2020/04/30 by Jonas Dehning, Johannes Zierenberg, F. Paul Spitzner +5 · 27 citations
Agricultural and Biological Sciences · Biochemistry, Genetics and Molecular Biology · Economics, Econometrics and Finance · Mathematics · #Agricultural risk and resilience #COVID-19 Pandemic Impacts #COVID-19 epidemiological studies #q-bio.PE
paper · pdf · doi:10.1126/science.abb9789
published as Science 369, 160 (2020) · 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/ )
arxiv created 2020/05/04 · openalex publication_date 2020/05/15 · arxiv updated 2022/11/17 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.