COVID-19 Case Prediction and Outbreak Control of Navy Cluster in Sri Lanka: Effectiveness of SIR Model

Abstract Introduction: Infectious diseases such as coronavirus disease 2019 (COVID-19) can spread dangerously fast in semi-confined places. Nevertheless, it has been found that rapid public health interventions such as isolation and quarantine could successfully curtail such outbreaks. 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. An epidemiological investigation followed by aggressive public health measures were implemented by the Epidemiology Unit of the Ministry of Health with the support of the Sri Lanka Navy in response to the above outbreak. The objective of this research was to predict possible number of cases within the susceptible population in Sri Lanka Navy, to be used primarily for operational planning purpose by the Ministry of Health in control of outbreak in Sri Lanka.Methods: COVID-19 Hospital Impact Model for Epidemics (CHIME) developed by Predictive Health Care Team at Penn Medicine, 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, as well as the duration of the curve extended. The number of infected cases from the first case ranged from 49th day to 54th day under social distancing scenarios from 0% to 74%. The doubling time increased from 3.1 days to 4.1 days from no social distancing to application of 74% social distancing, with corresponding decrease of Ro from 3.54 to 2.83. Expected daily growth rate of COVID-19 cases has decreased from 25.38 % to 18.53% under aforementioned increasing social distancing scenarios. The observed or actually experienced number of cases were well above the projected number of cases up to 07.05.2020, however, since this date the reported number of cases were lower than the projected number of cases from the model under four social distancing scenarios considered. Similar pattern was noted for the observed or actually experienced number of cases until the 20.05.2020, however, since then it was continuing at a very low intensity until the end of the modelling period. 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. However, based on the observed number of cases and the baseline model with no social distancing, 90.3% reduction was observed by the time of the model application date.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 a COVID-19 or similar communicable disease outbreaks in a closed community such as armed forces. While comprehensive epidemiological surveillance, contact tracing, case isolation and case management should be the cornerstone of outbreak management, predictive modelling could supplement above efforts.