How to Best Predict the Daily Number of New Infections of COVID-19

17 Pages Posted: 15 Apr 2020

See all articles by Bernd Skiera

Bernd Skiera

Goethe University Frankfurt

Lukas Jürgensmeier

Goethe University Frankfurt

Kevin Stowe

Darmstadt University of Technology

Iryna Gurevych

Darmstadt University of Technology

Date Written: April 5, 2020

Abstract

Knowledge about the daily number of new infections of COVID-19 is important because it is the basis for political decisions resulting in lockdowns and urgent health care measures. We use Germany as an example to illustrate shortcomings of official numbers, which are, at least in Germany, disclosed only with several days of delay and severely underreported on weekends (more than 40%). These shortcomings outline an urgent need for alternative data sources. The other widely cited source provided by the Center for Systems Science and Engineering at Johns Hopkins University (JHU) also deviates for Germany on average by 79% from the official numbers. We argue that Google Search and Twitter data should complement official numbers. They predict even better than the original values from Johns Hopkins University and do so several days ahead. These two data sources could also be used in parts of the world where official numbers do not exist or are perceived to be unreliable.

Keywords: Corona, COVID-19, Prediction, Google search, Twitter, Robert Koch Institute, Johns Hopkins University

Suggested Citation

Skiera, Bernd and Jürgensmeier, Lukas and Stowe, Kevin and Gurevych, Iryna, How to Best Predict the Daily Number of New Infections of COVID-19 (April 5, 2020). Available at SSRN: https://ssrn.com/abstract=3571252 or http://dx.doi.org/10.2139/ssrn.3571252

Bernd Skiera (Contact Author)

Goethe University Frankfurt ( email )

Theodor-W.-Adorno-Platz 4
Frankfurt, 60323
Germany
+49 69 798 34640 (Phone)
+49 69 798 35001 (Fax)

HOME PAGE: http://www.skiera.de

Lukas Jürgensmeier

Goethe University Frankfurt ( email )

Grüneburgplatz 1
Frankfurt am Main, 60323
Germany

Kevin Stowe

Darmstadt University of Technology ( email )

Universitaets- und Landesbibliothek Darmstadt
Magdalenenstrasse 8
Darmstadt, Hesse D-64289
Germany

Iryna Gurevych

Darmstadt University of Technology ( email )

Universitaets- und Landesbibliothek Darmstadt
Magdalenenstrasse 8
Darmstadt, Hesse D-64289
Germany

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