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Shedding light on dark figures: Steps towards a methodology for estimating actual numbers of COVID-19 infections in Germany based on Google Trends

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  • Christina H Maaß

Abstract

In order to shed light on unmeasurable real-world phenomena, we investigate exemplarily the actual number of COVID-19 infections in Germany based on big data. The true occurrence of infections is not visible, since not every infected person is tested. This paper demonstrates that coronavirus-related search queries issued on Google can depict true infection levels appropriately. We find significant correlation between search volume and national as well as federal COVID-19 cases as reported by RKI. Additionally, we discover indications that the queries are indeed causal for infection levels. Finally, this approach can replicate varying dark figures throughout different periods of the pandemic and enables early insights into the true spread of future virus outbreaks. This is of high relevance for society in order to assess and understand the current situation during virus outbreaks and for decision-makers to take adequate and justifiable health measures.

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  • Christina H Maaß, 2022. "Shedding light on dark figures: Steps towards a methodology for estimating actual numbers of COVID-19 infections in Germany based on Google Trends," PLOS ONE, Public Library of Science, vol. 17(10), pages 1-17, October.
    • Handle: RePEc:plo:pone00:0276485
    DOI: 10.1371/journal.pone.0276485
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    2. Hal R. Varian, 2014. "Big Data: New Tricks for Econometrics," Journal of Economic Perspectives, American Economic Association, vol. 28(2), pages 3-28, Spring.
    3. Hendrik Streeck & Bianca Schulte & Beate M. Kümmerer & Enrico Richter & Tobias Höller & Christine Fuhrmann & Eva Bartok & Ramona Dolscheid-Pommerich & Moritz Berger & Lukas Wessendorf & Monika Eschbac, 2020. "Infection fatality rate of SARS-CoV2 in a super-spreading event in Germany," Nature Communications, Nature, vol. 11(1), pages 1-12, December.
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