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1. Static Risk-Based Group Testing Schemes Under Imperfectly Observable Risk

   https://doi.org/10.1287/stsy.2019.0059  

   Aprahamian, Hrayer ; Bish, Ebru K. ; Bish, Douglas R. ( December 2020 , Stochastic Systems)

   null (Ed.)

   Testing multiple subjects within a group, with a single test applied to the group (i.e., group testing), is an important tool for classifying populations as positive or negative for a specific binary characteristic in an efficient manner. We study the design of easily implementable, static group testing schemes that take into account operational constraints, heterogeneous populations, and uncertainty in subject risk, while considering classification accuracy- and robustness-based objectives. We derive key structural properties of optimal risk-based designs and show that the problem can be formulated as network flow problems. Our reformulation involves computationally expensive high-dimensional integrals. We develop an analytical expression that eliminates the need to compute high-dimensional integrals, drastically improving the tractability of constructing the underlying network. We demonstrate the impact through a case study on chlamydia screening, which leads to the following insights: (1) Risk-based designs are shown to be less expensive, more accurate, and more robust than current practices. (2) The performance of static risk-based schemes comprised of only two group sizes is comparable to those comprised of many group sizes. (3) Static risk-based schemes are an effective alternative to more complicated dynamic schemes. (4) An expectation-based formulation captures almost all benefits of a static risk-based scheme. 

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2. A data-driven risk assessment of Arctic maritime incidents: Using machine learning to predict incident types and identify risk factors

   https://doi.org/10.1016/j.ress.2023.109779  

   Kandel, Rajesh ; Baroud, Hiba ( March 2024 , Reliability Engineering & System Safety)
3. Mapping high-risk clusters and identifying place-based risk factors of mental health burden in pregnancy

   https://doi.org/10.1016/j.ssmmh.2023.100270  

   Ulrich, Sarah E. ; Sugg, Margaret M. ; Ryan, Sophia C. ; Runkle, Jennifer D. ( December 2023 , SSM - Mental Health)
4. The Association between Risk Perception and the Risk-Taking Behaviors of Construction Workers

   https://doi.org/10.1061/9780784481288.042  

   Dao, Bac ; Hasanzadeh, Sogand ; Esmaeili, Behzad ( March 2018 , Construction Research Congress 2018)

   The majority of human-factor models in construction safety assume that risk-taking behaviors, failure to perceive hazards, or misinterpreting the associated risks of hazards are the main contributing factors in accident occurrences. However, the findings for the link between risk-taking behaviors and risk perception are inconsistent. To address this knowledge gap, the current study focuses on measuring the association between risk perception and the risk-taking behaviors of construction workers. To achieve this objective, 27 undergraduate students from the University of Nebraska–Lincoln with at least 1 year of experience in the construction industry were recruited to participate in an experiment. To measure risk perception, the subjects were asked to assess the risk—in terms of likelihood and severity—associated with various scenario statements related to fall hazards. Subsequently, subjects performed the balloon analogue risk task (BART), a computerized decision-making simulation, to test the subjects’ risk-taking behaviors. The results of a correlational analysis showed that there is a significant negative association between an individual’s risk perception of fall hazards and his/her risk-taking behaviors. Additionally, differences in the risk-taking behaviors of subjects evaluated against their risk-perception scores were examined using a permutation simulation analysis. The results showed that there is a moderately significant difference in the risk-taking behaviors of subjects with low and high fall-risk perception. The research findings provide empirical evidence that people with lower risk perception tend to engage in more risk-taking behaviors. Furthermore, this study is one of the first attempts at using BART in the assessment of risk taking in construction safety and paves the way for a better understanding the human factors that contribute to construction accidents. 

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5. Modeling the relative risk of SARS-CoV-2 infection to inform risk-cost-benefit analyses of activities during the SARS-CoV-2 pandemic

   https://doi.org/10.1371/journal.pone.0245381  

   McCarthy, John E. ; Dewitt, Barry D. ; Dumas, Bob A. ; McCarthy, Myles T. ( January 2021 , PLOS ONE)

   Linkov, Igor (Ed.)

   Risk-cost-benefit analysis requires the enumeration of decision alternatives, their associated outcomes, and the quantification of uncertainty. Public and private decision-making surrounding the COVID-19 pandemic must contend with uncertainty about the probability of infection during activities involving groups of people, in order to decide whether that activity is worth undertaking. We propose a model of SARS-CoV-2 infection probability that can produce estimates of relative risk of infection for diverse activities, so long as those activities meet a list of assumptions, including that they do not last longer than one day (e.g., sporting events, flights, concerts), and that the probability of infection among possible routes of infection (i.e., droplet, aerosol, fomite, and direct contact) are independent. We show how the model can be used to inform decisions facing governments and industry, such as opening stadiums or flying on airplanes; in particular, it allows for estimating the ranking of the constituent components of activities (e.g., going through a turnstile, sitting in one’s seat) by their relative risk of infection, even when the probability of infection is unknown or uncertain. We prove that the model is a good approximation of a more refined model in which we assume infections come from a series of independent risks. A linearity assumption governing several potentially modifiable risks factors—such as duration of the activity, density of participants, and infectiousness of the attendees—makes interpreting and using the model straightforward, and we argue that it does so without significantly diminishing the reliability of the model. 

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