Factors affecting COVID-19 cases before epidemic peaks

Abstract

OBJECTIVE: The 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. METHOD: This 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. RESULT: The 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. CLOCLUSION: Our 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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