Acquired insights from the long-term surveillance of SARS-CoV-2 RNA for COVID-19 monitoring: The case of Monterrey Metropolitan Area (Mexico)

Wastewater-based epidemiology offers a time-and cost-effective way to monitor SARS-CoV-2 spread in communities and therefore represents a complement to clinical testing. WBE applicability has been demonstrated in a number of cases over short-term periods as a method for tracking the prevalence of SARS-CoV-2 and an early warning tool for predicting outbreaks in the population. This study reports SARS-CoV-2 viral loads from wastewater treatment plants (WWTPs) and hospitals over a 6-month period (June to December 2020). Results show that the overall range of viral load in positive tested samples was between 1.2 x 10(3) and 3.5 x 10(6) gene copies/l, unveiling that secondary-treated wastewaters mirrored the viral load of influents. The interpretation suggests that the viral titers found in three out of four WWTPs were associated to clinical COVID-19 surveillance indicators preceding 2-7 days the rise of reported clinical cases. The median wastewater detection rate of SARSCoV-2 was one out of 14,300 reported new cases. Preliminary model estimates of prevalence ranged from 0.02 to 4.6% for the studied period. This comprehensive statistical and epidemiological analysis demonstrates that the applied wastewater-based approach to COVID-19 surveillance is in general consistent and feasible, although there is room for improvements.

Automated summary

Wastewater-based epidemiology is an effective and cost-effective method for monitoring the spread of SARS-CoV-2, providing early warning of outbreaks in the population. The study found a correlation between viral load in wastewater and clinical cases, with wastewater surveillance able to provide a 2-7 day early warning. The detection rate of SARS-CoV-2 in wastewater was relatively low, with one detection for every 14,300 reported new cases. Prevalence estimates during the study period ranged from 0.02% to 4.6%. The application of wastewater-based epidemiology for COVID-19 surveillance is generally consistent and feasible, with room for improvement.