Optimizing quarantine in pandemic control: a multi-stage SEIQR modeling approach to COVID-19 transmission dynamics
Abstract 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 e...
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| Format: | Article |
| Language: | English |
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BMC
2025-07-01
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| Series: | BMC Infectious Diseases |
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| Online Access: | https://doi.org/10.1186/s12879-025-11253-2 |
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| author | Nawal H. Siddig Laila A. Al-Essa |
| author_facet | Nawal H. Siddig Laila A. Al-Essa |
| author_sort | Nawal H. Siddig |
| collection | DOAJ |
| description | Abstract 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. |
| format | Article |
| id | doaj-art-b9ec9569b9d44861a01b7c2585934a61 |
| institution | Kabale University |
| issn | 1471-2334 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Infectious Diseases |
| spelling | doaj-art-b9ec9569b9d44861a01b7c2585934a612025-08-20T04:01:24ZengBMCBMC Infectious Diseases1471-23342025-07-0125111210.1186/s12879-025-11253-2Optimizing quarantine in pandemic control: a multi-stage SEIQR modeling approach to COVID-19 transmission dynamicsNawal H. Siddig0Laila A. Al-Essa1Department of Mathematical Sciences, College of Science, Princess Nourah bint Abdulrahman UniversityDepartment of Mathematical Sciences, College of Science, Princess Nourah bint Abdulrahman UniversityAbstract 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. https://doi.org/10.1186/s12879-025-11253-2COVID-19SEIQR modelQuarantine dynamicsReproduction numberPandemic modeling |
| spellingShingle | Nawal H. Siddig Laila A. Al-Essa Optimizing quarantine in pandemic control: a multi-stage SEIQR modeling approach to COVID-19 transmission dynamics BMC Infectious Diseases COVID-19 SEIQR model Quarantine dynamics Reproduction number Pandemic modeling |
| title | Optimizing quarantine in pandemic control: a multi-stage SEIQR modeling approach to COVID-19 transmission dynamics |
| title_full | Optimizing quarantine in pandemic control: a multi-stage SEIQR modeling approach to COVID-19 transmission dynamics |
| title_fullStr | Optimizing quarantine in pandemic control: a multi-stage SEIQR modeling approach to COVID-19 transmission dynamics |
| title_full_unstemmed | Optimizing quarantine in pandemic control: a multi-stage SEIQR modeling approach to COVID-19 transmission dynamics |
| title_short | Optimizing quarantine in pandemic control: a multi-stage SEIQR modeling approach to COVID-19 transmission dynamics |
| title_sort | optimizing quarantine in pandemic control a multi stage seiqr modeling approach to covid 19 transmission dynamics |
| topic | COVID-19 SEIQR model Quarantine dynamics Reproduction number Pandemic modeling |
| url | https://doi.org/10.1186/s12879-025-11253-2 |
| work_keys_str_mv | AT nawalhsiddig optimizingquarantineinpandemiccontrolamultistageseiqrmodelingapproachtocovid19transmissiondynamics AT lailaaalessa optimizingquarantineinpandemiccontrolamultistageseiqrmodelingapproachtocovid19transmissiondynamics |