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

Type Journal Article Author Jonas Dehning Author Johannes Zierenberg Author F. Paul Spitzner Author Michael Wibral Author Joao Pinheiro Neto Author Michael Wilczek Author Viola Priesemann URL https://science.sciencemag.org/content/early/2020/05/14/science.abb9789 Rights Copyright © 2020 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. This is an open-access article distributed under the terms of the Creative Commons Attribution license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Series Research Article Publication American Association for the Advancement of Science (AAAS) / Science ISSN 0036-8075, 1095-9203 Date 15/05/2020 Extra Publisher: American Association for the Advancement of Science Section: Research Article PMID: 32414780 DOI 10.1126/science.abb9789 Accessed 2020-07-05 12:11:06 Library Catalog science.sciencemag.org Language en Abstract As 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 analyze the time dependence of the effective growth rate of new infections. Focusing on COVID-19 spread in Germany, we detect change points in the effective growth rate that correlate well with the times of publicly announced interventions. Thereby, we can quantify the effect of interventions, and we can 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.