Tracking the early depleting transmission dynamics of COVID-19 with a time-varying SIR model

• Main Authors: Kian Boon Law, Kalaiarasu M. Peariasamy, Balvinder Singh Gill, Sarbhan Singh, Bala Murali Sundram, Kamesh Rajendran, Sarat Chandra Dass, Yi Lin Lee, Pik Pin Goh, Hishamshah Ibrahim, Noor Hisham Abdullah
• Format: Article
• Language: English
• Published: Nature Publishing Group 2020-12-01
• Series: Scientific Reports
• Online Access: https://doi.org/10.1038/s41598-020-78739-8

Abstract The susceptible-infectious-removed (SIR) model offers the simplest framework to study transmission dynamics of COVID-19, however, it does not factor in its early depleting trend observed during a lockdown. We modified the SIR model to specifically simulate the early depleting transmission dynamics of COVID-19 to better predict its temporal trend in Malaysia. The classical SIR model was fitted to observed total (I total), active (I) and removed (R) cases of COVID-19 before lockdown to estimate the basic reproduction number. Next, the model was modified with a partial time-varying force of infection, given by a proportionally depleting transmission coefficient, $$\beta_{t}$$ β t and a fractional term, z. The modified SIR model was then fitted to observed data over 6 weeks during the lockdown. Model fitting and projection were validated using the mean absolute percent error (MAPE). The transmission dynamics of COVID-19 was interrupted immediately by the lockdown. The modified SIR model projected the depleting temporal trends with lowest MAPE for I total, followed by I, I daily and R. During lockdown, the dynamics of COVID-19 depleted at a rate of 4.7% each day with a decreased capacity of 40%. For 7-day and 14-day projections, the modified SIR model accurately predicted I total, I and R. The depleting transmission dynamics for COVID-19 during lockdown can be accurately captured by time-varying SIR model. Projection generated based on observed data is useful for future planning and control of COVID-19.