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The Role of Data in the COVID-19 Pandemic (2021)

(2021)

Data is critical to understanding the COVID-19 pandemic. When tracking the spread of SARS-CoV-2, there is a strong focus on epidemiological data such as aggregated case numbers and the reproduction rate. Modeling can help understand disease dynamics and make predictions about the future course of the pandemic, and assess policy interventions by also taking into account clinical, serological, and demographic data. In public debate, characteristics such as sensitivity and specificity of PCR and antigen tests have attracted attention, while levels of efficacy and the statistical assessment of side effects are in the center of discussions on vaccines.

This Leopoldina International Virtual Panel will explore how data can help to support the containment of the COVID-19 pandemic. By taking into account international experiences in collecting, comparing, and understanding pandemic data, the Panel will discuss benefits and limits of data in informing public health responses as well as opportunities to increase preparedness for international health emergencies in the future.

Experts:

  • Simon Cauchemez, PhD, Head of the Mathematical Modelling of Infectious Diseases Unit, Institut Pasteur, Paris, France;
  • Professor Christopher Dye, PhD, Professor of Epidemiology at the University of Oxford, former WHO Director of Strategy, Fellow of the Royal Society, United Kingdom;
  • Professor Dr Gerd Gigerenzer, Director of the Harding Center for Risk Literacy at the University of Potsdam, Director Emeritus of the Max Planck Institute for Human Development, Member of the Leopoldina, Germany;
  • Professor Alessia Melegaro, PhD, Associate Professor of Demography and Director of the Covid Crisis Lab, Bocconi University, Milan, Member of the Health Ministry’s Technical and Scientific Committee on COVID-19, Italy.

This panel discussion was moderated by German science journalist Rebecca Winkels, Director of Communications and Strategy at Wissenschaft im Dialog.

Image: Adobe Stock / denisismagilov

© German National Academy of Sciences Leopoldina 2021

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