Inferring change points in the spread of COVID-19 reveals the effectiveness of interventions
Max Planck Institute for Dynamics and Self-Organization · University of Göttingen · +1 more institution
Abstract
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…
Citation impact
- FWCI
- 28.48
- Percentile
- 100%
- References
- 68
Authors
7- JDJonas DehningCorresponding
Max Planck Institute for Dynamics and Self-Organization
- JZJohannes ZierenbergCorresponding
Max Planck Institute for Dynamics and Self-Organization
- FPF. Paul SpitznerCorresponding
Max Planck Institute for Dynamics and Self-Organization
- MWMichael Wibral
University of Göttingen
- JPJoão Pinheiro Neto
Max Planck Institute for Dynamics and Self-Organization
Topics & keywords
- Coronavirus disease 2019 (COVID-19)
- 2019-20 coronavirus outbreak
- Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)
- Psychological intervention
- Psychology
- Medicine
- Virology
- Outbreak
- Good health and well-being