The challenges of modeling and forecasting the spread of COVID-19
The challenges of modeling and forecasting the spread of COVID-19
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DOI：
10.1073/pnas.2006520117
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发表时间：
2020-07-21
影响因子：
11.1
通讯作者：
Sledge, Daniel
中科院分区：
文献类型：
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作者：
Bertozzi, Andrea L.;Franco, Elisa;Sledge, Daniel
2019冠状病毒病（COVID-19）大流行将流行病建模置于全球公共政策制定的前沿。尽管如此，建模和预测COVID-19的传播仍然是一个挑战。在这里，我们详细介绍了三个区域规模的模型，用于预测和评估大流行的过程。这项工作展示了早期数据的简约模型的实用性，并提供了一个可访问的框架，用于生成与政策相关的见解。我们展示了这些模型如何相互连接，并连接到特定区域的时间序列数据。这些模型能够测量和预测社交距离的影响，强调了在没有疫苗或抗病毒疗法的情况下放松非药物公共卫生干预的危险。
The coronavirus disease 2019 (COVID-19) pandemic has placed epidemic modeling at the forefront of worldwide public policy making. Nonetheless, modeling and forecasting the spread of COVID-19 remains a challenge. Here, we detail three regional-scale models for forecasting and assessing the course of the pandemic. This work demonstrates the utility of parsimonious models for early-time data and provides an accessible framework for generating policy-relevant insights into its course. We show how these models can be connected to each other and to time series data for a particular region. Capable of measuring and forecasting the impacts of social distancing, these models high-light the dangers of relaxing nonpharmaceutical public health interventions in the absence of a vaccine or antiviral therapies.