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Inferring change points in the spread of COVID-19 reveals the effectiveness of interventions

MPG-Autoren
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Dehning,  Jonas
Max Planck Research Group Neural Systems Theory, Max Planck Institute for Dynamics and Self-Organization, Max Planck Society;

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Zierenberg,  Johannes
Max Planck Research Group Neural Systems Theory, Max Planck Institute for Dynamics and Self-Organization, Max Planck Society;

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Spitzner,  F. Paul
Max Planck Research Group Neural Systems Theory, Max Planck Institute for Dynamics and Self-Organization, Max Planck Society;

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Pinheiro Neto,  Joao
Max Planck Research Group Neural Systems Theory, Max Planck Institute for Dynamics and Self-Organization, Max Planck Society;

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Wilczek,  Michael
Max Planck Research Group Theory of Turbulent Flows, Max Planck Institute for Dynamics and Self-Organization, Max Planck Society;

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Priesemann,  Viola
Max Planck Research Group Neural Systems Theory, Max Planck Institute for Dynamics and Self-Organization, Max Planck Society;

Externe Ressourcen

https://www.ds.mpg.de/3579182/200515_corona_science
(Ergänzendes Material)

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Zitation

Dehning, J., Zierenberg, J., Spitzner, F. P., Wibral, M., Pinheiro Neto, J., Wilczek, M., et al. (2020). Inferring change points in the spread of COVID-19 reveals the effectiveness of interventions. Science, 369: eabb9789. doi:10.1126/science.abb9789.


Zitierlink: https://hdl.handle.net/21.11116/0000-0006-F6D1-8
Zusammenfassung
As coronavirus disease 2019 (COVID-19) is rapidly spreading across the globe, short-term modeling
forecasts provide time-critical information for decisions on containment and mitigation strategies.
A major challenge for short-term forecasts is the assessment of key epidemiological parameters
and how they change when first interventions show an effect. By combining an established epidemiological
model with Bayesian inference, we analyzed the time dependence of the effective growth rate of new
infections. Focusing on COVID-19 spread in Germany, we detected change points in the effective growth
rate that correlate well with the times of publicly announced interventions. Thereby, we could quantify the
effect of interventions and incorporate the corresponding change points into forecasts of future scenarios
and case numbers. Our code is freely available and can be readily adapted to any country or region.