Determining significant factors associated with daily COVID-19 cases within three social distancing regimes

Abstract

OBJECTIVE: The COVID-19 pandemic put enormous socio-economic pressures on most countries all over the world. In order to contain the spread of the coronavirus, governments implemented both pharmaceutical and non-pharmaceutical interventions. This simple modeling work aims to quantify the effect of three levels of social distancing and large-scale testing on daily COVID-19 cases in Malaysia, Republic of Korea, and Japan. METHOD: The model uses a Stepwise Multiple Regression (SWMR) method for selecting lagged mobility index and testing correlated with daily cases based on a 0.05 level of significance. RESULT: The models's predictability ranges are from 75% to 92%. It is also found that the mobility index plays a more important role, in comparison to testing rates, in determining daily confirmed cases. CONCLUSION: Behavioral changes that support physical distancing measures should be practiced to slow down the COVID-19 spreads.

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