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Risk, Uncertainty, and Decision Making : = Insights from Ambient PM2.5 and COVID-19 Case Studies.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Risk, Uncertainty, and Decision Making :/
Reminder of title:	Insights from Ambient PM2.5 and COVID-19 Case Studies.
Author:	Colonna, Kyle J.
Description:	1 online resource (161 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 84-12, Section: B.
Contained By:	Dissertations Abstracts International84-12B.
Subject:	Environmental health. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30489536click for full text (PQDT)
ISBN:	9798379603892

Risk, Uncertainty, and Decision Making : = Insights from Ambient PM2.5 and COVID-19 Case Studies.
Colonna, Kyle J.

Risk, Uncertainty, and Decision Making :Insights from Ambient PM2.5 and COVID-19 Case Studies. - 1 online resource (161 pages)

Source: Dissertations Abstracts International, Volume: 84-12, Section: B.

Thesis (Ph.D.)--Harvard University, 2023.

Includes bibliographical references

Risk assessment is the art and science of estimating risk based on the evidence which is currently available. It seeks to characterize the state of knowledge and, to the extent possible, produce probabilistic estimates of risk which provide decision makers with a sense of what is known and how well it is known. Such information allows decision makers to formally consider the tradeoffs between acting now based on imperfect information and delaying decisions to allow research studies (with the potential for reducing uncertainty) to be designed, conducted, and interpreted. Uncertainty may exist because of the unknowns that differ each time we run the same experiment (parameter or aleatory uncertainty) and/or because of more fundamental questions of basic science (model or epistemic uncertainty). In the first case, characterization of uncertainty using well-developed methods of frequentist statistics is relatively straightforward, objective, and uncontroversial. In the latter case, it becomes necessary to rely on formally elicited subjective judgments of leading experts in relevant fields.In chapter 1, we demonstrate in our review of the epidemiological evidence on mortality attributable to ambient fine particulate matter (PM2.5) exposure that the dominant uncertainties faced in efforts to estimate risk in understudied locations are epistemic in nature - e.g., what should we think about the differential toxicity of PM2.5 from various sources, the effect modification induced by various population characteristics, and what should we conclude about the sufficiency of evidence that the observed associations reflect causal relationships. In chapter 2, we utilized a method typically employed in structured expert judgment, the Classical Model, to assess the performance of probabilistic mortality forecasts (i.e., judgments) from COVID-19 models (i.e., experts). We concluded that this method has the potential to improve both public health decision making and modeling more generally. In chapter 3, we conducted a research study that aims to reduce the uncertainty inherent in the relationship between acute exposure to ambient PM2.5 and the risk for respiratory disease hospitalization in Kuwait, a region that is understudied and has very high PM2.5 concentration levels, in an effort to encourage actions to reduce exposure and minimize potential harm.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023

Mode of access: World Wide Web

ISBN: 9798379603892Subjects--Topical Terms:

543032
Environmental health.
Subjects--Index Terms:

Air qualityIndex Terms--Genre/Form:

542853
Electronic books.

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520 $a Risk assessment is the art and science of estimating risk based on the evidence which is currently available. It seeks to characterize the state of knowledge and, to the extent possible, produce probabilistic estimates of risk which provide decision makers with a sense of what is known and how well it is known. Such information allows decision makers to formally consider the tradeoffs between acting now based on imperfect information and delaying decisions to allow research studies (with the potential for reducing uncertainty) to be designed, conducted, and interpreted. Uncertainty may exist because of the unknowns that differ each time we run the same experiment (parameter or aleatory uncertainty) and/or because of more fundamental questions of basic science (model or epistemic uncertainty). In the first case, characterization of uncertainty using well-developed methods of frequentist statistics is relatively straightforward, objective, and uncontroversial. In the latter case, it becomes necessary to rely on formally elicited subjective judgments of leading experts in relevant fields.In chapter 1, we demonstrate in our review of the epidemiological evidence on mortality attributable to ambient fine particulate matter (PM2.5) exposure that the dominant uncertainties faced in efforts to estimate risk in understudied locations are epistemic in nature - e.g., what should we think about the differential toxicity of PM2.5 from various sources, the effect modification induced by various population characteristics, and what should we conclude about the sufficiency of evidence that the observed associations reflect causal relationships. In chapter 2, we utilized a method typically employed in structured expert judgment, the Classical Model, to assess the performance of probabilistic mortality forecasts (i.e., judgments) from COVID-19 models (i.e., experts). We concluded that this method has the potential to improve both public health decision making and modeling more generally. In chapter 3, we conducted a research study that aims to reduce the uncertainty inherent in the relationship between acute exposure to ambient PM2.5 and the risk for respiratory disease hospitalization in Kuwait, a region that is understudied and has very high PM2.5 concentration levels, in an effort to encourage actions to reduce exposure and minimize potential harm.
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