arbiter
JournalsPricingAccount

Markov SIR Model for Infectious Disease Transmission

V. Deneshkumar et al.

Model Assisted Statistics and Applications2026https://doi.org/10.1177/15741699261424801article
ABDC C
Weight
0.50

What the paper says

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.

Open paper page →

Cite this paper

https://doi.org/https://doi.org/10.1177/15741699261424801

Or copy a formatted citation

@article{v.2026,
  title        = {{Markov SIR Model for Infectious Disease Transmission}},
  author       = {V. Deneshkumar et al.},
  journal      = {Model Assisted Statistics and Applications},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1177/15741699261424801},
}

Paste directly into BibTeX, Zotero, or your reference manager.

Flag this paper

Markov SIR Model for Infectious Disease Transmission

Flags are reviewed by the Arbiter methodology team within 5 business days.

Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.