Before revision:  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:  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 and the 16 in total explanatory covariates was analyzed with machine learning. Web search in time was compared with the corresponding epidemiological situation, expressed by the Rt at the same week . 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\%).