Studies of the relationships between environmental exposures and adverse health outcomes often rely on a two-stage statistical modeling approach, where exposure is modeled/predicted in the first stage and used as input to a separately fit health outcome analysis in the second stage. Uncertainty in these predictions is frequently ignored, or accounted for in an overly simplistic manner when estimating the associations of interest. Working in the Bayesian setting, we propose a flexible kernel density estimation (KDE) approach for fully utilizing posterior output from the first stage modeling/prediction to make accurate inference on the association between exposure and health in the second stage, derive the full conditional distributions needed for efficient model fitting, detail its connections with existing approaches, and compare its performance through simulation. Our KDE approach is shown to generally have improved performance across several settings and model comparison metrics. Using competing approaches, we investigate the association between lagged daily ambient fine particulate matter levels and stillbirth counts in New Jersey (2011-2015), observing an increase in risk with elevated exposure 3 days prior to delivery. The newly developed methods are available in the R package KDExp.

/pdf/a-bayesian-framework-for-incorporating-exposure-uncertainty-19rfttsa.pdf

A Bayesian framework for incorporating exposure uncertainty into health analyses with application to air pollution and stillbirth.

Aims. Monte Carlo Radiative Transfer (MCRT) simulations are a powerful tool for understanding the role of dust in astrophysical systems and its inﬂuence on observations. However, due to the strong coupling of the radiation ﬁeld and medium across the whole computational domain, the problem is non-local and non-linear and such simulations are computationally expensive in case of re- alistic 3D inhomogeneous dust distributions. We explore a novel technique for post-processing MCRT output to reduce the total computational run time by enhancing the output of computationally less expensive simulations of lower-quality. Methods. We combine principal component analysis (PCA) and non-negative matrix factorization (NMF) as dimensionality reduction techniques together with Gaussian Markov random ﬁelds and the Integrated nested Laplace approximation (INLA), an approximate method for Bayesian inference, to detect and reconstruct the non-random spatial structure in the images of lower signal-to-noise or with missing data. Results. We test our methodology using synthetic observations of a galaxy from the SKIRT Auriga project - a suite of high resolution magneto-hydrodynamic Milky Way-sized galaxies simulated in cosmological environment by ’zoom-in’ technique. With this approach, we are able to reproduce high photon number reference images ∼ 5 times faster with median residuals below ∼ 20%.

/pdf/spatial-field-reconstruction-with-inla-application-to-2lv78i4c.pdf

Wenlong Gong

Papers

Multivariate spatial prediction of air pollutant concentrations with INLA