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 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.