Authors: Zeng Fung Liew, Yixing Lu, Liela Meng, Apurv Srivastav
Coronavirus disease 2019 is a contagious disease that has led to one of the biggest pandemics across the world. There has been 115 million cases of Covid-19 with 2.5 million deaths so far. One of the responses by countries has been to issue a stay-at-home mandate which has levels that range from no restrictions to a mandatory curfew. In this study, the stay-at-home mandate with apporiate lag is used to estimate the growth rate of infectious population. The exploratory data analysis showed that the Daily Percent change in infectious population. From the result, it is seen that each of the five countries had an increasing percent change at the beginning of the time interval (October 1st), but over time the daily percent change turned negative. This distribution was the same for all five countries and possibly indicates a negative association between stay-at-home mandate and infectious population. In the inferential analysis, it shown using multiple tests that Fixed effects model was better than random effects and ordinary least squares. We then use propensity scores to test whether the significant variables in the fixed effect model can cause the growth rate of infectious population to change.