Modeling SARS-CoV-2 spread with dynamic isolation
Background: The SARS-CoV-2 pandemic is spreading with a greater intensity across the globe. The synchrony of public health interventions and epidemic waves signify the importance of evaluation of the underline interventions. Method: We developed a mathematical model to present the transmission dy...
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| Format: | Article |
| Language: | English |
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Western Libraries
2021-10-01
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| Series: | Mathematics in Applied Sciences and Engineering |
| Subjects: | |
| Online Access: | https://ojs.lib.uwo.ca/index.php/mase/article/view/13886 |
| _version_ | 1828341849827311616 |
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| author | Md. Azmir Ibne Islam Sharmin Sultana Shanta Ashrafur Rahman |
| author_facet | Md. Azmir Ibne Islam Sharmin Sultana Shanta Ashrafur Rahman |
| author_sort | Md. Azmir Ibne Islam |
| collection | DOAJ |
| description | Background: The SARS-CoV-2 pandemic is spreading with a greater intensity across the globe. The synchrony of public health interventions and epidemic waves signify the importance of evaluation of the underline interventions.
Method: We developed a mathematical model to present the transmission dynamics of SARS-CoV-2 and to analyze the impact of key nonpharmaceutical interventions such as isolation and screening program on the disease outcomes to the people of New Jersey, USA. We introduced a dynamic isolation of susceptible population with a constant (imposed) and infection oriented interventions. Epidemiological and demographic data are used to estimate the model parameters. The baseline case was explored further to showcase several critical and predictive scenarios.
Results and analysis: The model simulations are in good agreement with the infection data for the period of 5 March 2020 to 31 January 2021. Dynamic isolation and screening program are found to be potential measures that can alter the course of epidemic. A 7% increase in isolation rate may result in a 31% reduction of epidemic peak whereas a 3 times increase in screening rate may reduce the epidemic peak by 35%. The model predicts that nearly 9.7% to 12% of the total population of New Jersey may become infected within the middle of July 2021 along with 24.6 to 27.3 thousand cumulative deaths. Within a wide spectrum of probable scenarios, there is a possibility of third wave
Conclusion: Our findings could be informative to the public health community to contain the pandemic in the case of economy reopening under a limited or no vaccine coverage. Additional epidemic waves can be avoided by appropriate screening and isolation plans. |
| first_indexed | 2024-04-13T23:20:22Z |
| format | Article |
| id | doaj.art-214b7ea91aae449cb1a242cc5fb3d64c |
| institution | Directory Open Access Journal |
| issn | 2563-1926 |
| language | English |
| last_indexed | 2024-04-13T23:20:22Z |
| publishDate | 2021-10-01 |
| publisher | Western Libraries |
| record_format | Article |
| series | Mathematics in Applied Sciences and Engineering |
| spelling | doaj.art-214b7ea91aae449cb1a242cc5fb3d64c2022-12-22T02:25:15ZengWestern LibrariesMathematics in Applied Sciences and Engineering2563-19262021-10-012421923410.5206/mase/138867790Modeling SARS-CoV-2 spread with dynamic isolationMd. Azmir Ibne IslamSharmin Sultana ShantaAshrafur RahmanBackground: The SARS-CoV-2 pandemic is spreading with a greater intensity across the globe. The synchrony of public health interventions and epidemic waves signify the importance of evaluation of the underline interventions. Method: We developed a mathematical model to present the transmission dynamics of SARS-CoV-2 and to analyze the impact of key nonpharmaceutical interventions such as isolation and screening program on the disease outcomes to the people of New Jersey, USA. We introduced a dynamic isolation of susceptible population with a constant (imposed) and infection oriented interventions. Epidemiological and demographic data are used to estimate the model parameters. The baseline case was explored further to showcase several critical and predictive scenarios. Results and analysis: The model simulations are in good agreement with the infection data for the period of 5 March 2020 to 31 January 2021. Dynamic isolation and screening program are found to be potential measures that can alter the course of epidemic. A 7% increase in isolation rate may result in a 31% reduction of epidemic peak whereas a 3 times increase in screening rate may reduce the epidemic peak by 35%. The model predicts that nearly 9.7% to 12% of the total population of New Jersey may become infected within the middle of July 2021 along with 24.6 to 27.3 thousand cumulative deaths. Within a wide spectrum of probable scenarios, there is a possibility of third wave Conclusion: Our findings could be informative to the public health community to contain the pandemic in the case of economy reopening under a limited or no vaccine coverage. Additional epidemic waves can be avoided by appropriate screening and isolation plans.https://ojs.lib.uwo.ca/index.php/mase/article/view/13886sars-cov-2,screening,dynamic isolation,basic reproduction number,economy reopening,final size. |
| spellingShingle | Md. Azmir Ibne Islam Sharmin Sultana Shanta Ashrafur Rahman Modeling SARS-CoV-2 spread with dynamic isolation Mathematics in Applied Sciences and Engineering sars-cov-2, screening, dynamic isolation, basic reproduction number, economy reopening, final size. |
| title | Modeling SARS-CoV-2 spread with dynamic isolation |
| title_full | Modeling SARS-CoV-2 spread with dynamic isolation |
| title_fullStr | Modeling SARS-CoV-2 spread with dynamic isolation |
| title_full_unstemmed | Modeling SARS-CoV-2 spread with dynamic isolation |
| title_short | Modeling SARS-CoV-2 spread with dynamic isolation |
| title_sort | modeling sars cov 2 spread with dynamic isolation |
| topic | sars-cov-2, screening, dynamic isolation, basic reproduction number, economy reopening, final size. |
| url | https://ojs.lib.uwo.ca/index.php/mase/article/view/13886 |
| work_keys_str_mv | AT mdazmiribneislam modelingsarscov2spreadwithdynamicisolation AT sharminsultanashanta modelingsarscov2spreadwithdynamicisolation AT ashrafurrahman modelingsarscov2spreadwithdynamicisolation |