Evaluating transient risk factors for avian influenza outbreaks in Canada using case-crossover study and machine learning

Avian influenza risk mapping in India using machine learning

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.

Predictive Modelling of Lassa Fever Outbreaks

Lassa fever remains a critical and highly perilous public health threat across West Africa, with Ondo State, Nigeria, consistently experiencing severe annual outbreaks. While accurate forecasting is essential for timely medical intervention, conventional predictive models often fail to capture the complex spatiotemporal transmission dynamics and the delayed ecological triggers of the Lassa virus. To address this, this study introduces a comprehensive Health Information Pattern Discovery framework to forecast outbreaks across four high-risk localized hotspots: Akure South, Akure North, Akoko Southwest, and Owo. Advancing beyond standard machine learning benchmarking, this research integrates spatial-temporal graph theoretic modelling to map the relational transmission velocity between regions , coupled with a Genetic Algorithm (GA) to optimize complex lagged meteorological variables, including temperature, relative humidity, and precipitation. The optimized spatiotemporal feature space was utilized to train and evaluate four distinct architectures: Random Forest, Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), and Long Short-Term Memory (LSTM) networks. Empirical evaluation demonstrated that the GA-optimized Random Forest ensemble outperformed the other models, achieving a Root Mean Squared Error (RMSE) of 5.627, a Mean Absolute Error (MAE) of 3.747, and an of 0.87. Beyond baseline predictive accuracy, this hybrid graph-theoretic and evolutionary approach successfully isolates the exact environmental triggers preceding an outbreak, providing interpretable, actionable intelligence to strengthen proactive public health planning and targeted interventions in endemic regions.

Spatio-temporal Modelling of H5N1 Avian Influenza Outbreaks in Europe (2021-2024)

A data-driven spatio-temporal framework that integrates geospatial, ecological and climatic datasets to explain and forecast the dynamics of H5N1 outbreaks between 2021 and 2024 indicates that H5N1 transmission is structured by ecological drivers and local persistence mechanisms rather than purely seasonal effects.

Forecasting high pathogenicity avian influenza with a stochastic mechanistic model: performance and lessons for Australia

CLARION-RF: A Climate-Lag-Aware Probabilistic Framework for Early Prediction of Vector-Borne Disease Outbreak Risk in Karnataka, India

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.

Geographic expansion, not viral intensification, leads to human dengue outbreaks in Mexico: a 40-year integrated remote sensing and machine learning analysis

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.