Epidemic Trend Analysis of SARS-CoV-2 in SAARC Countries Using Modified
SIR (M-SIR) Predictive Model
Abstract
A novel coronavirus causing the severe and fatal respiratory syndrome
was identified in China, is now producing outbreaks in more than two
hundred countries around the world, and became pandemic by the time. In
this article, a modified version of the well-known mathematical epidemic
model Susceptible (S)- Infected (I)- Recovered (R) is used to analyze
the epidemic’s course of COVID-19 in eight different countries of the
South Asian Association for Regional Cooperation (SAARC). To achieve
this goal, the parameters of the SIR model are identified by using
publicly available data for the corresponding countries: Afghanistan,
Bangladesh, Bhutan, India, the Maldives, Nepal, Pakistan, and Sri Lanka.
Based on the prediction model we estimated the epidemic trend of
COVID-19 outbreak in SAARC countries for 20 days, 90 days, and 180 days
respectively. An SML (short-mid-long) term prediction model has been
designed to understand the early dynamics of the COVID-19 Epidemic in
the southeast Asian region. The maximum and minimum basic reproduction
numbers (R0 = 1.33 and 1.07) for SAARC countries are predicted to be in
Pakistan and Bhutan. We equate simulation results with real data in the
SAARC countries on the COVID-19 outbreak, and model potential
countermeasure implementation scenarios. Our results should provide
policymakers with a method for evaluating the impacts of possible
interventions, including lockdown and social distancing, as well as
testing and contact tracking.