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

Title data

Dehning, Jonas ; Zierenberg, Johannes ; Spitzner, F. Paul ; Wibral, Michael ; Pinheiro Neto, Joao ; Wilczek, Michael ; Priesemann, Viola:
Inferring change points in the spread of COVID-19 reveals the effectiveness of interventions.
In: Science. Vol. 369 (2020) Issue 6500 .
ISSN 1095-9203
DOI: https://doi.org/10.1126/science.abb9789

Abstract in another language

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.

Further data

Item Type: Article in a journal
Refereed: Yes
Institutions of the University: Faculties > Faculty of Mathematics, Physics und Computer Science > Department of Physics > Chair Theoretical Physics I > Chair Theoretical Physics I - Univ.-Prof. Dr. Michael Wilczek
Profile Fields > Advanced Fields > Nonlinear Dynamics
Result of work at the UBT: No
DDC Subjects: 500 Science > 530 Physics
Date Deposited: 18 Feb 2022 08:23
Last Modified: 18 Feb 2022 08:23
URI: https://eref.uni-bayreuth.de/id/eprint/67560