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Surveillance of SARS-CoV-2: From Sew...
~
Xiao, Amy.
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Surveillance of SARS-CoV-2: From Sewage to the Clinic.
紀錄類型:
書目-電子資源 : Monograph/item
正題名/作者:
Surveillance of SARS-CoV-2: From Sewage to the Clinic./
作者:
Xiao, Amy.
出版者:
Ann Arbor : ProQuest Dissertations & Theses, : 2023,
面頁冊數:
147 p.
附註:
Source: Dissertations Abstracts International, Volume: 85-10, Section: B.
Contained By:
Dissertations Abstracts International85-10B.
標題:
Environmental health. -
電子資源:
https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31091663
ISBN:
9798381957853
Surveillance of SARS-CoV-2: From Sewage to the Clinic.
Xiao, Amy.
Surveillance of SARS-CoV-2: From Sewage to the Clinic.
- Ann Arbor : ProQuest Dissertations & Theses, 2023 - 147 p.
Source: Dissertations Abstracts International, Volume: 85-10, Section: B.
Thesis (Ph.D.)--Massachusetts Institute of Technology, 2023.
Early work has shown that wastewater surveillance could detect SARS-CoV-2 before it became widespread in a population, and that viral levels in wastewater largely paralleled local increases in clinical cases of COVID-19. These studies highlight the potential of wastewater surveillance to provide early warning of emerging outbreaks - as well as the need for improved modeling and quantitation to understand the relationship between viral concentrations in wastewater and active infections in the population. The work described in this thesis seeks to address this need by exploring this relationship at different temporal and spatial scales through a combination of experimental observation and computational modeling. First, we model wastewater SARS-CoV-2 RNA concentrations from the greater Boston area in conjunction with clinical case counts reported for the state of Massachusetts. We find that wastewater SARS-CoV-2 concentrations foreshadow new clinical case counts by 4-10 days, and that wastewater signals are most likely driven by a period of high fecal viral shedding early in infection. Next, we develop quantitative metrics that combine wastewater and clinical data in order to improve the interpretability of wastewater data for making public health decisions. We apply these metrics to the first and second waves of COVID-19 in the greater Boston area and find that wastewater was a leading indicator of clinical cases in the first but not the second wave, likely due to changes in clinical testing capacity. Finally, we describe the development of a campus-wide wastewater surveillance system. We find that wastewater surveillance is often sensitive to single-case resolution, and that student cases are more reliably detected compared to contractors and vendors. We also find notable examples where we observe strongly positive signals in wastewater that are not identified by clinical testing, which poses challenges to administrative action. Overall, we demonstrate the strengths and limitations of wastewater surveillance in the MIT dorms compared to twice a week nasal swab PCR. In all, the work presented in this thesis demonstrates the value of integrating wastewater surveillance with clinical case data to make public health decisions and highlights the avenues for future development of this rapidly expanding field.
ISBN: 9798381957853Subjects--Topical Terms:
543032
Environmental health.
Subjects--Index Terms:
Wastewater surveillance
Surveillance of SARS-CoV-2: From Sewage to the Clinic.
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Early work has shown that wastewater surveillance could detect SARS-CoV-2 before it became widespread in a population, and that viral levels in wastewater largely paralleled local increases in clinical cases of COVID-19. These studies highlight the potential of wastewater surveillance to provide early warning of emerging outbreaks - as well as the need for improved modeling and quantitation to understand the relationship between viral concentrations in wastewater and active infections in the population. The work described in this thesis seeks to address this need by exploring this relationship at different temporal and spatial scales through a combination of experimental observation and computational modeling. First, we model wastewater SARS-CoV-2 RNA concentrations from the greater Boston area in conjunction with clinical case counts reported for the state of Massachusetts. We find that wastewater SARS-CoV-2 concentrations foreshadow new clinical case counts by 4-10 days, and that wastewater signals are most likely driven by a period of high fecal viral shedding early in infection. Next, we develop quantitative metrics that combine wastewater and clinical data in order to improve the interpretability of wastewater data for making public health decisions. We apply these metrics to the first and second waves of COVID-19 in the greater Boston area and find that wastewater was a leading indicator of clinical cases in the first but not the second wave, likely due to changes in clinical testing capacity. Finally, we describe the development of a campus-wide wastewater surveillance system. We find that wastewater surveillance is often sensitive to single-case resolution, and that student cases are more reliably detected compared to contractors and vendors. We also find notable examples where we observe strongly positive signals in wastewater that are not identified by clinical testing, which poses challenges to administrative action. Overall, we demonstrate the strengths and limitations of wastewater surveillance in the MIT dorms compared to twice a week nasal swab PCR. In all, the work presented in this thesis demonstrates the value of integrating wastewater surveillance with clinical case data to make public health decisions and highlights the avenues for future development of this rapidly expanding field.
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