Cost-Effective Sampling Strategies for Wastewater Surveillance: A Large-Scale Longitudinal Study in Hong Kong and Shenzhen.

During the COVID-19 pandemic, wastewater surveillance undoubtedly served as a useful public health surveillance tool and catalyzed the establishment of over 4600 sampling sites and 195 wastewater surveillance dashboards worldwide. However, in the postpandemic era, the continuous regular operation of these sites has become labor-intensive and costly. In this study, we established two downsampling strategies (i.e., Enumerative Method and Iterative Hierarchical Method) to explore the potential of reducing the number of sampling sites and sampling frequencies without significantly affecting the observed SARS-CoV-2 transmission trends. To evaluate the method's effectiveness, we comprehensively applied them to two intensive large-scale wastewater surveillance data sets from two adjacent major cities, i.e., a 9-month monitoring across 12 sampling sites in Hong Kong and a 5-month monitoring across 38 sampling sites in Shenzhen. We found that consistent citywide SARS-CoV-2 transmission trends were captured in the two cities before and after halving the sampling sites. Additionally, we observed that multiple factors (i.e., flow rate, serving population size, number of sampling sites, geographical locations, etc.) influenced the site selection and trend representativeness, which should be comprehensively considered in the design of a sampling strategy. Moreover, sampling three times per week could reflect the virus transmission patterns during the Omicron outbreak. These findings highlight the necessity to optimize current sampling practices in most wastewater surveillance networks so that they can maximize the value of wastewater surveillance and achieve favorable cost-effectiveness for sustainable long-term monitoring.