  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.
