Abstract
ObjectiveThe COVID-19 pandemic has disrupted people's normal life as a result of strict policies applied to slow down the pandemic. To find out how extensive the virus spread is, most countries increase their daily testing rates. MethodThis simple modelling work uses stringency index and daily testing (including the lagged version up to the previous 14 days) to predict daily COVID-19 cases in India and Indonesia. A Stepwise Multiple Regression (SWMR) subroutine is used in this modelling to select factors based on a 0.01 significant level affecting daily COVID-19 cases before the epidemic peaks. ResultThe models have high predictability close to 94% (Indonesia) and 99% (India). Increasing number of daily COVID-19 cases in Indonesia is associated with the country's increased testing capacity. On the other hand, stringency indices play more important role in determining India's daily COVID-19 cases. CloclusionOur finding shows that one question remains to be answered as to why testing and strict policy differ in determining daily cases in both Asian countries.
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