Selecting sampling points to monitor traces of SARS-CoV-2 in sewage at the intra-urban scale is no trivial task given the complexity of the networks and the multiple technical, economic and socio-environmental constraints involved. This paper proposes two algorithms for the automatic selection of sampling locations in sewage networks. The first algorithm, is for the optimal selection of a predefined number of sampling locations ensuring maximum coverage of inhabitants and minimum overlapping amongst selected sites (static approach). The second is for establishing a strategy of iterations of sample&analysis to identify patient zero and hot spots of COVID-19 infected inhabitants in cities (dynamic approach). The algorithms are based on graph-theory and are coupled to a greedy optimization algorithm. The usefulness of the algorithms is illustrated in the case study of Girona (NE Iberian Peninsula, 148,504 inhabitants). The results show that the algorithms are able to automatically propose locations for a given number of stations. In the case of Girona, always covering more than 60% of the manholes and with less than 3% of them overlapping amongst stations. Deploying 5, 6 or 7 stations results in more than 80% coverage in manholes and more than 85% of the inhabitants. For the dynamic sensor placement, we demonstrate that assigning infection probabilities to each manhole as a function of the number of inhabitants connected reduces the number of iterations required to detect the zero patient and the hot spot areas.

鉴于网络的复杂性以及所涉及的多种技术、经济和社会环境限制，选择采样点来监测城市内污水中的 SARS-CoV-2 痕迹并非易事。本文提出了两种污水管网采样位置自动选择算法。第一种算法用于最佳选择预定义数量的采样位置，确保最大程度地覆盖居民并最小化选定地点之间的重叠（静态方法）。第二个是建立样本和分析迭代策略，以识别城市中零号患者和感染 COVID-19 居民的热点地区（动态方法）。该算法基于图论并与贪婪优化算法相结合。赫罗纳（伊比利亚半岛东北部，148,504 名居民）的案例研究说明了算法的实用性。结果表明，该算法能够自动为给定数量的站点提出位置建议。以赫罗纳为例，总是覆盖超过 60% 的沙井，其中只有不到 3% 的沙井在车站之间重叠。部署5个、6个或7个站可以实现80%以上的沙井覆盖率和85%以上的居民覆盖率。对于动态传感器放置，我们证明，将感染概率分配给每个沙井作为所连接居民数量的函数，可以减少检测零患者和热点区域所需的迭代次数。