Inferring change points in the spread of COVID-19 reveals the effectiveness of interventions
Inferring change points in the spread of COVID-19 reveals the effectiveness of interventions
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DOI：
10.1126/science.abb9789
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发表时间：
2020-07-10
期刊：
影响因子：
56.9
通讯作者：
Priesemann, Viola
中科院分区：
文献类型：
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作者：
Dehning, Jonas;Zierenberg, Johannes;Priesemann, Viola
随着2019冠状病毒病（COVID-19）在全球迅速蔓延，短期建模预测为遏制和缓解战略的决策提供了时间紧迫的信息。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。将建立的流行病学模型与贝叶斯推理相结合，分析了新发感染有效增长率的时间依赖性。以新冠病毒在德国的传播为重点，我们发现了有效增长率的变化点，这些变化点与公开宣布干预措施的时间密切相关。因此，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，可以很容易地适应任何国家或地区。
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.