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\%) followed by the governmental stringency index (15.2\%). Availability of hospital beds per 100k inhabitants explained 9\% extreme poverty explained 8.8\%, hand washing facilities 5.3\%, the fraction of the population aged 65 or older explained 3.9\%, and other disease prevalence (cardiovascular diseases plus diabetes) explained 2.9\%. The percentage of smokers within the population explained 2.6\% of the total variance, while population density explained 0.6\% .
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 web search effort in Greece on 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 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 the trained model exhibited a fit of R2 = 91\% between the actual and predicted web search effort. The top five variables for predicting web search effort were new deaths, the opening of international borders to non-Greek nationals, new cases, testing policy, and restrictions in internal movements. Web search peaked during the same weeks that the Rt was peaking although new deaths or new cases were not peaking during those dates, and Rt rarely is reported in public media. As both web search effort and Rt peaked during 1-15 August 2020, the peak of the tourist season, the implications of this are discussed .
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new
web search effort in Greece on
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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
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
R
cases per million
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
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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\%). Availability of hospital beds per 100k inhabitants explained 9\% extreme poverty explained 8.8\%, hand washing facilities 5.3\%,
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fraction of the population aged 65 or older explained 3.9\%, and other disease prevalence (cardiovascular diseases plus diabetes) explained 2.9\%. The percentage of smokers within the population explained 2.6\% of the total variance, while population density explained 0.6\%
trained model exhibited a fit of R2 = 91\% between the actual and predicted web search effort. The top five variables for predicting web search effort were new deaths, the opening of international borders to non-Greek nationals, new cases, testing policy, and restrictions in internal movements. Web search peaked during the same weeks that the Rt was peaking although new deaths or new cases were not peaking during those dates, and Rt rarely is reported in public media. As both web search effort and Rt peaked during 1-15 August 2020, the peak of the tourist season, the implications of this are discussed