Metrics to relate COVID-19 wastewater data to clinical testing dynamics
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 co...
| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier BV
2023
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| Online Access: | https://hdl.handle.net/1721.1/147741 |
| _version_ | 1811092574616682496 |
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| author | 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 |
| author2 | Massachusetts Institute of Technology. Department of Biological Engineering |
| author_facet | 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 |
| author_sort | Xiao, Amy |
| collection | MIT |
| description | 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. |
| first_indexed | 2024-09-23T15:20:23Z |
| format | Article |
| id | mit-1721.1/147741 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T15:20:23Z |
| publishDate | 2023 |
| publisher | Elsevier BV |
| record_format | dspace |
| spelling | mit-1721.1/1477412023-01-27T03:07:26Z Metrics to relate COVID-19 wastewater data to clinical testing dynamics 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 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. 2023-01-26T17:41:17Z 2023-01-26T17:41:17Z 2022 2023-01-26T14:34:36Z Article http://purl.org/eprint/type/JournalArticle https://hdl.handle.net/1721.1/147741 Xiao, Amy, Wu, Fuqing, Bushman, Mary, Zhang, Jianbo, Imakaev, Maxim et al. 2022. "Metrics to relate COVID-19 wastewater data to clinical testing dynamics." Water Research, 212. en 10.1016/J.WATRES.2022.118070 Water Research Creative Commons Attribution-NonCommercial-NoDerivs License http://creativecommons.org/licenses/by-nc-nd/4.0/ application/pdf Elsevier BV Elsevier |
| spellingShingle | 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 Metrics to relate COVID-19 wastewater data to clinical testing dynamics |
| title | Metrics to relate COVID-19 wastewater data to clinical testing dynamics |
| title_full | Metrics to relate COVID-19 wastewater data to clinical testing dynamics |
| title_fullStr | Metrics to relate COVID-19 wastewater data to clinical testing dynamics |
| title_full_unstemmed | Metrics to relate COVID-19 wastewater data to clinical testing dynamics |
| title_short | Metrics to relate COVID-19 wastewater data to clinical testing dynamics |
| title_sort | metrics to relate covid 19 wastewater data to clinical testing dynamics |
| url | https://hdl.handle.net/1721.1/147741 |
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