Predictive modelling for COVID-19 outbreak control: lessons from the navy cluster in Sri Lanka

N. W. A. N. Y. Wijesekara; Nayomi Herath; K. A. L. C. Kodituwakku; H. D. B. Herath; Samitha Ginige; Thilanga Ruwanpathirana; Manjula Kariyawasam; Sudath Samaraweera; Anuruddha Herath; Senarupa Jayawardena; Deepa Gamge

doi:10.1186/s40779-021-00325-4

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Predictive modelling for COVID-19 outbreak control: lessons from the navy cluster in Sri Lanka

LETTER TO THE EDITOR | Updated：2023-02-28

- Predictive modelling for COVID-19 outbreak control: lessons from the navy cluster in Sri Lanka
- Predictive modelling for COVID-19 outbreak control: lessons from the navy cluster in Sri Lanka
- 解放军医学杂志（英文版） 2022年9卷第1期页码：138-140
- Affiliations：
  
  1.Disaster Preparedness and Response Division, Ministry of Health, 385, Rev. Baddegama Wimalawansa Thero Mawatha, Colombo 01000, Sri Lanka
  2.Epidemiology Unit of the Ministry of Health, Colombo 01000, Sri Lanka
  3.Sri Lanka Navy, Colombo 01100, Sri Lanka
- Author bio：
  
  * novil.wijesekara@gmail.com
- Funds：
- DOI：10.1186/s40779-021-00325-4
  中图分类号：
- 纸质出版：2022-02
- Accepted：
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Predictive modelling for COVID-19 outbreak control: lessons from the navy cluster in Sri Lanka[J]. 解放军医学杂志（英文版）, 2022,9(1):138-140.

Wijesekara et al.: Predictive modelling for COVID-19 outbreak control: lessons from the navy cluster in Sri Lanka. Mil Med Res, 2021, 8: 31.
Predictive modelling for COVID-19 outbreak control: lessons from the navy cluster in Sri Lanka[J]. 解放军医学杂志（英文版）, 2022,9(1):138-140. DOI： 10.1186/s40779-021-00325-4.

Wijesekara et al.: Predictive modelling for COVID-19 outbreak control: lessons from the navy cluster in Sri Lanka. Mil Med Res, 2021, 8: 31. DOI： 10.1186/s40779-021-00325-4.

摘要

Abstract

In response to an outbreak of coronavirus disease 2019 (COVID-19) within a cluster of Navy personnel in Sri Lanka commencing from 22nd April 2020

an aggressive outbreak management program was launched by the Epidemiology Unit of the Ministry of Health. To predict the possible number of cases within the susceptible population under four social distancing scenarios

the COVID-19 Hospital Impact Model for Epidemics (CHIME) was used. With increasing social distancing

the epidemiological curve flattened

and its peak shifted to the right. The observed or actually reported number of cases was above the projected number of cases at the onset; however

subsequently

it fell below all predicted trends. Predictive modelling is a useful tool for the control of outbreaks such as COVID-19 in a closed community.

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references

Kak V . Infections in confined spaces: cruise ships, military barracks, and college dormitories . Infect Dis Clin N Am . 2007 ; 21 ( 3 ): 773–84, ix-x .

Payne DC , Smith-Jeffcoat SE , Nowak G , Chukwuma U , Geibe JR , Hawkins RJ , et al . SARS-CoV-2 infections and serologic responses from a sample of U.S. Navy service members - USS Theodore Roosevelt, April 2020 . MMWR Morb Mortal Wkly Rep . 2020 ; 69 ( 23 ): 714 - 21 .

Rocklöv J , Sjödin H , Wilder-Smith A . COVID-19 outbreak on the Diamond Princess cruise ship: estimating the epidemic potential and effectiveness of public health countermeasures . J Travel Med . 2020 ; 27 ( 3 ): taaa030 .

Epidemiology Unit . ( 2020 ). Coronavirus disease 2019 (COVID-19)—situation Report – 24.04.2020–10.00am . http://www.epid.gov.lk/web/images/pdf/corona_virus_report/sitrep-sl-en-24-04_10.pdf http://www.epid.gov.lk/web/images/pdf/corona_virus_report/sitrep-sl-en-24-04_10.pdf

Weissman GE , Crane-Droesch A , Chivers C , Luong T , Hanish A , Levy MZ , et al . Locally informed simulation to predict hospital capacity needs during the COVID-19 pandemic . Ann Intern Med . 2020 ; 173 ( 1 ): 21 - 8 .

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