Mathematical Modeling of Influenza Epidemic Dynamics using Fractional-Order SEIR Equations with IoT-Enabled Biosensors and AI-based Biotechnology for Real-Time Outbreak Prediction

Abstract

Influenza remains a major public health concern because of its rapid transmission, seasonal variation, and potential to cause widespread outbreaks. Accurate and timely prediction of influenza dynamics is essential for effective disease surveillance, healthcare resource allocation, and early intervention. This study proposes a hybrid mathematical modeling framework that integrates fractional-order Susceptible–Exposed–Infectious–Recovered (SEIR) equations, Internet of Things (IoT)-enabled biosensors, and artificial intelligence (AI)-based forecasting techniques for real-time influenza outbreak prediction. The fractional-order SEIR model incorporates memory effects and nonlocal temporal dependencies to represent the complex progression of influenza more flexibly than conventional integer-order epidemic models. IoT-enabled sensing technologies are considered for continuous collection of relevant physiological and epidemiological information, while AI techniques are employed to identify nonlinear patterns and improve short-term forecasting. The proposed hybrid framework combines mechanistic epidemic predictions with AI-based residual correction to enhance predictive performance. Model effectiveness will be assessed by comparing the classical SEIR, fractional-order SEIR, standalone AI, and hybrid SEIR–AI approaches using evaluation metrics such as Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and outbreak peak prediction error. The framework also incorporates sensitivity analysis and an early-warning mechanism to support timely public health decisions. The proposed study is expected to provide a flexible and data-driven approach to influenza surveillance and outbreak forecasting. Its practical performance will depend on the availability of reliable epidemiological data, sensor validation, and rigorous testing across different influenza seasons and populations.