Thanks for reopening, @orenwolf. Thanks for splitting, @ficuswhisperer.
Came here to post a paper. All emphasis mine.
Inferring COVID-19 spreading rates and potential change points for case number forecasts
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.
Results, paraphrased: measures in Germany seem to be working, but we need more time. Particularly,
We find first evidence for a successive decrease of the spreading rate in Germany around March 8 and around March 16, which significantly reduced the magnitude of exponential growth, but was not sufficient to turn growth into decay. The development in the coming two weeks will reveal the efficiency of the subsequent social distancing measures.
Also, they are very convinced of their results - this is a bold statement coming from some top of their field modelers:
In general, our analysis code may help to infer the efficiency of measures taken in other countries and inform policy makers about tightening, loosening and selecting appropriate rules for containment
Paper pdf, as always during these times: preprint, so not yet peer reviewed.
https://arxiv.org/pdf/2004.01105.pdf
Code:
Warning: if you arenât a experienced modeler with a strong epidemiological background, do not just apply this and come to your own conclusions, and please, pretty please with sugar on top and eternal damnation in the deepest frozen hell mythology has to offer donât post your own conclusions âsomewhereâ on the web. If you are really that good, and find something interesting, contact the authors. And share your code.
Donât meddle. Help, if you can. But fucking donât meddle. Please.
