Jul 2026· International Conference Computing Methodologies and Communication· pp. 1515-1521· 0 citations· 21 references
Abstract
Vector-borne diseases like dengue, chikungunya, and malaria continue to be a significant public health concern in India, especially in climatically sensitive areas where seasonal effects have a strong impact on disease transmission. Existing national surveillance systems are largely reactive, detecting outbreaks only after case counts rise substantially. This work proposes the CLARION-RF model, a probabilistic framework that is climate-lag aware and capable of early outbreak-risk prediction at the district level. The model combines time-series data from the weekly disease surveillance system with corresponding meteorological variables and climate lag features. The Random Forest-based probabilistic model is trained by balancing the samples to address class imbalance. Instead of deterministic predictions, the model estimates outbreak probabilities and stratifies them into three risk levels: low, medium, and high. Time-aware training (2011–2023) and testing in 2024 demonstrate stable predictive performance, with ROC-AUC and PR-AUC of 0.667 and 0.579, respectively, for the best model.
The development and assessment of a machine learning-driven early warning system for infectious disease prediction using geospatial big data from South-Western Nigeria, at the level of Local Government Area outperform conventional surveillance systems in developing countries.
I. Adewumi, N. Bakare, W. Ajayi et al.· London Journal of Physics· 0 citations
Background Dengue fever poses a pervasive, yet escalating public health burden in Mexico and abroad. Methods We conducted a 41-year spatiotemporal analysis of dengue fever across Mexico (1985–2025), integrating monthly case surveillance with climate, land cover, vegetation, and novel disaster severity covariates derived from the Emergency Events Database. Four supervised regression models were trained on 1990–2021 data, with models evaluated on a 2022–2023 temporal holdout and against observed 2024–2025 surveillance totals. Five supplementary hazard analyses examined temporal correlation, disaster type breakdown, spatial co-occurrence, pre/post event trajectories, and sensitivity to scoring weight assumptions. Results Mann–Kendall trend analysis identified statistically significant increasing dengue incidence in 15 of 32 states (9 inland), evidencing geographic expansion over four decades. Z-score analysis confirmed 2024 as a profound anomaly across both endemic and emerging states. Hazard features were significantly associated with national monthly dengue counts at lags 0–2 months across the full 1985–2025 series. A + 613% case increase following the June 2024 tropical storm. Sensitivity analyses confirmed the project’s developed ‘Severity Score’ performed comparably to four theoretically motivated differential weighting schemes. Conclusion This study provides the first structured integration of disaster severity covariates in a dengue forecasting framework for Mexico. Geographic expansion into inland states, the 41-year trend analysis, and the hazard adjustment results collectively support a differentiated public health response that targets both endemic coastal states and newly affected inland regions. The analytical framework is directly transferable to emerging dengue risk contexts in the United States and Central America, geographic neighbors also experiencing increased dengue virus transmission.
Huixuan Li, Christopher Lee, Sean Sweeney et al.· Frontiers in Public Health· 0 citations
Dengue remains a major public health problem in endemic regions, including Bangladesh. Forecasting algorithms relying on climatic variables may not capture epidemiological information such as dengue serotype patterns. This study proposes a horizon-dependent dengue forecasting framework and applies it to Bangladesh. This pipeline included temporal, meteorological, demographic, and epidemiological covariates in a district-panel pipeline and compares classical, machine-learning, and deep-learning algorithms using aggregate, horizon-wise, regime-wise, outbreak-detection, and uncertainty metrics. Coefficient-based interpretation, SHAP, permutation importance, and nonlinear causal-dependence analysis were used to examine predictor impacts across horizons. SARIMAX was used as the baseline model. TFT produced quantile forecasts but showed poor interval calibration during outbreak periods. Results show that no single model performed best across all forecasting horizons: SARIMAX ranked highest for one-step outbreak alerting (precision = 0.886, recall = 0.824, F1 score = 0.854, and ROC-AUC = 0.950), whereas Prophet performed best for pooled magnitude forecasting at horizons 2–6. MLR showed competitive performance in regime-wise evaluation. These findings show that dengue forecasting algorithms should be selected according to the intended decision objective and evaluated using task-relevant protocols.
M. Fuad, Maha Milki, Ridwan Al Aziz· PLoS ONE· 0 citations
Background: Evaluating outbreak detection models is a key component of syndromic surveillance. However, balancing timeliness, predictive performance, and local surveillance constraints remains a major challenge. We developed and assessed whether stacking ensemble approaches, which integrate multiple outbreak detection methods, can improve the timeliness and predictive performance of influenza-like illness (ILI) surge detection. Methods: We developed a two-stage stacking ensemble framework to detect early warning of ILI surges in city-level Primary Health Care encounter time series from Brazil (2022 to 2025). Epidemic thresholds were defined using the Moving Epidemic Method (MEM). In the first stage, multiple outbreak detection models (ODMs) generated warnings of unusual ILI activity. In the second, these warnings were then used as inputs to three supervised meta-classifiers: Logistic Regression, Extreme Gradient Boosting (XGB), and a Multi-layer Perceptron (MLP). For comparison, a Majority Voting (MV) aggregation is also examined. Timeliness, sensitivity, specificity, positive and negative predictive values are evaluated to measure each model's ability to anticipate epidemic periods of varying intensity in 2025. Robustness was further assessed using simulated outbreak scenarios with varying magnitudes and durations. Findings: We identified 5,765 ILI surge onsets across 5,365 Brazilian municipalities in 2025. Compared with individual ODMs and MV, stacking ensemble meta-classifiers anticipated up to 33% of surge onsets three weeks in advance (an average improvement of 15 percentage points) while reducing missed detections to <10%. They achieved sensitivity >90%, while maintaining balanced specificity >80%, PPV >65%, and NPV >99%. Improvements were greatest for very high-intensity surges, with missed detections reduced by more than half compared with individual ODMs. In simulated outbreak scenarios, the MLP and XGB classifiers remained robust despite being trained on fewer than half of all simulated surge events, consistently outperforming individual detection methods and simpler integration approaches. Interpretation: We provide a practical framework for integrating complementary ODMs into a single, robust early warning decision. By improving both timeliness and predictive performance without requiring additional surveillance data or resources, this approach offers a scalable methodological upgrade for syndromic surveillance systems and supports more reliable public health decision-making. Funding: The Rockefeller Foundation (award 2023 PPI 007 to MB-N); Brazilian National Research Council - CNPq (408775/2024-6); MB-N, PIPR, RFSA are CNPq fellows.
J. F. Oliveira, A. L. Alencar, E. R. Coutinho et al.· medRxiv· 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.
The feasibility of predicting dengue outbreaks using weekly multivariate time-series data from San Juan, Puerto Rico and Iquitos, Peru is examined, demonstrating that advanced machine learning models can serve as reliable early warning systems for vector-borne diseases.
Pratik S. Machchar, Purvi N. Ramanuj, R. Patel et al.· International Journal of Inf...· 0 citations
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