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Improving Surveillance of Emerging RNA Viruses.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Improving Surveillance of Emerging RNA Viruses./
作者:	Petrone, Mary E.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
面頁冊數:	168 p.
附註:	Source: Dissertations Abstracts International, Volume: 83-08, Section: B.
Contained By:	Dissertations Abstracts International83-08B.
標題:	Epidemiology. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28770000
ISBN:	9798790628856

Improving Surveillance of Emerging RNA Viruses.
Petrone, Mary E.

Improving Surveillance of Emerging RNA Viruses. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 168 p.

Source: Dissertations Abstracts International, Volume: 83-08, Section: B.

Thesis (Ph.D.)--Yale University, 2021.

This item must not be sold to any third party vendors.

Effective surveillance of emerging RNA viruses is essential for public health and epidemiological research. Without identifying incident infections in an actionable time frame, the prevention of future infections becomes nearly impossible. This was exemplified at the beginning of the COVID-19 pandemic in the United States, during which time only a small fraction of infected individuals was detected through surveillance, and the virus promptly spread across the country. Similarly, parameterizing informative epidemiological models requires accurate data. In the absence of effective surveillance and/or a clear understanding of where gaps in the system exist, efforts to better understand the dynamics of circulating viruses become misguided or futile.Major challenges to improving surveillance methods are (1) the inherent interdisciplinary nature of the field and (2) limited available resources. Molecular drivers including host-pathogen interactions and pathogen evolution influence the burden of disease and the changing effectiveness of diagnostic tools used to detect new infections. For example, syndromic surveillance systems used in dengue-endemic regions disproportionately detect individuals experiencing secondary dengue infections. Statistical methods may be useful for inferring information that would otherwise be unobservable in such cases, and obtaining community buy-in is essential for ensuring the success of any surveillance program. To further investigate these challenges, we characterize surveillance gaps in arbovirus surveillance systems in the Dominican Republic and propose some solutions for closing these gaps in Chapter 1. Because implementing these solutions becomes increasingly difficult in resource-limited settings, we next develop systems that utilize low-cost components. We first validate the use of saliva as a diagnostic medium for SARS-CoV-2 testing. Compared to nasopharyngeal swabs, saliva collection is less invasive and is less affected by clinical supply-chain bottlenecks, which mitigates fluctuations in the cost of consumables. In Chapter 2, we evaluate the safety and effectiveness of unobserved saliva self-collection and find that participants were able to provide saliva samples for diagnostic testing without difficulty. By increasing access to diagnostic testing, we aim to build sustainable surveillance systems and promote health equity.The expanding scale of available genomic data, particularly in response to the COVID-19 pandemic, has also helped address some surveillance gaps and provided important insights into the emergence and spread of novel RNA viruses. When a viral population is sampled at sufficient density, it is possible to answer key epidemiological questions such as when and from where the virus was introduced into a community. In Chapter 3, we use phylogenetics and genomic epidemiology to demonstrate that the initial COVID-19 outbreak in Connecticut was likely caused by domestic spread of SARS-CoV-2. This discovery highlighted surveillance gaps in the early COVID-19 response in the United States, which assumed that incident cases were associated with international travel at that time. Since then, the duration of the COVID-19 pandemic has prompted more complex questions, especially in response to the emergence of more transmissible variants. Thus, in Chapter 4, we develop a framework that combines genomic and traditional epidemiological data and compares the relative fitness of cocirculating SARS-CoV-2 variants. We specifically designed this framework for routine implementation by minimizing the requisite computational demands and run time.In this dissertation, we identify and address key challenges associated with the improvement of surveillance systems for RNA viruses of public health significance. Specifically, we use a case study in the Dominican Republic to demonstrate the risks associated with relying on syndromic surveillance and surveillance systems closely tied to initial outbreaks of emerging viruses. We then propose practical solutions for closing surveillance gaps using low-cost and genomic methods. In sum, we argue that sustainable rather than reactionary surveillance systems promote public health, and we provide feasible methods for facilitating this transition.

ISBN: 9798790628856Subjects--Topical Terms:

568544
Epidemiology.
Subjects--Index Terms:

Surveillance

Improving Surveillance of Emerging RNA Viruses.
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520 $a Effective surveillance of emerging RNA viruses is essential for public health and epidemiological research. Without identifying incident infections in an actionable time frame, the prevention of future infections becomes nearly impossible. This was exemplified at the beginning of the COVID-19 pandemic in the United States, during which time only a small fraction of infected individuals was detected through surveillance, and the virus promptly spread across the country. Similarly, parameterizing informative epidemiological models requires accurate data. In the absence of effective surveillance and/or a clear understanding of where gaps in the system exist, efforts to better understand the dynamics of circulating viruses become misguided or futile.Major challenges to improving surveillance methods are (1) the inherent interdisciplinary nature of the field and (2) limited available resources. Molecular drivers including host-pathogen interactions and pathogen evolution influence the burden of disease and the changing effectiveness of diagnostic tools used to detect new infections. For example, syndromic surveillance systems used in dengue-endemic regions disproportionately detect individuals experiencing secondary dengue infections. Statistical methods may be useful for inferring information that would otherwise be unobservable in such cases, and obtaining community buy-in is essential for ensuring the success of any surveillance program. To further investigate these challenges, we characterize surveillance gaps in arbovirus surveillance systems in the Dominican Republic and propose some solutions for closing these gaps in Chapter 1. Because implementing these solutions becomes increasingly difficult in resource-limited settings, we next develop systems that utilize low-cost components. We first validate the use of saliva as a diagnostic medium for SARS-CoV-2 testing. Compared to nasopharyngeal swabs, saliva collection is less invasive and is less affected by clinical supply-chain bottlenecks, which mitigates fluctuations in the cost of consumables. In Chapter 2, we evaluate the safety and effectiveness of unobserved saliva self-collection and find that participants were able to provide saliva samples for diagnostic testing without difficulty. By increasing access to diagnostic testing, we aim to build sustainable surveillance systems and promote health equity.The expanding scale of available genomic data, particularly in response to the COVID-19 pandemic, has also helped address some surveillance gaps and provided important insights into the emergence and spread of novel RNA viruses. When a viral population is sampled at sufficient density, it is possible to answer key epidemiological questions such as when and from where the virus was introduced into a community. In Chapter 3, we use phylogenetics and genomic epidemiology to demonstrate that the initial COVID-19 outbreak in Connecticut was likely caused by domestic spread of SARS-CoV-2. This discovery highlighted surveillance gaps in the early COVID-19 response in the United States, which assumed that incident cases were associated with international travel at that time. Since then, the duration of the COVID-19 pandemic has prompted more complex questions, especially in response to the emergence of more transmissible variants. Thus, in Chapter 4, we develop a framework that combines genomic and traditional epidemiological data and compares the relative fitness of cocirculating SARS-CoV-2 variants. We specifically designed this framework for routine implementation by minimizing the requisite computational demands and run time.In this dissertation, we identify and address key challenges associated with the improvement of surveillance systems for RNA viruses of public health significance. Specifically, we use a case study in the Dominican Republic to demonstrate the risks associated with relying on syndromic surveillance and surveillance systems closely tied to initial outbreaks of emerging viruses. We then propose practical solutions for closing surveillance gaps using low-cost and genomic methods. In sum, we argue that sustainable rather than reactionary surveillance systems promote public health, and we provide feasible methods for facilitating this transition.
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