Monte Carlo Modeling of Time‐Dependent Exposures Using a Microexposure Event Approach

Price, Paul S.; Curry, Cynthia L.; Goodrum, Philip E.; Gray, Michael N.; McCrodden, Jane I.; Harrington, Natalie W.; Carlson-Lynch, Heather; Keenan, Russell E.

doi:10.1111/j.1539-6924.1996.tb01468.x

Cited by 27 publications

(5 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A different average yearly dose is calculated for each year of the exposure interval; these yearly doses are then summed and averaged over the exposure interval to give an average daily dose received over that interval. Unlike microexposure event modeling (Price et al, 1996), which tracks an individual's daily exposure over that individual's life span, the ODEQ model estimates an individual's average exposure over a randomly selected exposure interval. The model was constructed using Microsoft ® Excel 7 for spreadsheet functions, Microsoft ® Visual Basic for custom function generation, and Crystal Ball ® (version 4.0c, 32 bit architecHuman and Ecological Risk Assessment: An International Journal 1999.5:785-808. ture), with Latin Hypercube sampling, for Monte Carlo processing and distribution accumulation (ODEQ, 1998).…”

Section: Age-and Gender-based Exposure Modelmentioning

confidence: 99%

Distributions Selected for Use in Probabilistic Human Health Risk Assessments in Oregon

Hope

1999

Human and Ecological Risk Assessment: An International Journal

View full text Add to dashboard Cite

In 1995, Oregon enacted amendments to its state Cleanup Law that emphasize risk-based remedial action decisions and allow a responsible party to conduct probabilistic human health risk assessments. This change required selection and/or development of probability density functions for exposure factors frequently used in human health risk assessments. Methods used to obtain distributions for body weight, soil, water, vegetable/fruit, fish, and animal product ingestion, soil adherence, daily inhalation rate, various event and exposure frequencies, and exposure duration are described. Primary data sources were U.S. Environmental Protection Agency guidance and peer-reviewed scientific literature. These distributions of exposure factors may be used, in conjunction with a probabilistic age-and gender-based model, to calculate prospective exposures and risks. A brief overview of this model, which handles temporal parameters (age, exposure frequency, exposure duration) in a manner substantially different from that typically used in deterministic assessments, is also provided. Oregon's development of an age/ gender-based exposure model, and its selection of exposure factor value distributions for that model, represents one of the first attempts to develop practical approaches to using probabilistic techniques in a hazardous waste regulatory program.

show abstract

Section: Age-and Gender-based Exposure Modelmentioning

confidence: 99%

Distributions Selected for Use in Probabilistic Human Health Risk Assessments in Oregon

Hope

1999

Human and Ecological Risk Assessment: An International Journal

View full text Add to dashboard Cite

show abstract

“…There has been a considerable paradigm change in risk assessment from a deterministic approach to a probabilistic approach over the past decade to better meet the demand of risk-based management decisions (Keenan et al 1994;Hattis and Burmaster 1994;Cullen 1994;Price et al 1995;Rai and Krewski 1998;Hamed 1999;Lunchick 2000;Rai et al 2002). The concept of risk is inherently probabilistic.…”

Section: Introductionmentioning

confidence: 99%

Uncertainty Analysis of Pesticide Residues in Drinking Water Risk Assessment

Tesfamichael

Kaluarachchi

2004

Human and Ecological Risk Assessment: An International Journal

View full text Add to dashboard Cite

Pesticide residues in drinking water can vary significantly from day to day. However, water quality monitoring performed under the Safe Drinking Water Act (SDWA) at most community water systems (CWSs) is typically limited to four data points per year over a few years. Due to this limited sampling, likely maximum residues may be underestimated in risk assessment. In this work, a statistical methodology is proposed to study two types of uncertainties in observed samples and their propagated effect in risk estimates. The methodology was demonstrated using data from 16 CWSs that have three independent databases of atrazine residue to estimate the uncertainty of risk in infants and children. The results showed that in 85% of the CWSs, chronic risks predicted with the proposed approach may be two-to four-folds higher than that predicted with the current approach, wheras intermediate risks may be two-to three-folds higher in 50% of the CWSs. In 12% of the CWSs, however, the proposed methodology showed a lower intermediate risk. A closed-form solution of propagated uncertainty was developed to demonstrate the number of years (seasons) of data and sampling frequency needed to reduce the uncertainty of risk estimates. In general, this methodology provided good insight into the importance of addressing uncertainty of observed water quality data and the need to predict likely maximum residues in risk assessment.

show abstract

“…The stochastic model developed in this paper was derived fusing principles of “microexposure” [ 22 , 23 ] modeling, agent-based modeling, and probabilistic modeling, which together are best suited to estimate individual-level COVID-19 exposure and infection risk. A daily workplace activity of an individual is parsed to elementary typical work environments.…”

Section: Introductionmentioning

confidence: 99%

Assessment of the COVID-19 infection risk at a workplace through stochastic microexposure modeling

Vecherin

Chang

Wells

et al. 2022

J Expo Sci Environ Epidemiol

View full text Add to dashboard Cite

Background The COVID-19 pandemic has a significant impact on economy. Decisions regarding the reopening of businesses should account for infection risks. Objective This paper describes a novel model for COVID-19 infection risks and policy evaluations. Methods The model combines the best principles of the agent-based, microexposure, and probabilistic modeling approaches. It takes into account specifics of a workplace, mask efficiency, and daily routines of employees, but does not require specific inter-agent rules for simulations. Likewise, it does not require knowledge of microscopic disease related parameters. Instead, the risk of infection is aggregated into the probability of infection, which depends on the duration and distance of every contact. The probability of infection at the end of a workday is found using rigorous probabilistic rules. Unlike previous models, this approach requires only a few reference data points for calibration, which are more easily collected via empirical studies. Results The application of the model is demonstrated for a typical office environment and for a real-world case. Conclusion The proposed model allows for effective risk assessment and policy evaluation when there are large uncertainties about the disease, making it particularly suitable for COVID-19 risk assessments.

show abstract

Monte Carlo Modeling of Time‐Dependent Exposures Using a Microexposure Event Approach

Cited by 27 publications

References 15 publications

Distributions Selected for Use in Probabilistic Human Health Risk Assessments in Oregon

Distributions Selected for Use in Probabilistic Human Health Risk Assessments in Oregon

Uncertainty Analysis of Pesticide Residues in Drinking Water Risk Assessment

Assessment of the COVID-19 infection risk at a workplace through stochastic microexposure modeling

Contact Info

Product

Resources

About