TY - JOUR
T1 - Comparative assessment of sewer sampling methods for infectious disease surveillance
T2 - Insights from transport modeling and simulations of SARS-CoV-2 emissions
AU - Ban, Min Jeong
AU - Kim, Keugtae
AU - Kim, Sungpyo
AU - Kim, Lan Hee
AU - Kang, Joo Hyon
N1 - Publisher Copyright:
© 2025 Elsevier Ltd
PY - 2025/6/15
Y1 - 2025/6/15
N2 - Emerging infectious diseases like COVID-19 present significant public health challenges, necessitating effective surveillance methods. Wastewater-based epidemiology (WBE), detecting viral pathogens in wastewater, has emerged as a proactive tool for monitoring infections. This study evaluated various wastewter sampling methods through SARS-CoV-2 transport simulations in an urban sewer network in Sejong City, South Korea, to identify cost-effective strategies for accurate infection monitoring. Using the U.S. EPA's Storm Water Management Model (SWMM) and Markov chain Monte Carlo (MCMC) sampling, we simulated wastewater flow and viral concentrations based on reported COVID-19 case data for the year 2021. In this study, we used reported COVID-19 cases as a hypothetical estimate of the number of infected individuals in the simulation. The SWMM effectively replicated daily and monthly patterns in sewer flow rates. Combining the SWMM with MCMC sampling from the probability distributions of spatio-temporal virus emission patterns, we generated an ensemble time series dataset of hourly virus concentrations based on 200 simulations, forming the basis for evaluating sampling alternatives. Results showed a strong correlation (R2 = 0.81) between daily average virus concentrations and daily infection rates on the fifth day following new infections, consistent with simulated viral emission patterns. Flow-weighted and equally timed sampling methods provided highly reliable infection pattern estimates, suggesting that equally timed sampling is a cost-effective alternative. In contrast, grab sampling performed poorly due to difficulties in capturing peak viral emission periods. We found that a minimum sampling duration of four to six hours was crucial for accurate detection, with performance increasing if the sampling was applied in the morning (R2 ≈ 0.7). Longer durations steadily, but only slightly, improved results. While this simulation-based approach focused on predicting daily virus concentration patterns in wastewater rather than precisely estimating its absolute levels, it provides valuable insights for optimizing WBE in public health surveillance and underscores the need for further validation with real-world data.
AB - Emerging infectious diseases like COVID-19 present significant public health challenges, necessitating effective surveillance methods. Wastewater-based epidemiology (WBE), detecting viral pathogens in wastewater, has emerged as a proactive tool for monitoring infections. This study evaluated various wastewter sampling methods through SARS-CoV-2 transport simulations in an urban sewer network in Sejong City, South Korea, to identify cost-effective strategies for accurate infection monitoring. Using the U.S. EPA's Storm Water Management Model (SWMM) and Markov chain Monte Carlo (MCMC) sampling, we simulated wastewater flow and viral concentrations based on reported COVID-19 case data for the year 2021. In this study, we used reported COVID-19 cases as a hypothetical estimate of the number of infected individuals in the simulation. The SWMM effectively replicated daily and monthly patterns in sewer flow rates. Combining the SWMM with MCMC sampling from the probability distributions of spatio-temporal virus emission patterns, we generated an ensemble time series dataset of hourly virus concentrations based on 200 simulations, forming the basis for evaluating sampling alternatives. Results showed a strong correlation (R2 = 0.81) between daily average virus concentrations and daily infection rates on the fifth day following new infections, consistent with simulated viral emission patterns. Flow-weighted and equally timed sampling methods provided highly reliable infection pattern estimates, suggesting that equally timed sampling is a cost-effective alternative. In contrast, grab sampling performed poorly due to difficulties in capturing peak viral emission periods. We found that a minimum sampling duration of four to six hours was crucial for accurate detection, with performance increasing if the sampling was applied in the morning (R2 ≈ 0.7). Longer durations steadily, but only slightly, improved results. While this simulation-based approach focused on predicting daily virus concentration patterns in wastewater rather than precisely estimating its absolute levels, it provides valuable insights for optimizing WBE in public health surveillance and underscores the need for further validation with real-world data.
KW - COVID-19
KW - Infectious disease
KW - Markov chain Monte Carlo (MCMC) sampling
KW - Sewer network
KW - Storm Water Management Model (SWMM)
KW - Wastewater-based epidemiology
UR - http://www.scopus.com/inward/record.url?scp=85218452605&partnerID=8YFLogxK
U2 - 10.1016/j.watres.2025.123373
DO - 10.1016/j.watres.2025.123373
M3 - Article
C2 - 40015223
AN - SCOPUS:85218452605
SN - 0043-1354
VL - 278
JO - Water Research
JF - Water Research
M1 - 123373
ER -