论文标题
及时跟踪人群中个体的感染状况
Timely Tracking of Infection Status of Individuals in a Population
论文作者
论文摘要
我们考虑实时及时跟踪人群中个体的感染状况（例如，COVID-19）。在这项工作中，医疗保健提供者希望发现受感染的人以及尽快从疾病中康复的人。为了衡量跟踪过程的及时性，我们根据最新的测试结果，使用医疗保健提供者的实际感染状态与他们的实时估算之间的长期平均差异。我们首先在给定的测试率以及给定的人的感染和恢复率的情况下找到了该平均差异的分析表达。接下来，我们提出了一种基于最小化的算法，以最大程度地减少这种平均差异。我们观察到，如果总测试率受到限制，而不是同等地测试人口的所有成员，则只有一部分人口根据其感染和恢复率进行测试。我们还观察到，提高总测试率有助于更好地跟踪感染状态。此外，人口规模的增加会增加感染和恢复率不同的人的多样性，可以利用这可以更有效地支出测试能力，从而提高系统性能。最后，根据医疗保健提供者的喜好，可以更改测试率分配以更快地检测被感染者或被恢复的人。
We consider real-time timely tracking of infection status (e.g., covid-19) of individuals in a population. In this work, a health care provider wants to detect infected people as well as people who recovered from the disease as quickly as possible. In order to measure the timeliness of the tracking process, we use the long-term average difference between the actual infection status of the people and their real-time estimate by the health care provider based on the most recent test results. We first find an analytical expression for this average difference for given test rates, and given infection and recovery rates of people. Next, we propose an alternating minimization based algorithm to minimize this average difference. We observe that if the total test rate is limited, instead of testing all members of the population equally, only a portion of the population is tested based on their infection and recovery rates. We also observe that increasing the total test rate helps track the infection status better. In addition, an increased population size increases diversity of people with different infection and recovery rates, which may be exploited to spend testing capacity more efficiently, thereby improving the system performance. Finally, depending on the health care provider's preferences, test rate allocation can be altered to detect either the infected people or the recovered people more quickly.
