Computational Intelligence Models for Disease Outbreak Prediction

Abstract

Disease outbreak prediction has become increasingly important due to the rise of emerging and re-emerging infectious diseases such as COVID-19, Ebola, Zika, Dengue, and Influenza. Traditional epidemiological surveillance systems rely on historical case reports and laboratory confirmations, often resulting in delayed reporting and limited spatial resolution, which hinder timely intervention and preparedness. Computational Intelligence Systems (CIS), including machine learning (ML), deep learning (DL), evolutionary computing, and fuzzy systems, offer improved prediction by identifying complex nonlinear patterns from heterogeneous data sources such as epidemiological, environmental, mobility, and social media data. This paper presents a comprehensive review of CI models for outbreak prediction, comparing supervised and unsupervised methods such as Support Vector Machines (SVM), Random Forest (RF), Artificial Neural Networks (ANN), Long Short-Term Memory (LSTM), and hybrid models. A multi-layered predictive framework is proposed, incorporating data preprocessing, feature engineering, dimensionality reduction, ensemble learning, and performance evaluation. A hybrid LSTM–CNN model is introduced to capture temporal and spatial dependencies in outbreak data. Experimental results demonstrate that CI models outperform traditional statistical approaches like ARIMA and regression models in short- and medium-term forecasting. Evaluation metrics including RMSE, MAPE, Precision, Recall, and F1-score confirm the superior predictive performance and robustness of hybrid models. The study highlights the importance of integrating heterogeneous data and intelligent modeling for smart disease surveillance and supports future research in explainable AI, federated learning, and real-time adaptive prediction systems.