An Explainable Hybrid CNN-LSTM-Random Forest Framework for Early Epidemic Outbreak Detection from Multimodal Data, Validated by Real Corpora and Controlled Simulation
DOI:
https://doi.org/10.33003/fjs-2026-1010-5369Keywords:
Epidemic early warning, Hybrid deep learning, Random Forest, Explainable AI, Multimodal data, Simulation ValidationAbstract
Early detection of epidemic outbreaks is critical for timely intervention, yet machine-learning early-warning systems are limited by low recall on rare early signals, weak integration of heterogeneous data, and poor interpretability. This study develops an explainable hybrid framework in which a Convolutional Neural Network (CNN) and a Bidirectional Long Short-Term Memory (LSTM) network extract features that a Random Forest (RF) classifier uses for decision-making, with SHapley Additive exPlanations (SHAP) providing transparency. The framework is evaluated on two complementary public corpora, 13,629 Ebola-related tweets from the 2022 Ugandan outbreak and 5,644 COVID-19 patient records with 112 clinical features, and its multimodal-fusion mechanism is validated in a controlled simulation in which a stochastic SEIR process generates coupled epidemic and digital signals for the same region-time units under a realistic surveillance blind spot. On the social-media task the proposed CNN-LSTM-RF achieves an accuracy of 0.94, a recall of 0.86, and an F1-score of 0.89, cutting false negatives from 108 to 75. On the clinical task it attains the best recall (0.54) and F1-score (0.56) among compared models; recall and F1 are reported in preference to accuracy because the cohort is strongly imbalanced. SHAP identifies patient age and host-response markers as dominant predictors, consistent with clinical knowledge. In simulation, the fused model attains the highest ROC-AUC (0.943) and PR-AUC (0.851) and alerts on average 2.6 days earlier than a clinical-only model, providing controlled evidence of a genuine, if modest, fusion benefit.
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Copyright (c) 2026 Faruk Obansa Muhammed, Muhammed Aliyu Suleiman, Saleh El-Yakub Abdullahi, Austin Olom Ogar
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