Dataset Information

A Bayesian Monte Carlo approach for predicting the spread of infectious diseases.

ABSTRACT: In this paper, a simple yet interpretable, probabilistic model is proposed for the prediction of reported case counts of infectious diseases. A spatio-temporal kernel is derived from training data to capture the typical interaction effects of reported infections across time and space, which provides insight into the dynamics of the spread of infectious diseases. Testing the model on a one-week-ahead prediction task for campylobacteriosis and rotavirus infections across Germany, as well as Lyme borreliosis across the federal state of Bavaria, shows that the proposed model performs on-par with the state-of-the-art hhh4 model. However, it provides a full posterior distribution over parameters in addition to model predictions, which aides in the assessment of the model. The employed Bayesian Monte Carlo regression framework is easily extensible and allows for incorporating prior domain knowledge, which makes it suitable for use on limited, yet complex datasets as often encountered in epidemiology.

SUBMITTER: Stojanovic O

PROVIDER: S-EPMC6919583 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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A Bayesian Monte Carlo approach for predicting the spread of infectious diseases.

Stojanović Olivera O Leugering Johannes J Pipa Gordon G Ghozzi Stéphane S Ullrich Alexander A

PloS one 20191218 12

In this paper, a simple yet interpretable, probabilistic model is proposed for the prediction of reported case counts of infectious diseases. A spatio-temporal kernel is derived from training data to capture the typical interaction effects of reported infections across time and space, which provides insight into the dynamics of the spread of infectious diseases. Testing the model on a one-week-ahead prediction task for campylobacteriosis and rotavirus infections across Germany, as well as Lyme b ...[more]

PMID: 31851680

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Project description:Current CT dosimetry employing CTDI methodology has come under fire in recent years, partially in response to the increasing width of collimated x-ray fields in modern CT scanners. This study was conducted to provide a better understanding of the radiation dose distributions in CT.Monte Carlo simulations were used to evaluate radiation dose distributions along the z axis arising from CT imaging in cylindrical phantoms. Mathematical cylinders were simulated with compositions of water, polymethyl methacrylate (PMMA), and polyethylene. Cylinder diameters from 10 to 50 cm were studied. X-ray spectra typical of several CT manufacturers (80, 100, 120, and 140 kVp) were used. In addition to no bow tie filter, the head and body bow tie filters from modern General Electric and Siemens CT scanners were evaluated. Each cylinder was divided into three concentric regions of equal volume such that the energy deposited is proportional to dose for each region. Two additional dose assessment regions, central and edge locations 10 mm in diameter, were included for comparisons to CTDI100 measurements. Dose spread functions (DSFs) were computed for a wide number of imaging parameters.DSFs generally exhibit a biexponential falloff from the z=0 position. For a very narrow primary beam input (<< 1 mm), DSFs demonstrated significant low amplitude long range scatter dose tails. For body imaging conditions (30 cm diameter in water), the DSF at the center showed 160 mm at full width at tenth maximum (FWTM), while at the edge the FWTM was approximately 80 mm. Polyethylene phantoms exhibited wider DSFs than PMMA or water, as did higher tube voltages in any material. The FWTM were 80, 180, and 250 mm for 10, 30, and 50 cm phantom diameters, respectively, at the center in water at 120 kVp with a typical body bow tie filter. Scatter to primary dose ratios (SPRs) increased with phantom diameter from 4 at the center (1 cm diameter) for a 16 cm diameter cylinder to approximately 12.5 for a 32 cm diameter cylinder. The SPRs increased dramatically at the center of the phantom compared to the edge. For the three equal area regions, the edge to center SPRs for a 32 cm diameter phantom were approximately 1.8, 3.5, and 6.3, respectively.DSFs demonstrate low amplitude long ranging tails which reach considerable distances in cylindrical phantoms. The buildup that results from these long-ranged tails increases at the center of the field (at z=0) with increasing scan length. The DSF distributions lend a better understanding of the trends in CT dose deposition over a range of relevant imaging parameters. The DSFs as well as other related data are available to interested parties using EPAPS at http://www.aip.org/pubservs/epaps.html.

Project description:High-throughput gene expression data are often obtained from pure or complex (heterogeneous) biological samples. In the latter case, data obtained are a mixture of different cell types and the heterogeneity imposes some difficulties in the analysis of such data. In order to make conclusions on gene expresssion data obtained from heterogeneous samples, methods such as microdissection and flow cytometry have been employed to physically separate the constituting cell types. However, these manual approaches are time consuming when measuring the responses of multiple cell types simultaneously. In addition, exposed samples, on many occasions, end up being contaminated with external perturbations and this may result in an altered yield of molecular content. In this paper, we model the heterogeneous gene expression data using a Bayesian framework, treating the cell type proportions and the cell-type specific expressions as the parameters of the model. Specifically, we present a novel sequential Monte Carlo (SMC) sampler for estimating the model parameters by approximating their posterior distributions with a set of weighted samples. The SMC framework is a robust and efficient approach where we construct a sequence of artificial target (posterior) distributions on spaces of increasing dimensions which admit the distributions of interest as marginals. The proposed algorithm is evaluated on simulated datasets and publicly available real datasets, including Affymetrix oligonucleotide arrays and national center for biotechnology information (NCBI) gene expression omnibus (GEO), with varying number of cell types. The results obtained on all datasets show a superior performance with an improved accuracy in the estimation of cell type proportions and the cell-type specific expressions, and in addition, more accurate identification of differentially expressed genes when compared to other widely known methods for blind decomposition of heterogeneous gene expression data such as Dsection and the nonnegative matrix factorization (NMF) algorithms. MATLAB implementation of the proposed SMC algorithm is available to download at https://github.com/moyanre/smcgenedeconv.git.

Dataset Information

A Bayesian Monte Carlo approach for predicting the spread of infectious diseases.

Publications

A Bayesian Monte Carlo approach for predicting the spread of infectious diseases.

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