Wastewater monitoring is an effective approach for the early detection of viral and bacterial disease outbreaks. It has recently been used to identify the presence of individuals infected with COVID-19. To monitor large communities and accurately localize buildings with infected individuals with a limited number of sensors, one must carefully choose the sampling locations in wastewater networks. We also have to account for concentration requirements on the collected wastewater samples to ensure reliable virus presence test results. We model this as a sensor placement problem. Although sensor placement for source localization arises in numerous problems, most approaches use application-specific heuristics and fail to consider multiple source scenarios. To address these limitations, we develop a novel approach that combines Bayesian networks and discrete optimization to efficiently identify informative sensor placements and accurately localize virus sources. Our approach also takes into account concentration requirements on wastewater samples during optimization. Our simulation experiments demonstrate the quality of our sensor placements and the accuracy of our source localization approach. Furthermore, we show the robustness of our approach to discrepancies between the virus outbreak model and the actual outbreak rates.

翻译：污水监测是早期检测病毒和细菌疾病爆发的有效方法，近期已被用于识别感染COVID-19的个体存在。为在有限传感器数量下监测大规模社区并准确定位感染个体所在的建筑物，必须谨慎选择污水管网中的采样点。同时还需考虑所采集污水样本的浓度要求，以确保病毒检测结果的可靠性。我们将此建模为传感器布局问题。尽管源定位的传感器布局在众多问题中均有出现，但大多数方法使用特定应用场景的启发式策略，且未能考虑多源场景。为克服这些局限，我们开发了一种创新方法，结合贝叶斯网络与离散优化，高效识别信息量丰富的传感器布局并准确定位病毒源。该方法在优化过程中还考虑了污水样本的浓度要求。仿真实验验证了传感器布局的质量与源定位方法的准确性。此外，我们展示了该方法对病毒爆发模型与实际爆发率之间差异的鲁棒性。