Optimal Selection of Sampling Points for Detecting SARS-CoV-2 RNA in Sewer System Using NSGA-II Algorithm

Abstract

Sampling and analysing urban wastewater are found to be a reliable indicator of the regional spread of infectious diseases. During the COVID-19 pandemic, several research groups around the globe sampled wastewater from treatment plants or other points throughout a sewer system and tried to identify the presence of the virus. Since infected persons are found to excrete the virus in their feces and urine, urban wastewater analysis proved to be a valuable tool for the early detection of spikes in the disease. In the present study, an effort was made to investigate several fate and transport scenarios of SARS-CoV-2 in a sewer system. USEPA’s Storm Water Management Model (SWMM) was utilized for the analysis. The modelling results were then used as an input to an optimization procedure using an NSGA-II algorithm. The optimization procedure aimed to determine the appropriate number and combination of sampling points for a better assessment of the disease’s dispersion in the community. Four to six sampling points seem to offer a high likelihood of SARS-CoV-2 RNA detection in minimum time, representing the maximum population.