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Reviews of "How early into the outbreak can surveillance of SARS-CoV-2 in wastewater tell us?"

Reviewers: Han Xia (Wuhan Institute of Virology) | 📗📗📗📗 ◻️ • Shivranjani Moharir, Rakesh Mishra (Centre for Cellular & Molecular Biology) | 📒📒📒 ◻️ ◻️

Published onOct 02, 2020
Reviews of "How early into the outbreak can surveillance of SARS-CoV-2 in wastewater tell us?"
key-enterThis Pub is a Review of
How early into the outbreak can surveillance of SARS-CoV-2 in wastewater tell us?
Description

There is increasing interest to use wastewater-based surveillance of SARS-CoV-2 as an early warning of the outbreak within a community. Despite successful detection of SARS-CoV-2 in wastewaters sampled from multiple locations, there is still no clear idea on the minimal number of cases needed in a community to result in a positive detection of the virus in wastewaters. To address this knowledge gap, we sampled wastewaters from a septic tank and biological activated sludge tank located on-site of a hospital. The hospital is providing treatment for SARS-CoV-2 infected patients, with the number of hospitalized patients per day known. It was observed that > 253 positive cases out of 10,000 persons are required prior to detecting SARS-CoV-2 in wastewater. There was a weak correlation between N1 and N2 gene abundances in wastewater with the number of hospitalized cases. This correlation was however not observed for N3 gene. The occurrence frequency of SARS-CoV-2 is at least 5 times lower in the partially treated wastewater than in the septic tank. Furthermore, abundance of N1 and N3 genes in the activated sludge tank were 50 and 70% of the levels detected in septic tank, suggesting poor persistence of the SARS-CoV-2 gene fragments in wastewater.

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Summary of Reviews: This study explores wastewater surveillance for monitoring COVID-19 outbreaks and identifies the case prevalence required for detecting infection in a hospital setting. The claims are somewhat supported by the data presented, but confounding variables limit policy applications.

Reviewer 1 (Han Xia) | 📗📗📗📗◻️

Reviewer 2 (Shivranjani Moharir, Rakesh Mishra) | 📒📒📒 ◻️◻️

RR:C19 Strength of Evidence Scale Key

📕 ◻️◻️◻️◻️ = Misleading

📙📙 ◻️◻️◻️ = Not Informative

📒📒📒 ◻️◻️ = Potentially Informative

📗📗📗📗◻️ = Reliable

📘📘📘📘📘 = Strong

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