测试数字在揭示COVID-19真值和应用干预方面的可靠性如何？
How Reliable are Test Numbers for Revealing the COVID-19 Ground Truth and Applying Interventions?
摘要：
在公共话语和政策制定中，COVID-19确诊病例的数量通常被用作真值COVID-19感染病例的实际数量的代表。然而，确诊病例的数量取决于检测策略，理解使用不同检测策略获得的阳性病例数量如何揭示未知的真值是很重要的。我们在Python中开发了一个基于代理的模拟框架，可以模拟各种测试策略以及基于它们的锁定等干预措施。代理之间的交互可以考虑各种社区和移动模式。我们的框架的一个显著特点是存在另一种症状类似于COVID-19的“流感”样疾病，这使我们能够在选择待检测患者时对噪音进行建模。我们实例化我们的模型，在印度的Bengalu市，使用人口普查数据分布代理的地理位置，交通流的流动性数据来模拟长距离的互动和混合。我们使用模拟框架来比较三种测试策略的性能：随机症状测试（Random Symptoms Testing，简称ESTA）、接触跟踪（Contact Tracing，简称CT）和一种新的基于位置的测试策略（Location Based Testing，简称LBT）。我们观察到，如果有足够比例的有症状的患者出来进行测试，那么即使每天进行很少的测试，ESTA也可以非常接近地捕获真值。然而，CT始终捕获更多的阳性病例。有趣的是，我们的新LBT，这是操作强度低于CT，提供了性能与CT相媲美。在另一个方向上，我们比较了这三种测试策略在启用锁定方面的功效，并观察到CT最大限度地使真值曲线平滑，LBT紧随其后，并且显著优于平滑。
英文摘要：
The number of confirmed cases of COVID-19 is often used as a proxy for the actual number of ground truth COVID-19 infected cases in both public discourse and policy making. However, the number of confirmed cases depends on the testing policy, and it is important to understand how the number of positive cases obtained using different testing policies reveals the unknown ground truth. We develop an agent-based simulation framework in Python that can simulate various testing policies as well as interventions such as lockdown based on them. The interaction between the agents can take into account various communities and mobility patterns. A distinguishing feature of our framework is the presence of another `flu'-like illness with symptoms similar to COVID-19, that allows us to model the noise in selecting the pool of patients to be tested. We instantiate our model for the city of Bengaluru in India, using census data to distribute agents geographically, and traffic flow mobility data to model long-distance interactions and mixing. We use the simulation framework to compare the performance of three testing policies: Random Symptomatic Testing (RST), Contact Tracing (CT), and a new Location Based Testing policy (LBT). We observe that if a sufficient fraction of symptomatic patients come out for testing, then RST can capture the ground truth quite closely even with very few daily tests. However, CT consistently captures more positive cases. Interestingly, our new LBT, which is operationally less intensive than CT, gives performance that is comparable with CT. In another direction, we compare the efficacy of these three testing policies in enabling lockdown, and observe that CT flattens the ground truth curve maximally, followed closely by LBT, and significantly better than RST.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
