具有压倒性噪声网络中的局部流行病检测
Localized epidemic detection in networks with overwhelming noise
摘要：
我们考虑的问题，检测流行病在人口中的个人诊断是非常嘈杂的。这个问题的动机是过多的例子（人类的流感病毒株，或智能手机中的计算机病毒等）。可靠的诊断很少，但噪音数据丰富。在流感/手机病毒中，极少数受感染的人/手机被专业诊断（只有一小部分去看医生），但不太可靠的二级签名（例如，人们呆在家里，或者比典型的上传活动更频繁）更容易获得。这些次要数据往往不可靠：许多流感患者并不呆在家里，而许多呆在家里的人并没有患流感。本文确定了精确的制度，接触网络的知识，使大海捞针：我们提供了一个分布式的，高效的和强大的算法，可以正确地识别存在的传播流行病从高度不可靠的本地数据。我们的算法只需要这个图的局部邻居知识，并且在我们描述的一系列广泛的设置中，即使假阴性和假阳性占可用数据的绝大部分，也能成功。我们的研究结果表明，它成功地在接触网络的部分信息的存在下，也没有一个单一的“零号病人”，而是许多（数百，在我们的例子中）初始零号病人，分布在整个图。
英文摘要：
We consider the problem of detecting an epidemic in a population where individual diagnoses are extremely noisy. The motivation for this problem is the plethora of examples (influenza strains in humans, or computer viruses in smartphones, etc.) where reliable diagnoses are scarce, but noisy data plentiful. In flu/phone-viruses, exceedingly few infected people/phones are professionally diagnosed (only a small fraction go to a doctor) but less reliable secondary signatures (e.g., people staying home, or greater-than-typical upload activity) are more readily available. These secondary data are often plagued by unreliability: many people with the flu do not stay home, and many people that stay home do not have the flu. This paper identifies the precise regime where knowledge of the contact network enables finding the needle in the haystack: we provide a distributed, efficient and robust algorithm that can correctly identify the existence of a spreading epidemic from highly unreliable local data. Our algorithm requires only local-neighbor knowledge of this graph, and in a broad array of settings that we describe, succeeds even when false negatives and false positives make up an overwhelming fraction of the data available. Our results show it succeeds in the presence of partial information about the contact network, and also when there is not a single "patient zero", but rather many (hundreds, in our examples) of initial patient-zeroes, spread across the graph.
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。
