Optimizing quarantine in pandemic control: a multi-stage SEIQR modeling approach to COVID-19 transmission dynamics.

This study develops and applies an advanced SEIQR (Susceptible-Exposed-Infectious-Quarantined-Removed) model to explore the intricate dynamics of COVID-19 transmission. By incorporating a quarantined compartment into traditional epidemiological frameworks, the model offers a comprehensive examination of how isolation protocols affect pandemic progression. Key parameters such as infection rates, incubation periods, and quarantine durations are systematically analyzed to quantify their influence on the basic reproduction number (ℛ₀) and pandemic trajectory. Simulations reveal that timely and stringent quarantine interventions can reduce peak caseloads by up to 30%, delaying outbreak surges and alleviating pressure on healthcare systems. The model’s robustness is validated against empirical data, confirming its suitability as a predictive and policy-supporting tool. This research not only emphasizes the vital role of quarantine in public health management but also sets a foundational precedent for modeling future outbreaks with similar transmission profiles.