Optimization of solid tank design for fan-beam optical CT based 3D radiation dosimetry.

Optical computed tomography (CT) is one of the leading modalities for imaging gel dosimeters for 3D radiation dosimetry. There exist multiple scanner designs that have showcased excellent 3D dose verification capabilities of optical CT gel dosimetry. However, due to multiple experimental and reconstruction based factors there is currently no single scanner that has become a preferred standard. A significant challenge with setup and maintenance can be attributed to maintaining a large refractive index bath (1-15 l). In this work, a prototype solid 'tank' optical CT scanner is proposed that minimizes the volume of refractive index bath to between 10 and 35 ml. A ray-path simulator was created to optimize the design such that the solid tank geometry maximizes light collection across the detector array, maximizes the volume of the dosimeter scanned, and maximizes the collected signal dynamic range. An objective function was created to score possible geometries, and was optimized to find a local maximum geometry score from a set of possible design parameters. The design parameters optimized include the block length x bl , bore position x bc , fan-laser position x lp , lens block face semi-major axis length x ma , and the lens block face eccentricity x be . For the proposed design it was found that each of these parameters can have a significant effect on the signal collection efficacy within the scanner. Simulations scores are specific to the attenuation characteristics and refractive index of a simulated dosimeter. It was found that for a FlexyDos3D dosimeter, the ideal values for each of the five variables were: x bl = 314 mm, x bc = 6.5 mm, x lp = 50 mm, x ma = 66 mm, and x be = 0. In addition, a ClearView™ dosimeter was found to have ideal values at: x bl = 204 mm, x bc = 13 mm, x lp = 58 mm, x ma = 69 mm, and x be = 0. The ray simulator can also be used for further design and testing of new, unique and purpose-built optical CT geometries.

Clustering of and risk factors for the porcine high fever disease in a region of Vietnam.

Porcine high fever disease (PHFD) emerged in 2006 in China and spread to Vietnam. Little work has been carried out to investigate PHFD risk factors and space-time dynamics. To fill this gap, we investigated probable cases of PHFD at household level as the outcome. A study area, approximately 100 sq. km, was selected from a province of southern Vietnam that had reported the outbreak of PHFD in 2008. A survey was conducted in the study area to collect information about swine health problems during 2008. The questionnaire included three sections: general information, clinical signs of disease in pigs and production factors believed to be risk factors. Cases were defined at the household level and included interpretation of clinical signs in series. Logistic regression with a random intercept at the hamlet level was used to assess risk factors for PHFD at the household level. Spatial clustering was investigated using the D-function and a Cuzick-Edward's test. Spatial clusters were evaluated using a spatial relative risk surface and the spatial scan statistic using a Bernoulli model. Space-time clustering was explored using a space-time K-function and Knox's test. Space-time clusters were evaluated using a space-time permutation model in SaTScan. Of 955 households with questionnaire data, 33.4% were classified as cases. The statistical significance of space and space-time clustering differed between methods employed. The risk factors associated with occurrence of cases were higher numbers of sows and finishing pigs (log 2 transformed), receiving pigs from an external source and the interaction between using 'water green crop' (WGC) as pig feed and owning ducks with or without direct contact with pigs. The interaction between the presence of ducks and feeding WGC to pigs suggested the involvement of pathogens that might be present in water (environment) and could further replicate in or on ducks.

Goodness-of-fit measures for individual-level models of infectious disease in a Bayesian framework.

In simple models there are a variety of tried and tested ways to assess goodness-of-fit. However, in complex non-linear models, such as spatio-temporal individual-level models, less research has been done on how best to ascertain goodness-of-fit. Often such models are fitted within a Bayesian statistical framework, since such a framework is ideally placed to account for the many areas of data uncertainty. Within a Bayesian context, a major tool for assessing goodness-of-fit is the posterior predictive distribution. That is, a distribution for a test statistic is found through simulation from the posterior distribution and then compared with the observed test statistic for the data. Here, we examine different test statistics and ascertain how well they can detect model misspecification via a simulation study.