论文标题
优化检测COVID-19爆发的测试策略
Optimizing testing policies for detecting COVID-19 outbreaks
论文作者
论文摘要
COVID-19大流行对继续经济活动构成了挑战，同时降低了健康风险。尽管可以通过测试来缓解这些挑战，但测试预算通常受到限制。在这里，我们研究了诸如疗养院之类的机构如何利用固定的测试预算来早日发现爆发。我们使用扩展的网络分析模型，表明鉴于一定的测试预算，通常比测试较大的组相比，经常测试较小的人群亚组，但频率较低。数值结果与我们在指数扩散模型中检测到的爆发大小的分析表达式一致。我们的工作为机构提供了一个简单的指南：在几批批次上分配您的总测试，而不是一次使用它们。我们预计，在适当的情况下，这种易于实施的政策建议将导致早期发现并更好地缓解当地Covid-19-19。
The COVID-19 pandemic poses challenges for continuing economic activity while reducing health risks. While these challenges can be mitigated through testing, testing budget is often limited. Here we study how institutions, such as nursing homes, should utilize a fixed test budget for early detection of an outbreak. Using an extended network-SEIR model, we show that given a certain budget of tests, it is generally better to test smaller subgroups of the population frequently than to test larger groups but less frequently. The numerical results are consistent with an analytical expression we derive for the size of the outbreak at detection in an exponential spread model. Our work provides a simple guideline for institutions: distribute your total tests over several batches instead of using them all at once. We expect that in the appropriate scenarios, this easy-to-implement policy recommendation will lead to earlier detection and better mitigation of local COVID-19 outbreaks.
