Cluster detection and risk estimation for spatio-temporal health data

In epidemiological disease mapping one aims to estimate the spatio-temporal pattern in disease risk and identify high-risk clusters, allowing health interventions to be appropriately targeted. Bayesian spatio-temporal models are used to estimate smoothed risk surfaces, but this is contrary to the aim of identifying groups of areal units that exhibit elevated risks compared with their neighbours. Therefore, in this paper we propose a new Bayesian hierarchical modelling approach for simultaneously estimating disease risk and identifying high-risk clusters in space and time. Inference for this model is based on Markov chain Monte Carlo simulation, using the freely available R package CARBayesST that has been developed in conjunction with this paper. Our methodology is motivated by two case studies, the first of which assesses if there is a relationship between Public health Districts and colon cancer clusters in Georgia, while the second looks at the impact of the smoking ban in public places in England on cardiovascular disease clusters

Soil Governance: Accessing Cross-Disciplinary Perspectives

Soil provides the foundation for agricultural and environmental systems, and are subject to a complex governance regime of property rights and secondary impacts from industry and domestic land use. Complex natural resource management issues require approaches to governance that acknowledge uncertainty and complexity. Theories of next generation environmental governance assume that inclusion of diverse perspectives will improve reform directions and encourage behaviour change. This paper reports on a qualitative survey of an international workshop that brought together cross-disciplinary perspectives to address the challenges of soil governance. Results reveal the challenges of communicating effectively across disciplines. The findings suggest that strategies for improved soils governance must focus on increasing communications with community stakeholders and engaging land managers in designing shared governance regimes. The need for more conscious articulation of the challenges of cross-disciplinary environments is discussed and strategies for increasing research collaboration in soils governance are suggested. The identified need for more systematic approaches to cross-disciplinary learning, including reporting back of cross-disciplinary initiatives to help practitioners learn from past experience, forms part of the rationale for this paper

Learning From Experience With Performance Assessment Frameworks for General Budget Support

This report provides the findings of a study financed by SECO and undertaken under the auspices of the OECD-DAC multi-country evaluation of General Budget Support (GBS). The overall objective was to gather preliminary lessons on what could be good international practice in the development\ud of Performance Assessment Frameworks (PAFs) for GBS. The study is based on the experience of three countries which have adopted harmonised PAFs – namely Ghana, Mozambique, and Tanzania, and two which are moving in this direction – Benin and Nicaragua. In order to assess the effectiveness of these PAFs, the study employed a simplified, standard framework reflecting the OECD-DAC guiding principles for the provision of budget support.\u

A Bayesian Hierarchical Model to Derive Novel Gene Networks from Gene Ontology Fingerprints

We developed a Bayesian hierarchical model to identify gene networks based on the similarity score generated from comparing the gene ontology fingerprints of gene pairs. Genes in this network were assumed to have similar biological functions that can be indicated by their ontology fingerprints. Our results indicate that different pathways show consistent score threshold that allow us to distinguish biological relevant gene—gene connections in the network

A Bayesian space–time model for clustering areal units based on their disease trends

Population-level disease risk across a set of non-overlapping areal units varies in space and time, and a large research literature has developed methodology for identifying clusters of areal units exhibiting elevated risks. However, almost no research has extended the clustering paradigm to identify groups of areal units exhibiting similar temporal disease trends. We present a novel Bayesian hierarchical mixture model for achieving this goal, with inference based on a Metropolis-coupled Markov chain Monte Carlo ((MC) 3 ) algorithm. The effectiveness of the (MC) 3 algorithm compared to a standard Markov chain Monte Carlo implementation is demonstrated in a simulation study, and the methodology is motivated by two important case studies in the United Kingdom. The first concerns the impact on measles susceptibility of the discredited paper linking the measles, mumps, and rubella vaccination to an increased risk of Autism and investigates whether all areas in the Scotland were equally affected. The second concerns respiratory hospitalizations and investigates over a 10 year period which parts of Glasgow have shown increased, decreased, and no change in risk

Analysis and optimisation of the basis set filtration algorithm

PhD ThesisThe ltration algorithm has recently been introduced as a way of increasing the speed of ab initio modelling calculations using Cartesian Gaussian basis functions. It works by developing a novel set of basis functions which are constructed specifically for the system being modelled. It has been implemented in the ab initio density functional theory based modelling package AIMPRO. The standard ltration process is found to be accurate when the ltration radius is increased to at least 10 Bohr radii in silicon. The standard ltration process uses all the basis functions centred on points inside a sphere centred on each atom in turn. By rejecting some of these functions (a trimming process), the ltration process can be speeded up, however there will be a resulting loss of accuracy. Three approaches to developing a ltered basis for an atom are considered, and compared. The most successful criterion for function trimming is found to be where functions are kept which exceed a threshold value on the surface of a sphere. Structural optimisation using ltration produce accurate nal structures, even when using parameters that give rise to poorly converged absolute energies. For the most time consuming elements of a calculation, a rapid ltration process is possible. However, very poor ltration thresholds introduce small inconsistencies between energies and forces, which can make optimisation difficult if algorithms are chosen that use both the energy and force. Algorithms that only use forces are implemented, and shown to be stable and produce accurate structures. This is further demonstrated using a new implementation of the Lanczos method for determining transition states. This is compared against the current AIMPRO method, the nudged elastic band. The new method is far superior in terms of speed, and offers greater stability towards the end of calculations

Bayesian Point Event Modeling in Spatial and Environmental Epidemiology: A Review

This paper reviews the current state of point event modeling in spatial epidemiology from a Bayesian perspective. Point event (or case event) data arise when geo-coded addresses of disease events are available. Often this level of spatial resolution would not be accessible due to medical confidentiality constraints. However, for the examination of small spatial scales it is important to be capable of examining point process data directly. Models for such data are usually formulated based on point process theory. In addition, special conditioning arguments can lead to simpler Bernoulli likelihoods and logistic spatial models. Goodness-of-fit diagnostics and Bayesian residuals are also considered. Applications within putative health hazard risk assessment, cluster detection, and linkage to environmental risk fields (misalignment) are considered

Author's Abstracts. The Geological Society of America, Cordilleran Section

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