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Research
N.W.A.N.Y. Wijesekara
Ministry of Health
Corresponding Author
ORCiD: https://orcid.org/0000-0002-0391-6220
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Introduction: Infectious diseases such as coronavirus disease 2019 (COVID-19) can spread contagiously fast in semi-confined places, which demand prompt public health interventions such as isolation and quarantine for their effective control. An outbreak of COVID-19 was reported within a cluster of Navy personnel in the Western Province of Sri Lanka commencing from 22nd April 2020. In response, an aggressive outbreak management program was launched by the Epidemiology Unit of the Ministry of Health supported by the Sri Lanka Navy. The objective of this research was to predict possible number of cases within the susceptible population in Sri Lanka Navy.
Methods: COVID-19 Hospital Impact Model for Epidemics (CHIME) developed by Predictive Health Care Team at Penn Medicine, Philadelphia, USA, which was a Susceptibility, Infected and Removed (SIR) model was used. The model was run on 20.05.2020 for a susceptible population of 10400, with number of hospitalized patients on the day of running the model being 357, first case hospitalized on 22.04.2020 and social distancing being implemented on 26.04.2020. Social distancing scenarios of 0, 25, 50 and 74% were run with 10 days of infectious period and 30 days of projection period.
Results: With increasing social distancing measures, the peak number of infected persons decreased, and the duration of the curve extended. With increasing social distancing from 0% to 74%, the date on which the peak number of infected cases was reported increased from 49th day to the 54th day, the doubling time increased from 3.1 days to 4.1 days, the Ro decreased from 3.54 to 2.83, and expected daily growth rate decreased from 25.38% to 18.53%. The number of COVID-19 cases prevented as per the model ranged from 2.3 – 21.1 %, compared to the base line prediction of no social distancing. When comparing the observed number of cases with the baseline model with no social distancing, a 90.3% reduction was observed.
Conclusion: The research demonstrated the practical use of a prediction model made readily available through an online open-source platform for the operational aspects of controlling outbreaks such as COVID-19 in a closed community. Predictive modelling is a useful tool for outbreak management.
Keywords
COVID-19, SIR Model, Navy Cluster, Outbreak Management
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CURRENT STATUS: Under Review
Version 2
Posted 30 Dec, 2020
No community comments so far
Editorial decision: Major revision
On 07 Feb, 2021
Reviewer #2 agreed
On 01 Feb, 2021
Review #2 received
Received 01 Feb, 2021
Review #1 received
Received 07 Jan, 2021
Reviewer #1 agreed
On 03 Jan, 2021
Reviewers invited
Invitations sent on 02 Jan, 2021
Editor assigned
On 05 Dec, 2020
Submission checks complete
On 05 Dec, 2020
Editor invited
On 05 Dec, 2020
No comments provided
Reviewer #2 agreed
On 08 Nov, 2020
Review #2 received
Received 08 Nov, 2020
Editorial decision: Minor revision
On 08 Nov, 2020
Review #1 received
Received 26 Sep, 2020
Reviewer #1 agreed
On 12 Sep, 2020
Reviewers invited
Invitations sent on 08 Sep, 2020
Editor assigned
On 04 Sep, 2020
Submission checks complete
On 03 Sep, 2020
Editor invited
On 03 Sep, 2020
First submitted
On 02 Sep, 2020
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
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A new preprint design is coming!
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This preprint is under consideration at Military Medical Research. A preprint is a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research
N.W.A.N.Y. Wijesekara
Ministry of Health
Corresponding Author
ORCiD: https://orcid.org/0000-0002-0391-6220
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Under Review
Version 2
Posted 30 Dec, 2020
No community comments so far
Editorial decision: Major revision
On 07 Feb, 2021
Reviewer #2 agreed
On 01 Feb, 2021
Review #2 received
Received 01 Feb, 2021
Review #1 received
Received 07 Jan, 2021
Reviewer #1 agreed
On 03 Jan, 2021
Reviewers invited
Invitations sent on 02 Jan, 2021
Editor assigned
On 05 Dec, 2020
Submission checks complete
On 05 Dec, 2020
Editor invited
On 05 Dec, 2020
No comments provided
Reviewer #2 agreed
On 08 Nov, 2020
Review #2 received
Received 08 Nov, 2020
Editorial decision: Minor revision
On 08 Nov, 2020
Review #1 received
Received 26 Sep, 2020
Reviewer #1 agreed
On 12 Sep, 2020
Reviewers invited
Invitations sent on 08 Sep, 2020
Editor assigned
On 04 Sep, 2020
Submission checks complete
On 03 Sep, 2020
Editor invited
On 03 Sep, 2020
First submitted
On 02 Sep, 2020
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Introduction: Infectious diseases such as coronavirus disease 2019 (COVID-19) can spread contagiously fast in semi-confined places, which demand prompt public health interventions such as isolation and quarantine for their effective control. An outbreak of COVID-19 was reported within a cluster of Navy personnel in the Western Province of Sri Lanka commencing from 22nd April 2020. In response, an aggressive outbreak management program was launched by the Epidemiology Unit of the Ministry of Health supported by the Sri Lanka Navy. The objective of this research was to predict possible number of cases within the susceptible population in Sri Lanka Navy.
Methods: COVID-19 Hospital Impact Model for Epidemics (CHIME) developed by Predictive Health Care Team at Penn Medicine, Philadelphia, USA, which was a Susceptibility, Infected and Removed (SIR) model was used. The model was run on 20.05.2020 for a susceptible population of 10400, with number of hospitalized patients on the day of running the model being 357, first case hospitalized on 22.04.2020 and social distancing being implemented on 26.04.2020. Social distancing scenarios of 0, 25, 50 and 74% were run with 10 days of infectious period and 30 days of projection period.
Results: With increasing social distancing measures, the peak number of infected persons decreased, and the duration of the curve extended. With increasing social distancing from 0% to 74%, the date on which the peak number of infected cases was reported increased from 49th day to the 54th day, the doubling time increased from 3.1 days to 4.1 days, the Ro decreased from 3.54 to 2.83, and expected daily growth rate decreased from 25.38% to 18.53%. The number of COVID-19 cases prevented as per the model ranged from 2.3 – 21.1 %, compared to the base line prediction of no social distancing. When comparing the observed number of cases with the baseline model with no social distancing, a 90.3% reduction was observed.
Conclusion: The research demonstrated the practical use of a prediction model made readily available through an online open-source platform for the operational aspects of controlling outbreaks such as COVID-19 in a closed community. Predictive modelling is a useful tool for outbreak management.
Figure 1
Figure 2
Figure 3
Figure 4
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