tetano
Editor, Senior Moderator
Environ Sci Pollut Res Int
. 2022 May 6.
doi: 10.1007/s11356-022-20609-z. Online ahead of print.
"pySewage": a hybrid approach to predict the number of SARS-CoV-2-infected people from wastewater in Brazil
Adriano Roberto Vieira de Sousa[SUP] 1 [/SUP], Lívia do Carmo Silva[SUP] 2 [/SUP], Juliana Santana de Curcio[SUP] 2 [/SUP], Hugo Delleon da Silva[SUP] 2 3 [/SUP], Carlos Eduardo Anunciação[SUP] 4 [/SUP], Silvia Maria Salem Izacc[SUP] 2 [/SUP], Flavio Olimpio Sanches Neto[SUP] 5 [/SUP], Elisângela de Paula Silveira Lacerda[SUP] 2 [/SUP]
Affiliations
Abstract
It is well known that the new coronavirus pandemic has global environmental, public health, and economic implications. In this sense, this study aims to monitor SARS-CoV-2 in the largest wastewater treatment plant of Goiânia, which processes wastewater from more than 700,000 inhabitants, and to correlate the molecular and clinical data collected. Influent and effluent samples were collected at Dr. Helio de Seixo Britto's wastewater treatment plant from January to August 2021. Viral concentration was performed with polyethylene glycol before viral RNA extraction. Real-time qPCR (N1 and N2 gene assays) was performed to detect and quantify the viral RNA present in the samples. The results showed that 43.63% of the samples were positive. There is no significant difference between the detection of primers N1 (mean 3.23 log10 genome copies/L, std 0.23) and N2 (mean 2.95 log10 genome copies/L, std 0.29); also, there is no significant difference between the detection of influent and effluent samples. Our molecular data revealed a positive correlation with clinical data, and infection prevalence was higher than clinical data. In addition, we developed a user-friendly web application to predict the number of infected people based on the detection of viral load present in wastewater samples and may be applied as a public policy strategy for monitoring ongoing outbreaks.
Keywords: COVID-19; Coronavirus; SARS-CoV-2; Surveillance; Wastewater-based epidemiology; Web application.
. 2022 May 6.
doi: 10.1007/s11356-022-20609-z. Online ahead of print.
"pySewage": a hybrid approach to predict the number of SARS-CoV-2-infected people from wastewater in Brazil
Adriano Roberto Vieira de Sousa[SUP] 1 [/SUP], Lívia do Carmo Silva[SUP] 2 [/SUP], Juliana Santana de Curcio[SUP] 2 [/SUP], Hugo Delleon da Silva[SUP] 2 3 [/SUP], Carlos Eduardo Anunciação[SUP] 4 [/SUP], Silvia Maria Salem Izacc[SUP] 2 [/SUP], Flavio Olimpio Sanches Neto[SUP] 5 [/SUP], Elisângela de Paula Silveira Lacerda[SUP] 2 [/SUP]
Affiliations
- PMID: 35524091
- DOI: 10.1007/s11356-022-20609-z
Abstract
It is well known that the new coronavirus pandemic has global environmental, public health, and economic implications. In this sense, this study aims to monitor SARS-CoV-2 in the largest wastewater treatment plant of Goiânia, which processes wastewater from more than 700,000 inhabitants, and to correlate the molecular and clinical data collected. Influent and effluent samples were collected at Dr. Helio de Seixo Britto's wastewater treatment plant from January to August 2021. Viral concentration was performed with polyethylene glycol before viral RNA extraction. Real-time qPCR (N1 and N2 gene assays) was performed to detect and quantify the viral RNA present in the samples. The results showed that 43.63% of the samples were positive. There is no significant difference between the detection of primers N1 (mean 3.23 log10 genome copies/L, std 0.23) and N2 (mean 2.95 log10 genome copies/L, std 0.29); also, there is no significant difference between the detection of influent and effluent samples. Our molecular data revealed a positive correlation with clinical data, and infection prevalence was higher than clinical data. In addition, we developed a user-friendly web application to predict the number of infected people based on the detection of viral load present in wastewater samples and may be applied as a public policy strategy for monitoring ongoing outbreaks.
Keywords: COVID-19; Coronavirus; SARS-CoV-2; Surveillance; Wastewater-based epidemiology; Web application.