Challenges in Interpreting Epidemiological Surveillance Data – Experiences From Germany

MCML Authors

Cornelius Fritz

Dr.

* Former Member

→ Group Göran Kauermann
 Applied Statistics in Social Sciences, Economics and Business

David Rügamer

Prof. Dr.

Principal Investigator

Statistics, Data Science and Machine Learning

Andreas Bender

Dr.

→ Group Bernd Bischl
 Statistical Learning and Data Science

Maximilian Weigert

* Former Member

→ Group Helmut Küchenhoff
 Statistical Consulting Unit (StaBLab)

Helmut Küchenhoff

Prof. Dr.

Principal Investigator

Statistical Consulting Unit (StaBLab)

Göran Kauermann

Prof. Dr.

Principal Investigator

Applied Statistics in Social Sciences, Economics and Business

Abstract

As early as March 2020, the authors of this letter started to work on surveillance data to obtain a clearer picture of the pandemic’s dynamic. This letter outlines the lessons learned during this peculiar time, emphasizing the benefits that better data collection, management, and communication processes would bring to the table. We further want to promote nuanced data analyses as a vital element of general political discussion as opposed to drawing conclusions from raw data, which are often flawed in epidemiological surveillance data, and therefore underline the overall need for statistics to play a more central role in public discourse.

article

Journal of Computational and Graphical Statistics

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Research Areas

 A1 | Statistical Foundations & Explainability

 C4 | Computational Social Sciences