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Metrics to relate COVID-19 wastewater data to clinical testing dynamics

Authors :
Massachusetts Institute of Technology. Department of Biological Engineering
Xiao, Amy
Wu, Fuqing
Bushman, Mary
Zhang, Jianbo
Imakaev, Maxim
Chai, Peter R
Duvallet, Claire
Endo, Noriko
Erickson, Timothy B
Armas, Federica
Arnold, Brian
Chen, Hongjie
Chandra, Franciscus
Ghaeli, Newsha
Gu, Xiaoqiong
Hanage, William P
Lee, Wei Lin
Matus, Mariana
McElroy, Kyle A
Moniz, Katya
Rhode, Steven F
Thompson, Janelle
Alm, Eric J
Massachusetts Institute of Technology. Department of Biological Engineering
Xiao, Amy
Wu, Fuqing
Bushman, Mary
Zhang, Jianbo
Imakaev, Maxim
Chai, Peter R
Duvallet, Claire
Endo, Noriko
Erickson, Timothy B
Armas, Federica
Arnold, Brian
Chen, Hongjie
Chandra, Franciscus
Ghaeli, Newsha
Gu, Xiaoqiong
Hanage, William P
Lee, Wei Lin
Matus, Mariana
McElroy, Kyle A
Moniz, Katya
Rhode, Steven F
Thompson, Janelle
Alm, Eric J
Source :
Elsevier
Publication Year :
2023

Abstract

Wastewater surveillance has emerged as a useful tool in the public health response to the COVID-19 pandemic. While wastewater surveillance has been applied at various scales to monitor population-level COVID-19 dynamics, there is a need for quantitative metrics to interpret wastewater data in the context of public health trends. 24-hour composite wastewater samples were collected from March 2020 through May 2021 from a Massachusetts wastewater treatment plant and SARS-CoV-2 RNA concentrations were measured using RT-qPCR. The relationship between wastewater copy numbers of SARS-CoV-2 gene fragments and COVID-19 clinical cases and deaths varies over time. We demonstrate the utility of three new metrics to monitor changes in COVID-19 epidemiology: (1) the ratio between wastewater copy numbers of SARS-CoV-2 gene fragments and clinical cases (WC ratio), (2) the time lag between wastewater and clinical reporting, and (3) a transfer function between the wastewater and clinical case curves. The WC ratio increases after key events, providing insight into the balance between disease spread and public health response. Time lag and transfer function analysis showed that wastewater data preceded clinically reported cases in the first wave of the pandemic but did not serve as a leading indicator in the second wave, likely due to increased testing capacity, which allows for more timely case detection and reporting. These three metrics could help further integrate wastewater surveillance into the public health response to the COVID-19 pandemic and future pandemics.

Details

Database :
OAIster
Journal :
Elsevier
Notes :
application/pdf, English
Publication Type :
Electronic Resource
Accession number :
edsoai.on1370256389
Document Type :
Electronic Resource