论文标题
一种可观察到的MDP方法，用于进行连续性疾病（例如COVID-19）
A Partially Observable MDP Approach for Sequential Testing for Infectious Diseases such as COVID-19
论文作者
论文摘要
新颖的冠状病毒（Covid-19）的爆发正在发生，这是一场重大的国际危机，其影响力扩展到了我们日常生活的各个方面。有效的测试可以使受感染的人被隔离，从而减少了199日的传播，挽救了无数的生命，并帮助安全，安全地重新启动经济。通过接触跟踪提供有关卫生保健提供者有关受感染患者下落的信息以确定谁测试的信息，可以极大地帮助制定良好的测试策略。在围绕病毒方面更成功的国家通常使用``测试，治疗，跟踪，测试''策略，该策略始于测试症状的人，通过患者记忆，应用程序，WIFI，GPS等组合进行正面测试的个体的痕迹接触，然后测试他们的接触并重复此过程。问题在于，这种策略是近视的，并且不能有效地使用测试资源。尤其是Covid-19，在感染后几天可能出现症状（或根本没有，有证据表明许多Covid-19载体是渐近的，但可能会传播病毒）。这种贪婪的策略，错过了病毒可能处于休眠状态并在未来爆发的人口领域。 在本文中，我们表明，测试问题可以作为基于序列学习的资源分配问题和约束，其中该问题的输入是由通过各种触点跟踪工具获得的时间变化的社交接触图提供的。然后，我们制定有效的学习策略，以最大程度地减少受感染者的数量。这些策略基于政策迭代和审视规则。我们研究了基本性能界限，并确保我们的解决方案对输入图和测试本身中的错误具有鲁棒性。
The outbreak of the novel coronavirus (COVID-19) is unfolding as a major international crisis whose influence extends to every aspect of our daily lives. Effective testing allows infected individuals to be quarantined, thus reducing the spread of COVID-19, saving countless lives, and helping to restart the economy safely and securely. Developing a good testing strategy can be greatly aided by contact tracing that provides health care providers information about the whereabouts of infected patients in order to determine whom to test. Countries that have been more successful in corralling the virus typically use a ``test, treat, trace, test'' strategy that begins with testing individuals with symptoms, traces contacts of positively tested individuals via a combinations of patient memory, apps, WiFi, GPS, etc., followed by testing their contacts, and repeating this procedure. The problem is that such strategies are myopic and do not efficiently use the testing resources. This is especially the case with COVID-19, where symptoms may show up several days after the infection (or not at all, there is evidence to suggest that many COVID-19 carriers are asymptotic, but may spread the virus). Such greedy strategies, miss out population areas where the virus may be dormant and flare up in the future. In this paper, we show that the testing problem can be cast as a sequential learning-based resource allocation problem with constraints, where the input to the problem is provided by a time-varying social contact graph obtained through various contact tracing tools. We then develop efficient learning strategies that minimize the number of infected individuals. These strategies are based on policy iteration and look-ahead rules. We investigate fundamental performance bounds, and ensure that our solution is robust to errors in the input graph as well as in the tests themselves.
