Bayesian Hierarchical Framework for Occupational Hygiene Decision Making

Cardiovascular diseases (CVDs) are the leading cause of death globally and the number one cause of death globally. Over 75% of CVD deaths take place in low- and middle-income countries. Hence, comprehensive information about the spatio-temporal distribution of mortality due to cardio vascular disease is of interest. We fitted different spatio-temporal models within Bayesian hierarchical framework allowing different space-time interaction for mortality mapping with integrated nested Laplace approximations to analyze mortality data extracted from the health and demographic surveillance system in Kersa District in Hararege, Oromia Region, Ethiopia. The result indicates that non-parametric time trends models perform better than linear models. Among proposed models, one with non-parametric trend, type II interaction and second order random walk but without unstructured time effect was found to perform best according to our experience and. simulation study. An application based on real data revealed that, mortality due to CVD increased during the study period, while administrative regions in northern and south-eastern part of the study area showed a significantly elevated risk. The study highlighted distinct spatiotemporal clusters of mortality due to CVD within the study area. The study is a preliminary assessment step in prioritizing areas for further and more comprehensive research raising questions to be addressed by detailed investigation. Underlying contributing factors need to be identified and accurately quantified.

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Out of gas: quantifying fatigue in MLB relievers

Journal of Quantitative Analysis in Sports ◽

10.1515/jqas-2018-0007 ◽

2018 ◽

Vol 14 (2) ◽

pp. 57-64

Author(s):

Kyle Burris ◽

Jacob Coleman

Keyword(s):

Decision Making ◽

Hierarchical Model ◽

Major League Baseball ◽

Bayesian Hierarchical Model ◽

Response Relationship ◽

Dose Response Relationship ◽

Short Term ◽

Bayesian Hierarchical ◽

Rate Of Recovery ◽

The Relationship

Abstract As relief pitcher usage in Major League Baseball has spiked in recent years, optimal bullpen decision-making has become increasingly vital for team managers. Throughout the season, managers must be mindful to avoid overusing their most talented relievers, due to the risks of injury and ineffectiveness. Despite the substantial amount of attention given to pitcher arm health and injury prevention, the effect of workload on pitcher fatigue is poorly understood. As a result, many of these overuse decisions are driven by feel and intuition. In this paper, we borrow ideas from toxicology to provide a framework for estimating the effect of recent workload on short-term reliever effectiveness, as measured by fastball velocity. Treating a thrown pitch as a fatigue-inducing “toxin” administered to a player’s arm, we develop a Bayesian hierarchical model to estimate the pitcher-level dose-response relationship, the rate of recovery, and the relationship between pitch count and fatigue. Based on the model, we find that the rate of reliever fatigue rises with increasing pitch count. When relief pitchers throw more than 15 pitches in an appearance, they are expected to suffer small, short-term velocity decreases in future games; upon crossing the 20 pitch threshold, this dip is further amplified. For each day that passes after the appearance, we estimate that the effect on a player’s velocity is cut roughly in half. Finally, we identify the relievers most affected by fatigue, along with those most resilient to its effects.

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A Bayesian hierarchical approach to improve model parameter estimates and predictions of silage maize phenology in Germany

10.5194/egusphere-egu21-7962 ◽

2021 ◽

Author(s):

Michelle Viswanathan ◽

Tobias KD Weber ◽

Andreas Scheidegger ◽

Thilo Streck

Keyword(s):

Environmental Conditions ◽

Plant Phenology ◽

Parameter Estimates ◽

Crop Species ◽

Bayesian Hierarchical ◽

Crop Models ◽

Hierarchical Framework ◽

Silage Maize ◽

Parameterized Model ◽

Species Specific

Crop models are used to evaluate the impact of climate change on food security by simulating plant phenology, yield, biomass and leaf area index. Plant phenology defines the timing of crucial growth stages and physiological processes that influence organ appearance and assimilate partitioning. It is governed by environmental factors such as solar radiation, temperature and water availability. Plant phenology is not only specific for the crop species, but also depends on the cultivar. Additionally, growth of a cultivar could vary depending on the environment. Common crop models cannot fully capture the influence of the environment on phenology, resulting in cultivar-specific parameters that are environment-dependent. These parameter estimates may be unreliable in case of limited data. Moreover, crucial species-specific information is ignored. On the other hand, in large regional-scale models covering multiple cultivars and environments, information about the cultivars grown is generally not available. In this case, a shared set of parameters for the crop species would suppress within-species differences leading to unreliable predictions.A Bayesian hierarchical framework enables us to alleviate these problems by honouring the multi-level data structure. Additionally, we can reflect the uncertainty from different sources, for example, model inputs and measurements. In this study we implement a Bayesian hierarchical framework to estimate parameters of the Soil-Plant-Atmosphere System Simulation (SPASS) model for simulating phenological development of different cultivars of silage maize grown over all the contrasting climatological regions of Germany.We used data from the German weather service on the phenological development stages of silage maize grown across Germany between 2009 and 2019. During this period, silage maize was grown in over 3000 unique location-year combinations. Maize crops were differentiated into early, mid-early, mid-late and late ripening groups and were further classified into cultivars within each ripening group. Within the hierarchical framework, we estimate maize species-specific parameters as well as parameters per ripening group and cultivar, through Bayesian model calibration. We analyse the influence of environmental conditions on parameter estimates, to further develop the hierarchical structure. We perform cross-validation to assess the prediction quality of the parameterized model.With this approach, we show that robust parameter estimates account for differences between cultivars, ripening groups as well as different environmental conditions. The parameterized model can be used for large-scale phenology predictions of silage maize grown across Germany. These parameter estimates may perform better than independent species- or cultivar-specific estimates, in predicting phenology of future cultivars where specific cultivar characteristics are not known.

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Exposure Models for the Prior Distribution in Bayesian Decision Analysis for Occupational Hygiene Decision Making

Journal of Occupational and Environmental Hygiene ◽

10.1080/15459624.2012.748627 ◽

2013 ◽

Vol 10 (2) ◽

pp. 97-108 ◽

Cited By ~ 6

Author(s):

Eun Gyung Lee ◽

Seung Won Kim ◽

Charles E. Feigley ◽

Martin Harper

Keyword(s):

Decision Making ◽

Decision Analysis ◽

Prior Distribution ◽

Bayesian Decision ◽

Occupational Hygiene ◽

Exposure Models

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Prediction and decision making using Bayesian hierarchical models

Statistics in Medicine ◽

10.1002/sim.4780142002 ◽

1995 ◽

Vol 14 (20) ◽

pp. 2173-2190 ◽

Cited By ~ 23

Author(s):

Dalene K. Stangl

Keyword(s):

Decision Making ◽

Hierarchical Models ◽

Bayesian Hierarchical Models ◽

Bayesian Hierarchical

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Aggregating Distributional Treatment Effects: A Bayesian Hierarchical Analysis of the Microcredit Literature

10.31222/osf.io/7tkvm ◽

2017 ◽

Cited By ~ 7

Author(s):

Rachael Meager

Keyword(s):

Treatment Effects ◽

Randomized Trials ◽

Economic Knowledge ◽

Hierarchical Analysis ◽

Discrete Variables ◽

Unknown Parameters ◽

Distributional Effects ◽

Continuous Outcome ◽

Bayesian Hierarchical ◽

Hierarchical Framework

This paper develops methods to aggregate evidence on distributional treatment effects from multiple studies conducted in different settings, and applies them to the microcredit literature. Several randomized trials of expanding access to microcredit found substantial effects on the tails of household outcome distributions, but the extent to which these findings generalize to future settings was not known. Aggregating the evidence on sets of quantile effects poses additional challenges relative to average effects because distributional effects must imply monotonic quantiles and pass information across quantiles. Using a Bayesian hierarchical framework, I develop new models to aggregate distributional effects and assess their generalizability. For continuous outcome variables, the methodological challenges are addressed by applying transforms to the unknown parameters. For partially discrete variables such as business profits, I use contextual economic knowledge to build tailored parametric aggregation models. I find generalizable evidence that microcredit has negligible impact on the distribution of various household outcomes below the 75th percentile, but above this point there is no generalizable prediction.

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Development of a species distribution model for fin whales (Balaenoptera physalus) within a Bayesian hierarchical framework: Implications for uncertainty

10.7287/peerj.preprints.27424 ◽

2018 ◽

Author(s):

Douglas B Sigourney ◽

Samuel Chavez-Rosales ◽

Paul Conn ◽

Lance Garrison ◽

Elizabeth Josephson ◽

...

Keyword(s):

Species Distribution ◽

Species Distribution Model ◽

Distance Sampling ◽

Distribution Model ◽

Balaenoptera Physalus ◽

Density Estimates ◽

Bayesian Hierarchical ◽

Hierarchical Framework ◽

Final Density ◽

Fin Whales

Species distribution models (SDMs) have proven to be an integral tool in the conservation and management of cetaceans. Many applications have adopted a two-step approach where a detection function is estimated using conventional distance sampling in the first step and subsequently used as an offset to a density-habitat model in the second step. A drawback to this approach, hereafter referred to as the conventional species distribution model (CSDM), is the difficulty in propagating the uncertainty from the first step to the final density estimates. We describe a Bayesian hierarchical species distribution model (BHSDM) which has the advantage of simultaneously propagating multiple sources of uncertainty. Our framework includes 1) a mark-recapture distance sampling observation model that can accommodate two team line transect data, 2) an informed prior for surface availability 3) spatial smoothers using spline-like bases and 4) a compound Poisson-gamma likelihood which is a special case of the Tweedie distribution. We compare our approach to the CSDM method using a simulation study and a case study of fin whales (Balaenoptera physalus) off the East Coast of the USA. Simulations showed that the BHSDM method produced estimates with lower precision but with confidence interval coverage closer to the nominal 95% rate (94% for the BSHDM vs 85% for the CSDM). Results from the fin whale analysis showed that density estimates and predicted distribution patterns were largely similar among methods. Abundance estimates were also similar though modestly higher for the CSDM (4700, CV=0.13) than the BHSDM (4526, CV=0.26). Estimated sampling error differed substantially among the two methods where the average CV for density estimates from BHSDM method was approximately 3.5 times greater than estimates from the CSDM method. Successful wildlife management hinges on the ability to properly quantify uncertainty. Underestimates of uncertainty can result in ill-informed management decisions. Our results highlight the additional sampling uncertainty that is propagated in a hierarchical framework. Future applications of SDMs should consider techniques that allow all sources of error to be fully represented in final density predictions.

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