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<p>The study of infectious disease transmission involves understanding its progression within populations and the nature of interactions between individuals. This paper presents a network-based stochastic model, Susceptible, Exposed, Infected, Quarantined, Vaccinated, Recovered (SEIQVR), which extends the classical SEIR framework by incorporating quarantine and vaccination stages. These additional compartments aim to reflect real-world interventions such as isolation and immunization, often overlooked in earlier models. By integrating population density, human mobility, and healthcare infrastructure into the contact network, the model simulates the dynamics of disease spread with greater accuracy. The SEIQVR model has been validated using real-world COVID-19 data from Mumbai and Maharashtra, India. The results demonstrate close alignment with actual statistics, with 262,452 recoveries and 10,165 deaths predicted in Mumbai, compared to the official figures of 265,625 and 10,995 respectively. The model illustrates the significance of incorporating quarantine and vaccination in reducing mortality and enhancing recovery. It further enables comparative analysis of intervention strategies and supports informed public health planning through simulation-based insights.</p>

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SEIQVR: a network-based model for analyzing the spread of infectious diseases

  • 期刊论文

  • 发表日期：

    2025年11月20日

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  • Tapan Chowdhury,
  • Soham Patra,
  • Shubhadeep Saha,
  • Aditi Das,
  • Shayambhavi Bakshi,
  • Mrinal Kanti Nath

摘要

The study of infectious disease transmission involves understanding its progression within populations and the nature of interactions between individuals. This paper presents a network-based stochastic model, Susceptible, Exposed, Infected, Quarantined, Vaccinated, Recovered (SEIQVR), which extends the classical SEIR framework by incorporating quarantine and vaccination stages. These additional compartments aim to reflect real-world interventions such as isolation and immunization, often overlooked in earlier models. By integrating population density, human mobility, and healthcare infrastructure into the contact network, the model simulates the dynamics of disease spread with greater accuracy. The SEIQVR model has been validated using real-world COVID-19 data from Mumbai and Maharashtra, India. The results demonstrate close alignment with actual statistics, with 262,452 recoveries and 10,165 deaths predicted in Mumbai, compared to the official figures of 265,625 and 10,995 respectively. The model illustrates the significance of incorporating quarantine and vaccination in reducing mortality and enhancing recovery. It further enables comparative analysis of intervention strategies and supports informed public health planning through simulation-based insights.