A Hybrid Mathematical Model for Epidemic Outbreak Forecasting Using Machine Learning and Cloud Computing

Abstract

The increasing frequency of infectious disease outbreaks has emphasized the necessity for intelligent epidemic surveillance systems capable of predicting disease spread at an early stage. Conventional outbreak detection approaches rely heavily on delayed statistical reporting and manual monitoring techniques, resulting in reduced responsiveness during critical periods. This paper presents a mathematical predictive framework for epidemic outbreak detection using machine learning and cloud computing technologies. The proposed framework integrates the Susceptible–Infected–Recovered (SIR) mathematical epidemic model with a Random Forest machine learning classifier to improve outbreak prediction accuracy. Cloud computing infrastructure was utilized to deliver scalable storage, distributed computing, and real-time analytical capabilities. Experimental evaluation was conducted using epidemiological datasets that included infection rates, recovery statistics, mobility patterns, and demographic data. The proposed hybrid model attained an accuracy of 95.4%, surpassing conventional machine learning approaches such as Support Vector Machines and Artificial Neural Networks. In addition, cloud-based deployment considerably minimized processing latency while improving overall system scalability. The proposed framework provides an effective, scalable, and intelligent approach for public health surveillance and epidemic preparedness.