climate series, and the absence of operational dashboards that make model outputs usable for district health officers without statistical training.
Abstract
Malaria remains a significant public health issue in Sub-Saharan Africa. Predictive modelling is increasingly viewed as a way to move from counting cases reactively to preparing proactively for outbreaks. This literature review examines peer-reviewed research published from 1997 to 2025. It covers recurrent neural networks (RNNs) methods, attention mechanisms, multi-source data integration, and health-system informatics relevant to malaria prediction in low- and middle-income countries. A thorough search through Google Scholar, IEEE Xplore, ScienceDirect, PubMed, and SpringerLink identified 187 candidate papers. After reviewing titles and abstracts, 42 papers met the inclusion criteria. The review organizes the selected studies into six main categories: climate-disease ecology, statistical forecasting, machine learning (ML) baselines, RNNs, attention mechanisms, and health-system data integration. A comparative matrix highlights the similarities and differences in methods across twenty representative studies. The review points out four persistent gaps: the lack of attention-augmented RNNs for malaria forecasting in Southern Africa, limited integration of health-facility infrastructure features with climate predictors, inadequate handling of missing data in African satellite-derived climate series, and the absence of operational dashboards that make model outputs usable for district health officers without statistical training. Future research should focus on cross-country transferability studies, using graph neural networks to capture spatial spillover, and real-time integration with national surveillance systems like district health information software 2 (DHIS2).
Malaria remains one of the most significant infectious disease burdens in Sub-Saharan Africa, placing sustained pressure on public health systems and malaria-control programmes. Accurate and interpretable identification of high-risk districts is therefore essential for early warning, resource allocation, and targeted intervention planning. This study proposes an explainable multimodal artificial intelligence framework that integrates epidemiological, climatic, geospatial, and demographic information through a modality-specific encoder-fusion architecture. The framework is further extended with a spatial Graph Neural Network component designed to capture inter-district dependencies and potential spatial spillover effects. Using a synthetically generated district-level dataset (n = 1,200), calibrated to reflect the statistical properties of variables commonly used in Malaria Atlas Project (MAP), NASA POWER, CHIRPS, WorldPop, and Sentinel-2 products across five malaria-endemic countries (the Democratic Republic of the Congo, Uganda, Nigeria, Tanzania, and Kenya), the proposed framework achieved a validation R² of 0.7285 for Plasmodium falciparum parasite rate regression. For binary high-risk classification, the framework attained an area under the curve of 0.9740 and an F1-score of 0.9630 on the held-out test dataset. SHapley Additive exPlanations were used to examine the contribution of individual predictors at global and local levels. Incidence rate (67.2%), rainfall (10.2%), and temperature (6.2%) emerged as the leading drivers of predicted risk, while intervention coverage showed a protective contribution. The results should be read as a synthetic proof-of-concept rather than as an operational malaria map. Even so, they show how multimodal learning, graph-based spatial modeling, and explainable AI can be combined into a transparent surveillance pipeline for future validation with real-world malaria data in resource-constrained settings.
Kiro M Lauraine, M. Kasambya, Mundeke Ghislain et al.· Cureus Journal of Computer S...· 0 citations
Dengue is an arboviral disease of high public health relevance, characterized by pronounced temporal variability, nonlinearity, and recurrent outbreaks, which pose challenges to epidemiological surveillance and decision-making. This study evaluated the performance of machine learning methods for short-term forecasting of the weekly dengue morbidity rate in the 27 Brazilian capital cities, comprising the 26 state capitals and Brasília, Federal District, with horizons up to 4 weeks. Epidemiological, climatic, and socioeconomic data were compiled for these capital cities and used to compare a Gated Recurrent Unit (GRU) neural network, formulated as a Multi-Input Multi-Output (MIMO) model, and a Gradient Boosting model (CatBoost), implemented using a Direct forecasting strategy with horizon-specific models. Validation was conducted using a walk-forward approach, with evaluation based on absolute error metrics and the coefficient of determination. The results indicated that the GRU architecture presented recurring limitations, including underfitting, temporal lag, and low capacity to anticipate epidemic peaks. In contrast, the CatBoost model demonstrated greater robustness and better adaptation to the variability of epidemiological time series, showing superior performance in most of the analyzed capitals. The findings reinforce that greater architectural complexity does not necessarily imply better operational performance and highlight the potential of ensemble-based methods for short-term epidemiological surveillance applications. These findings contribute to dengue forecasting by showing that, under a common validation framework, ensemble-based strategies may provide greater operational robustness than recurrent MIMO architectures for short-term prediction in heterogeneous epidemiological settings.
D. C. C. Silva, L. M. Nery, N. P. Nicomedes et al.· International journal of bio...· 0 citations
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.
Beyond predictive accuracy, qualitative criteria including explain-ability, transparency, and adaptability were evaluated, further highlighting the superiority of CBR 2 over conventional black-box models.
Konan N’gatta Aimé Kouassi, Koffi Kouakou Ive Arsene, Goore Bi Tra· International Journal of Adv...· 0 citations
Cancer mortality is rising rapidly across the African continent, yet Somalia lacks localized national forecasting evidence due to decades of instability and the absence of a formal registry. This study aims to develop and compare high-precision time-series forecasting models to project cancer mortality trends in Somalia through 2030, providing a critical statistical baseline for proactive healthcare resource allocation. Annual cancer mortality data (1980–2021) were retrieved from the Global Burden of Disease (GBD) database. A rigorous comparative analysis of 12 representative forecasting configurations was conducted, comprising six single models (including ARIMA, ETS, TBATS, and the Theta method) and six optimized hybrid ensembles integrated with Artificial Intelligence (AI) proxies like Extreme Learning Machines (ELM) and Neural Network Autoregression (NNAR). To stabilize non-stationary trends and mitigate overfitting in a limited sample context (n = 42), second-order differencing (d = 2) and a 10-year rolling-origin cross-validation (tsCV) were implemented. Model performance was benchmarked using RMSE, MAPE, Theil’s U, Willmott’s Index of Agreement (WI), and Skill Scores. All evaluated configurations achieved Theil’s U statistics below 1.0, indicating superior performance relative to the naive benchmark. The parsimonious ARIMA (2,2,1) model emerged as the most accurate configuration for long-term forecasting, achieving the lowest MAPE (1.127%) and RMSE (100.16) in the independent holdout set. The statistical superiority of ARIMA (2,2,1) was confirmed via the Diebold–Mariano test (p < 0.01) against the naive benchmark, outperforming complex hybrid ensembles which showed higher error accumulation over the 8-year horizon. Projections indicate a consistent monotonic upward trend, with annual cancer deaths estimated to reach approximately 9,652 by the year 2030. The findings indicate a significant and ongoing epidemiological transition in Somalia. These forecasts provide a robust statistical baseline to assist the Somali Ministry of Health in strategic planning for oncology infrastructure, diagnostic capacity expansion, and the formal establishment of a National Cancer Registry to transition from reconstructed estimates to actual patient records.
Suhaib Mohamed Kahie Seiman, Mustafe Mohamoud Abdi, S. Nadarajah et al.· Discover Public Health· 0 citations
Highly pathogenic avian influenza (HPAI) remains a recurrent threat to poultry production and One-Health surveillance in India. We developed a national relative spatial risk map for India using curated outbreak records spanning January 2006 to April 2024 (predominantly HPAI H5N1 and H5N8), buffered pseudo-absence sampling, H3 resolution-7 hexagons, and 94 environmental, livestock, land-cover, and anthropogenic predictors. Under 5-fold spatial block cross-validation, eight base classifiers were trained and all performed above chance (AUC > 0.76). The Gaussian-process stacked meta-learner achieved the highest AUC (0.853), but the improvement over the strongest individual base learner, Random Forest (AUC 0.851; Brier 0.155; ECE 0.079), was small and statistically non-significant. Its principal added value was a companion uncertainty layer, the GPR posterior standard deviation, which showed internal consistency with ensemble disagreement across base models (r = 0.74, p < 0.001). Feature attribution ranked human population density, extensive chicken density, June precipitation, and seasonal humidity variables among the predictors most associated with model outputs, with interpretation constrained by passive-surveillance bias and multicollinearity. The resulting relative spatial risk surface, prediction-uncertainty surface, and subdistrict risk-uncertainty classification layers identify eastern, northeastern, coastal, and selected southern regions as priorities for targeted surveillance and prospective validation.
Satish Gaikwad, T. R. Arun, Basavaraj Shrinivasa et al.· Frontiers in Veterinary Scie...· 0 citations
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