论文标题
嘈杂的PCR用于病毒测试
Noisy Pooled PCR for Virus Testing
论文作者
论文摘要
快速测试可以帮助减轻2019年冠状病毒病（COVID-19）大流行。尽管具有单个样本分析的准确性，但传染病（例如RT-PCR）仍需要大量资源来测试大量人群。我们开发了一种可扩展的方法来确定合并患者样品的病毒状态。我们的方法将小组测试转换为线性反问题，在该问题中，假阳性和负面因素被解释为嘈杂的通信渠道产生的，并且传递算法的消息估计患者的疾病状况。数值结果表明，我们的方法比现有的噪声组测试算法估算了患者疾病的估计。我们的方法很容易扩展到各种应用程序，包括必须最小化假否定的地方。最后，在一个乌托邦世界中，我们将与RT-PCR专家合作；大流行期间很难形成这种联系。我们欢迎新的合作者联系并帮助改善这项工作！
Fast testing can help mitigate the coronavirus disease 2019 (COVID-19) pandemic. Despite their accuracy for single sample analysis, infectious diseases diagnostic tools, like RT-PCR, require substantial resources to test large populations. We develop a scalable approach for determining the viral status of pooled patient samples. Our approach converts group testing to a linear inverse problem, where false positives and negatives are interpreted as generated by a noisy communication channel, and a message passing algorithm estimates the illness status of patients. Numerical results reveal that our approach estimates patient illness using fewer pooled measurements than existing noisy group testing algorithms. Our approach can easily be extended to various applications, including where false negatives must be minimized. Finally, in a Utopian world we would have collaborated with RT-PCR experts; it is difficult to form such connections during a pandemic. We welcome new collaborators to reach out and help improve this work!
