论文标题
推断COVID-19中的变更点揭示了干预措施的有效性
Inferring change points in the COVID-19 spreading reveals the effectiveness of interventions
论文作者
论文摘要
由于Covid-19正在迅速遍及全球，因此短期建模预测为关于遏制和缓解策略的决策提供了时间关键信息。短期预测的主要挑战是评估关键流行病学参数，以及在初次干预措施显示出效果时如何改变。通过将既定的流行病学模型与贝叶斯推断相结合，我们分析了新感染的有效生长速率的时间依赖性。为了关注德国的共同传播，我们检测到与公开宣布的干预时期相关的有效增长率的变化点。因此，我们可以量化干预措施的效果，我们可以将相应的更改点纳入未来场景和案例数的预测中。我们的代码是免费的，可以很容易地适应任何国家或地区。
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.
