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

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

Research output: Contribution to journalArticlepeer-review

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, 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.

Original languageEnglish
Article number21721
JournalScientific Reports
Volume10
DOIs
Publication statusPublished - 10 Dec 2020

Keywords

  • COVID-19/epidemiology
  • Humans
  • Malaysia/epidemiology
  • Models, Biological
  • Pandemics
  • SARS-CoV-2

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