id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-247554-535cpe5x	Moustakas, Aristides	Ranking the explanatory power of factors associated with worldwide new Covid-19 cases	2020-05-29		.txt	text/plain	3584	169	46	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. To that end methods that can account for both spatial and temporal autocorrelation [17] in the data of new Covid-19 cases but can quantify the effect of each epidemiological, economic, public health, and governmental intervention are key to our understanding of how the disease spreads in populations worldwide [18, 19] . Hierarchical Variance Partitioning (HVP) statistical modelling was implemented to account for the contribution of each data driven epidemiological, economic, public health, and governmental intervention explanatory variable to the total variance of new Covid-19 per million cases [29, 30] . Results from variance partitioning of the data-driven selected 9 epidemiological, public health, economic, and governmental intervention variables explaining Covid-19 new cases per million across countries through time, indicated that the vast majority of new cases per million are explained by the number of tests conducted.	./cache/cord-247554-535cpe5x.txt	./txt/cord-247554-535cpe5x.txt