Inferring COVID-19 spreading rates and potential change points for case number forecasts
Jonas Dehning, Johannes Zierenberg, F. Paul Spitzner, Michael Wibral, Joao Pinheiro Neto, Michael Wilczek, Viola Priesemann
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- github.com/Priesemann-Group/covid19_inference_forecastOfficialIn papernone★ 178
- github.com/Priesemann-Group/covid19_inferencenone★ 72
- github.com/anonym0305/cov19infnone★ 0
- github.com/herysedra/covid19-mankaiza-clone-none★ 0
- github.com/rccreswell/covid19_inference_forecastnone★ 0
- github.com/ichironakamoto/Japannone★ 0
- github.com/Cognizant-Covid19Solutions/ProjectionModelstf★ 0
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 as first governmental intervention measures are showing an effect. By combining an established epidemiological model with Bayesian inference, we analyze the time dependence of the effective growth rate of new infections. For the case of COVID-19 spreading in Germany, we detect change points in the effective growth rate which correlate well with the times of publicly announced interventions. Thereby, we can (a) quantify the effects of recent governmental measures to mitigating the disease spread, and (b) incorporate analogue change points to forecast future scenarios and case numbers. Our code is freely available and can be readily adapted to any country or region.