tetano
Editor, Senior Moderator
Trop Med Infect Dis
. 2025 Sep 16;10(9):264.
doi: 10.3390/tropicalmed10090264. Wastewater-Based Surveillance of SARS-CoV-2 and Modeling of COVID-19 Infection Trends
Wenli Wang[SUP] 1 2 [/SUP], Ruoyu Li[SUP] 1 2 [/SUP], Shilin Chen[SUP] 1 2 [/SUP], Liangping Chen[SUP] 1 2 [/SUP], Yu Jiang[SUP] 1 2 [/SUP], Jianjun Xiang[SUP] 1 2 [/SUP], Jing Wu[SUP] 1 2 [/SUP], Jing Li[SUP] 1 2 [/SUP], Zhiwei Chen[SUP] 3 [/SUP], Chuancheng Wu[SUP] 1 2 [/SUP]
Affiliations
Background: This study was performed to evaluate the early warning value of wastewater-based epidemiology (WBE) in monitoring severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and its correlation with population-level coronavirus disease 2019 (COVID-19) infection trends.
Methods: Wastewater samples from Fuzhou's Sewage Treatment Plant A were concentrated via membrane filtration and quantified using reverse transcription quantitative polymerase chain reaction (RT-qPCR). Viral load data were integrated with sentinel hospital positivity rates and respiratory outpatient visits from 11 city hospitals. Stratified cross-correlation lag analysis was performed by gender, age, and hospital type.
Results: Using the lowest single-day genome concentration as a proxy for daily SARS-CoV-2 levels was advantageous. Wastewater viral concentrations correlated positively with clinical cases, with peaks preceding reports by 0 to 17 days. Stratified analysis further indicated that women, older adults, and individuals from general hospitals were more sensitive to changes in wastewater viral loads, showing stronger correlations between infection trends and wastewater signals.
Conclusions: Wastewater surveillance of SARS-CoV-2 can effectively predict COVID-19 infection trends and offers a scientific basis for stratified and targeted interventions. The findings underscore the value of WBE as an early warning tool in public health surveillance.
Keywords: COVID-19; SARS-CoV-2; early warning; public health monitoring; time-series analysis; viral load; wastewater-based epidemiology.
. 2025 Sep 16;10(9):264.
doi: 10.3390/tropicalmed10090264. Wastewater-Based Surveillance of SARS-CoV-2 and Modeling of COVID-19 Infection Trends
Wenli Wang[SUP] 1 2 [/SUP], Ruoyu Li[SUP] 1 2 [/SUP], Shilin Chen[SUP] 1 2 [/SUP], Liangping Chen[SUP] 1 2 [/SUP], Yu Jiang[SUP] 1 2 [/SUP], Jianjun Xiang[SUP] 1 2 [/SUP], Jing Wu[SUP] 1 2 [/SUP], Jing Li[SUP] 1 2 [/SUP], Zhiwei Chen[SUP] 3 [/SUP], Chuancheng Wu[SUP] 1 2 [/SUP]
Affiliations
- PMID: 41003574
- PMCID: PMC12474395
- DOI: 10.3390/tropicalmed10090264
Background: This study was performed to evaluate the early warning value of wastewater-based epidemiology (WBE) in monitoring severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and its correlation with population-level coronavirus disease 2019 (COVID-19) infection trends.
Methods: Wastewater samples from Fuzhou's Sewage Treatment Plant A were concentrated via membrane filtration and quantified using reverse transcription quantitative polymerase chain reaction (RT-qPCR). Viral load data were integrated with sentinel hospital positivity rates and respiratory outpatient visits from 11 city hospitals. Stratified cross-correlation lag analysis was performed by gender, age, and hospital type.
Results: Using the lowest single-day genome concentration as a proxy for daily SARS-CoV-2 levels was advantageous. Wastewater viral concentrations correlated positively with clinical cases, with peaks preceding reports by 0 to 17 days. Stratified analysis further indicated that women, older adults, and individuals from general hospitals were more sensitive to changes in wastewater viral loads, showing stronger correlations between infection trends and wastewater signals.
Conclusions: Wastewater surveillance of SARS-CoV-2 can effectively predict COVID-19 infection trends and offers a scientific basis for stratified and targeted interventions. The findings underscore the value of WBE as an early warning tool in public health surveillance.
Keywords: COVID-19; SARS-CoV-2; early warning; public health monitoring; time-series analysis; viral load; wastewater-based epidemiology.