Enhancing Early Warning Outbreak Detection Using Multi Model Stacking Ensemble

Abstract

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.