Tilman M. Davies

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Kernel smoothing is routinely used for the estimation of relative risk based on point locations of disease cases and sampled controls over a geographical region. Typically, fixed-bandwidth kernel estimation has been employed, despite the widely recognized problems experienced with this methodology when the underlying densities exhibit the type of spatial(More)
Kernel smoothing is a popular approach to estimating relative risk surfaces from data on the locations of cases and controls in geographical epidemiology. The interpretation of such surfaces is facilitated by plotting of tolerance contours which highlight areas where the risk is sufficiently high to reject the null hypothesis of unit relative risk.(More)
Researchers from Australia, New Zealand, Canada and the United States collaborated to validate their foot and mouth disease models--AusSpread, InterSpread Plus and the North American Animal Disease Spread Model--in an effort to build confidence in their use as decision-support tools. The final stage of this project involved using the three models to(More)
The kernel-smoothed density-ratio or 'relative risk' function for planar point data is a useful tool for examining disease rates over a certain geographical region. Instrumental to the quality of the resulting risk surface estimate is the choice of bandwidth for computation of the required numerator and denominator densities. The challenge associated with(More)
Highly pathogenic avian influenza (HPAI) subtype H5N1 is a trans-boundary animal disease that has crossed the animal-human species barrier and over the past decade has had a considerable impact on the poultry industry, wild bird populations and on human health. Understanding the spatio-temporal patterns of H5N1 outbreaks can provide visual clues to the(More)
Identification of high-risk regions of schistosomiasis is important for rational resource allocation and effective control strategies. We conducted the first study to apply the newly developed method of adaptive kernel density estimation (KDE)-based spatial relative risk function (sRRF) to detect the high-risk regions of schistosomiasis in the Guichi region(More)
This paper introduces an R package for spatial and spatio-temporal prediction and forecasting for log-Gaussian Cox processes. The main computational tool for these models is Markov chain Monte Carlo (MCMC) and the new package, lgcp, therefore also provides an extensible suite of functions for implementing MCMC algorithms for processes of this type. The(More)
Cadaver dissection is the first opportunity for many students to practice handling human tissue and is their first exposure to the occupational hazards involved with this task. Few studies examine dissection room injuries to ascertain the dangers associated with dissecting. We performed a retrospective cohort analysis of dissection room injuries from four(More)
Despite being commonly affected by degenerative disorders, there are few data on normal thoracic intervertebral disc dimensions. A morphometric analysis of adult thoracic intervertebral discs was, therefore, undertaken. Archival computed tomography scans of 128 recently deceased individuals (70 males, 58 females, 20–79 years) with no known spinal pathology(More)