An Extended Susceptible-Exposed-Infected-Recovered (SEIR) Model with Vaccination for Forecasting the COVID-19 Pandemic in Sri Lanka

Rajapaksha, R. M. N. U.; Wijesinghe, M. S. D.; Jayasooriya, S. P.; Gunawardana, B. M. I.; Weerasinghe, W. M. P. C.

The role of modelling in predicting the spread of an epidemic is important for health planning and policies. This study aims to apply a compartmental model for predicting the variations of epidemiological parameters in Sri Lanka. We used a dynamic Susceptible-Exposed-Infected-Recovered-Vaccinated (SEIRV) model, and simulated for potential vaccine strategies under a range of epidemic conditions. The predictions were based on different vaccination coverages (5% to 90%), vaccination-rates (1%, 2%, 5%) and vaccine-efficacies (40%, 60%, 80%) under different R0 (2,4,6). We observed how the above dynamics influenced the SEIRV model without COVID-19 vaccination at different R0 values, and estimated the duration, exposed and infected populations. When the R0 was increased, the days of reduction of susceptibility and the days to reach the peak of the infection were reduced gradually. At least 45% vaccine coverage is required for reducing the infected population as early as possible. The results revealed that when R0 is increased in the SEIRV model along with the increase of vaccination efficacy and vaccination rate, the population to be vaccinated is reducing. Thus, the vaccination offers greater benefits to the local population by reducing the time to reach the peak, exposed and infected population through flattening the curves.