Before revision:  Data of new Covid-19 cases per million were analysed worldwide at the spatial scale of a country and time replicated from the end of December 2019 to late May 2020. Data driven analysis of epidemiological, economic, public health, and governmental intervention variables was performed in order to select the optimal variables in explaining new Covid-19 cases across all countries in time. Sequentially, hierarchical variance partitioning of the optimal variables was performed in order to quantify the independent contribution of each variable in the total variance of new Covid-19 cases per million . Results indicated that from the variables available new tests per thousand explained the vast majority of the total variance in new cases(51.6\%) followed by the governmental stringency index (15.2\%).
After revision:  Data of new Covid-19 as a topic for one year on a weekly temporal scale were analyzed using governmental intervention measures such a s school closures, movement restrictions, national and international travelling restrictions, stay at home requirements, mask requirements, financial support measures, and epidemiological variables such as new cases and new deaths as potential explanatory covariates. The relationship between web search effort on Covid-19 cases per million . Results indicated that from the variables available new tests per thousand explained the vast majority of the total variance in new cases(51.6\%) followed by the governmental stringency index (15.2\%).