Internet search effort on Covid-19 and the underlying public interventions and epidemiological status
Aristides Moustakas

TL;DR
This study analyzes how web search effort related to Covid-19 in Greece correlates with epidemiological data and government interventions, revealing strong predictive relationships and insights into public interest during the pandemic.
Contribution
It introduces a machine learning model that predicts web search effort based on epidemiological and intervention variables, highlighting key factors influencing public search behavior.
Findings
Model achieved 91% fit between actual and predicted search effort.
Top predictors included new deaths, border openings, and testing policies.
Web search peaks aligned with Rt peaks, especially during tourist season.
Abstract
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…
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Taxonomy
TopicsCOVID-19 epidemiological studies · Data-Driven Disease Surveillance · Vaccine Coverage and Hesitancy
