Ranking the explanatory power of factors associated with worldwide new Covid-19 cases
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Disease spread is a complex phenomenon requiring an interdisciplinary approach. Covid-19 exhibited a global spatial spread in a very short time frame resulting in a global pandemic. 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 (15.2 extreme poverty explained 8.8 the population aged 65 or older explained 3.9 (cardiovascular diseases plus diabetes) explained 2.9 smokers within the population explained 2.6 population density explained 0.6
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