key: cord-0852573-v7i6rmi9 authors: Domokos, Endre; Sebestyén, Viktor; Somogyi, Viola; Trájer, Attila János; Gerencsér-Berta, Renáta; Horváth, Borbála Oláhné; Tóth, Endre Gábor; Jakab, Ferenc; Kemenesi, Gábor; Abonyi, János title: Identification of sampling points for the detection of SARS-CoV-2 in the sewage system date: 2021-10-29 journal: Sustain Cities Soc DOI: 10.1016/j.scs.2021.103422 sha: 700b1442d709a5adfa13cb9b94befebd0931098c doc_id: 852573 cord_uid: v7i6rmi9 A suitable tool for monitoring the spread of SARS-CoV-2 is to identify potential sampling points in the wastewater collection system that can be used to monitor the distribution of COVID-19 disease affected clusters within a city. The applicability of the developed methodology is presented through the description of the 72,837 population equivalent wastewater collection system of the city of Nagykanizsa, Hungary and the results of the analytical and epidemiological measurements of the wastewater samples. The wastewater sampling was conducted during the 3rd wave of the COVID-19 epidemic. It was found that the overlap between the road system and the wastewater network is high, it is 82 %. It was showed that the proposed methodological approach, using the tools of network science, determines confidently the zones of the wastewater collection system and provides the ideal monitoring points in order to provide the best sampling resolution in urban areas. The strength of the presented approach is that it estimates the network based on publicly available information. It was concluded that the number of zones or sampling points can be chosen based on relevant epidemiological intervention and mitigation strategies. The algorithm allows for continuous effective monitoring of the population infected by SARS-CoV-2 in small-sized cities. zones of the wastewater collection system and provides the ideal monitoring points in order to provide the best sampling resolution in urban areas. The strength of the presented approach is that it estimates the network based on publicly available information. It was concluded that the number of zones or sampling points can be chosen based on relevant epidemiological intervention and mitigation strategies. The algorithm allows for continuous effective The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2; [1] ) 2 that caused the COVID-19 pandemic, resulted in the death of more than 4.5 3 million people until end of August of 2021, and the number of the recorded 4 cases reached 218 million people [2] . The virus is similar to the SARS-CoV 5 coronavirus and it was transmitted from animals to humans by a host switch 6 [3] . The global epidemic has severe and pervasive socio-economic impacts 7 [4] including the direct mortality of the disease, the mortality related to de- 8 pression, social isolation and unemployment [5] . In general, the pandemic 9 posed challenges in achieving several of the sustainability goals[6], cities be-10 ing more affected [7] . The built environment density is in positive correlation 11 with incident rates [8] beside income and poverty [9] . Based on mobile phone 12 positioning data it was revealed that even after the incident rates were under sampling is applied, a larger group of people can be tested by combining 46 their samples [25] saving time [26] and resources [27] but this still requires 47 the collection of the individual clinical specimens. Describing the distribution of the infected population in the cities helps 125 planning appropriate measures on an early warning basis, as the structural 126 description of the wastewater collection system can be used to determine 127 the involvement in the parts of the settlement. Identifying the zones that 128 represent residents living in that area consistently and without blind spots, 129 has to be carried out considering several factors and requires information 130 of the wastewater collection system, and if possible a hydraulic model of it. The aim of this study was to provide a Geographic Information System (GIS) 132 based analytical tool to aid this purpose by evaluating the wastewater based 133 surveillance data with regards to the structure of the wastewater collection 134 system and thus to assess the relative incidence patterns of the COVID- In addition to route identification, potential shaft locations also need to be 176 modelled because sampling can be performed efficiently in these structures. In the model, the shafts are placed at the intersections to which the distance 178 between the two points can be calculated. Taking advantage of the fact that OSM database stores the necessary data for the buildings, the wastewater discharges were estimated and then assigned to the nodes that represent the sections. The cumulative discharge of the node is estimated from the total area of the nearest buildings in the given section: where Q i denotes the cumulative wastewater discharges of the node i (m 3 /day), The proposed method is based on the reachability analysis of the network 206 that calculates which discharges are observable from a given measurement. When R pm defines the set of nodes observable from the p m -th node (mea-208 suring point), the sum of the discharges sampled from that measurement is The set of nodes (discharges) observed by the sensors can significantly 211 overlap, e.g. it can happen that all of the nodes observed by the m-th sensor 212 are also observed by the k-th sensor, as R pm ⊂ R p k . Although this case 213 can be considered as a redundancy, placing the k-th or m-th sensor provides 214 additional information as the q p k − q pm difference provides information about 215 the nodes that are in the R p k \ R pm set (that represents the nodes that are 216 observable from the k-th set and not observable from the m-th set). The intersection of these sets segment the network. The measurements When the B matrix is designed to provide linearly independent aggregates of the segments, the estimated sum of the discharges can be estimated as: The good measurement placement provides good resolution that can be evaluated based on the maximum or the average of the x k values, so the measurement placement problem can be formulated as the minimisation of the maximum or average of the cumulative discharge of the identified segments: This optimisation problem can be solved by any meta-heuristic optimi- [60]. The inconsistency of the edges are measured based on the difference between the sum of the discharges that can be sampled from the related nodes: The proposed greedy algorithm finds the edges with the greatest inconsis- reasons can lead to discrepancies in the sampling strategy. Regression analyis was carried out on the measurement results (Table 394 4) to evaluate the relationship between the parameters. The filtered COD Table 5 . A sensitivity analysis of the zoning available in the territorial coverage 581 was also prepared for the presented settlement, which is illustrated in Figure 582 7, expressed as a percentage of the nodes of the whole settlement size. 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