Markov SIR Model for Infectious Disease Transmission
Abstract
This study assesses the effectiveness of transmission models, including the Markov SIR, Gillespie Algorithm, and Reed-Frost Model, in simulating disease trends. Each model was estimated using daily infection and recovery counts and applied to project the progression of active cases. The Markov SIR model demonstrated a strong fit for COVID-19 transmission, particularly in modeling ongoing outbreaks. These findings emphasize its potential for forecasting the spread of infectious diseases, highlighting its adaptability for future outbreak monitoring and response efforts.
Fields of Study & Subjects
COVID-19 epidemiological studies
Mathematical and Theoretical Epidemiology and Ecology Models
Data-Driven Disease Surveillance
Publication details
- Journal / source
- Model Assisted Statistics and Applications
- Volume
- 21
- Issue
- 2
- Pages
- 171–179
- Publication date
- 24 February 2026
- Language
- EN