Methods
Linear regression models were calculated to assess and quantify the effects of general testing activity and public mobility on the number of confirmed cases per day. Pearson correlation coefficients were calculated to further explore the type of correlation between mobility and number of confirmed cases.
To visualize a potential impact of major political decisions on the trend in newly confirmed cases, residuals were obtained from the linear regression model including background test activity. The residuals were submitted to time series decomposition to examine seasonal variation per calendar week and the remaining error variation. The trend curve, defined as moving average over seven days, was used to identify trend changes in plausible temporal relationship to tightening or loosening of governmental restrictions.