Max Planck Institute for Dynamics and Self-Organization, 37077 Göttingen, Germany.
Campus Institute for Dynamics of Biological Networks, University of Göttingen, 37075 Göttingen, Germany.
Science. 2020 Jul 10;369(6500). doi: 10.1126/science.abb9789. Epub 2020 May 15.
As coronavirus disease 2019 (COVID-19) is rapidly spreading across the globe, short-term modeling forecasts provide time-critical information for decisions on containment and mitigation strategies. A major 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 analyzed the time dependence of the effective growth rate of new infections. Focusing on COVID-19 spread in Germany, we detected change points in the effective growth rate that correlate well with the times of publicly announced interventions. Thereby, we could quantify the effect of interventions and 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.
随着 2019 年冠状病毒病（COVID-19）在全球范围内迅速蔓延，短期建模预测为控制和缓解策略的决策提供了关键的时间信息。短期预测的一个主要挑战是评估关键的流行病学参数，以及当首次干预显示出效果时，这些参数如何变化。我们通过将一个成熟的流行病学模型与贝叶斯推断相结合，分析了新感染的有效增长率的时间依赖性。关注 COVID-19 在德国的传播，我们检测到有效增长率的变化点与公开宣布干预的时间很好地相关。由此，我们可以量化干预措施的效果，并将相应的变化点纳入未来情景和病例数的预测中。我们的代码是免费提供的，可以很容易地适用于任何国家或地区。
