Day Level Forecasting for COVID-19 Pandemic Spread in SAARC Countries

Abstract

The COVID-19 is declared a global epidemic by the World Health Organization (WHO).  Till now there is no effective medicine and vaccine is not available to treat COVID-19 and the genetic mutation capability of COVID-19 is very aggressive. Day-level knowledge of COVID-19 spread distribution is essential to measure the nature of the virus. The aim of this paper is two folds. Firstly, we compare the forecasting performance of three models namely Simple Exponential Smoothing (SES), Moving Average (MA) and Weighted Moving Average (WMA) for South Asian Association for Regional Cooperation (SAARC) countries. Secondly, the best forecasting model is used to predict the day level number of infected case for SAARC countries. In this study we consider the daily infected case from March 08, 2020 to May 18, 2020. The empirical analysis showed that the SES model give beter forcasting performance for all of the SAARC countries based on graphical comparison as well as Mean Absolute Percentage Eorror (MAPE), Mean Absolute Deviation (MAD), Mean Square Error (MSE). The one month aheah forecast result of infected case for Bangladesh, India, Pakistan, Bhutan, Nepal, Maldives and Afghanistan will be 71702, 246726, 90926, 26, 1339, 1490, 2152 and 17934 respectively. Besides these we also shows the growth rate of the infected case based on Exponential and logarithmic scale. Having access to skilful daily updated forecast value of infected case could help better informed decision on how to manage the spread of COVID-19.

International Journal of Statistical Sciences, Vol. 23(2), November, 2023, pp 129-142