Combining mathematical epidemiology and machine learning for disease spread analysis
Abstract
The Susceptible-Infected-Recovered (SIR) model is a cornerstone in epidemiological modelling used to predict the spread of infectious diseases. With the emergence of machine learning (ML), combining these technologies has shown considerable potential in enhancing the predictive accuracy and adaptability of the SIR model. This paper reviews the integration of the SIR model with machine learning, focusing on methodologies, applications, advancements, and challenges in improving disease outbreak predictions.
Fields of Study & Subjects
Mathematical and Theoretical Epidemiology and Ecology Models
COVID-19 epidemiological studies
Complex Network Analysis Techniques
Publication details
- Journal / source
- AIP conference proceedings
- Volume
- 3398
- Pages
- 020008–020008
- Publication date
- 1 January 2026
- Language
- EN