Inferring change points in the COVID-19 spreading reveals the effectiveness of interventions
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 main 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 the 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.
Keywords
Cite
@article{arxiv.2004.01105,
title = {Inferring change points in the COVID-19 spreading reveals the effectiveness of interventions},
author = {Jonas Dehning and Johannes Zierenberg and F. Paul Spitzner and Michael Wibral and Joao Pinheiro Neto and Michael Wilczek and Viola Priesemann},
journal= {arXiv preprint arXiv:2004.01105},
year = {2022}
}
Comments
23 pages, 11 figures. Our code is freely available and can be readily adapted to any country or region ( https://github.com/Priesemann-Group/covid19_inference_forecast/ )