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This paper describes a generalised linear mixed model (GLMM) approach for understanding spatial patterns of participation in population health screening, in the presence of multiple screening facilities. The models presented have dual focus, namely the prediction of expected patient flows from regions to services and relative rates of participation by region- service combination, with both outputs having meaningful implications for the monitoring of current service uptake and provision. The novelty of this paper lies with the former focus, and an approach for distributing expected participation by region based on proximity to services is proposed. The modelling of relative rates of participation is achieved through the combination of different random effects, as a means of assigning excess participation to different sources. The methodology is applied to participation data collected from a government-funded mammography program in Brisbane, Australia.

In the past decade there has been strong interest in the special needs of overseas students attending Australian universities, with respect to teaching and learning. This paper reports on three action research studies that address the question of whether such issues remain in the teaching and learning of statistics in particular.

This paper deals with dynamic small enterprises which grow to achieve stock market quotation and comprises three main sections: a review of theoretical arguments concerning the effects of growth on small enterprises' financial characteristics; a consideration of the predictability of small enterprise failure and success; and an assessment of development of predictive models. Results of empirical work in the UK and Australia are reported. The first section of the paper deals with the development of a prior arguments concerning the effects of growth on financial characteristics. This links financial characteristics in the form of profitability, liquidity and leverage with stage of development of enterprises including that of growth. This includes consideration of the 'finance gap' and the effect of stock market flotation on small enterprises' financial characteristics. The purpose of this theorising is to make explicit what might be expected in terms of the effects of growth on the financial characteristics of small enterprises. The section concludes with an explanation of the methodology for the empiricial studies carried out at the University of New England on this topic. The second section of the paper deals with the predictability of small enterprise success and failure. This includes: consideration of the financial characteristics of dynamic small enterprises as they approach and go past stock market flotation; a review of previous work on predictive models in general and those for small enterprises in particular; and discussion of the issue of the problem of distinguishing between success and failure. The third section deals with the issues involved in the development of predictive models. These include: statistical and theoretical issues; the reliability of financial statement data; and the incorporation of non-financial variables. The conclusions consider the implications for relationships with providers of finance, particularly banks, and for owner-managers of the dynamic small enterprise. The finding that dynamic small enterprises have financial characteristics in common with bankrupt enterprises raises questions as to the effectiveness of credit scoring techniques which make use of accounting data. This in turn raises the prospect of small enterprises being denied finance and the creation of a new 'finance gap'. Finally, consideration given to the implications of the findings for financial institutions, small enterprise owner-managers and for future research.

Estimating potential health risks associated with recycled (reused) water is highly complex given the multiple factors affecting water quality. We take a conceptual model, which represents the factors and pathways by which recycled water may pose a risk of contracting gastroenteritis, convert the conceptual model to a Bayesian net, and quantify the model using one expert's opinion. This allows us to make various predictions as to the risks posed under various scenarios. Bayesian nets provide an additional way of modeling the determinants of recycled water quality and elucidating their relative influence on a given disease outcome. The important contribution to Bayesian net methodology is that all model predictions, whether risk or relative risk estimates, are expressed as credible intervals.

Abstract Background The impact of socio-environmental factors on suicide has been examined in many studies. Few of them, however, have explored these associations from a spatial perspective, especially in assessing the association between meteorological factors and suicide. This study examined the association of meteorological and socio-demographic factors with suicide across small areas over different time periods. Methods Suicide, population and socio-demographic data (e.g., population of Aboriginal and Torres Strait Islanders (ATSI), and unemployment rate (UNE) at the Local Government Area (LGA) level were obtained from the Australian Bureau of Statistics for the period of 1986 to 2005. Information on meteorological factors (rainfall, temperature and humidity) was supplied by Australian Bureau of Meteorology. A Bayesian Conditional Autoregressive (CAR) Model was applied to explore the association of socio-demographic and meteorological factors with suicide across LGAs. Results In Model I (socio-demographic factors), proportion of ATSI and UNE were positively associated with suicide from 1996 to 2000 (Relative Risk (RR) ATSI = 1.0107, 95% Credible Interval (CI): 1.0062-1.0151; RR UNE = 1.0187, 95% CI: 1.0060-1.0315), and from 2001 to 2005 (RR ATSI = 1.0126, 95% CI: 1.0076-1.0176; RR UNE = 1.0198, 95% CI: 1.0041-1.0354). Socio-Economic Index for Area (SEIFA) and IND, however, had negative associations with suicide between 1986 and 1990 (RR SEIFA = 0.9983, 95% CI: 0.9971-0.9995; RR ATSI = 0.9914, 95% CI: 0.9848-0.9980). Model II (meteorological factors): a 1°C higher yearly mean temperature across LGAs increased the suicide rate by an average by 2.27% (95% CI: 0.73%, 3.82%) in 1996–2000, and 3.24% (95% CI: 1.26%, 5.21%) in 2001–2005. The associations between socio-demographic factors and suicide in Model III (socio-demographic and meteorological factors) were similar to those in Model I; but, there is no substantive association between climate and suicide in Model III. Conclusions Proportion of Aboriginal and Torres Strait Islanders, unemployment and temperature appeared to be statistically associated with of suicide incidence across LGAs among all selected variables, especially in recent years. The results indicated that socio-demographic factors played more important roles than meteorological factors in the spatial pattern of suicide incidence.

Hospitals monitoring is becoming more complex and is increasing both because staff want their data analysed and because of increasing mandated surveillance. This book provides a suite of functions in R, enabling scientists and data analysts working in infection management and quality improvement departments in hospitals, to analyse their often non-independent data which is frequently in the form of trended, over-dispersed and sometimes auto-correlated time series; this is often difficult to analyse using standard office software. This book provides much-needed guidance on data analy

AbstractAppendicitis usually afflicts the young, but there is a large tail in the distribution of onset age. The genetics of this disease are still not well understood. A heritability analysis and genome wide linkage analysis of a large twin dataset was undertaken. Treating age of onset of appendicitis as a censored survival trait revealed a heritability of 0.21, and found evidence of linkage to Chromosome 1p37.3.

BACKGROUND: The global impact of COVID-19 and the country-specific responses to the pandemic provide an unparalleled opportunity to learn about different patterns of the outbreak and interventions. We model the global pattern of reported COVID-19 cases during the primary response period, with the aim of learning from the past to prepare for the future. METHODS: Using Bayesian methods, we analyse the response to the COVID-19 outbreak for 158 countries for the period 22 January to 9 June 2020. This encompasses the period in which many countries imposed a variety of response measures and initial relaxation strategies. Instead of modelling specific intervention types and timings for each country explicitly, we adopt a stochastic epidemiological model including a feedback mechanism on virus transmission to capture complex nonlinear dynamics arising from continuous changes in community behaviour in response to rising case numbers. We analyse the overall effect of interventions and community responses across diverse regions. This approach mitigates explicit consideration of issues such as period of infectivity and public adherence to government restrictions. RESULTS: Countries with the largest cumulative case tallies are characterised by a delayed response, whereas countries that avoid substantial community transmission during the period of study responded quickly. Countries that recovered rapidly also have a higher case identification rate and small numbers of undocumented community transmission at the early stages of the outbreak. We also demonstrate that uncertainty in numbers of undocumented infections dramatically impacts the risk of multiple waves. Our approach is also effective at pre-empting potential flare-ups. CONCLUSIONS: We demonstrate the utility of modelling to interpret community behaviour in the early epidemic stages. Two lessons learnt that are important for the future are: i) countries that imposed strict containment measures early in the epidemic fared better with respect to numbers of reported ...

In environmental monitoring, the ability to obtain high-quality data across space and time is often limited by the cost of purchasing, deploying and maintaining a large collection of equipment, and the employment of personnel to perform these tasks. An ideal design for a monitoring campaign would be dense enough in time to capture short-range variation at each site, long enough in time to examine trends at each site and across all sites, and dense enough in space to allow modelling of the relationship between the means at each of the sites. This paper outlines a methodology for semiparametric spatiotemporal modelling of data that is dense in time but sparse in space, obtained from a split panel design, the most feasible approach to covering space and time with limited equipment. The data are hourly averaged particle number concentration (PNC) and were collected as part of the International Laboratory for Air Quality and Health's Ultrafine Particles from Traffic Emissions and Children's Health (UPTECH) project. The panel design comprises two weeks of continuous measurements taken at each of a number of government primary schools in the Brisbane Metropolitan Area, with each school visited sequentially. The school data are augmented by data from long-term monitoring stations at three locations in Brisbane, Australia. The temporal part of the model explains daily and weekly cycles in PNC at the schools. The temporal variation is modelled hierarchically with a penalised random walk term common to all sites and a similar term accounting for the remaining temporal trend at each site. The modelling of temporal trends requires an acknowledgement that the observations are correlated rather than independent. At each school and long-term monitoring site, peaks in PNC can be attributed to the morning and afternoon rush hour traffic and new particle formation events. The spatial component of the model describes the school-to-school variation in mean PNC at each school and within each school ground. The spatial term in the model is derived from a stochastic partial differential equation and approximates a Gaussian process with a Gaussian Markov Random field. Fitting the model helps describe spatial and temporal variability at a subset of the UPTECH schools and the long-term monitoring sites, which can be used to estimate the exposure of school children to ultrafine particles. Parameter estimates and their uncertainty are computed in a computationally efficient approximate Bayesian inference environment, R-INLA.

For dose–response analysis in quantitative microbial risk assessment (QMRA), the exact beta‐Poisson model is a two‐parameter mechanistic dose–response model with parameters and , which involves the Kummer confluent hypergeometric function. Evaluation of a hypergeometric function is a computational challenge. Denoting as the probability of infection at a given mean dose d, the widely used dose–response model is an approximate formula for the exact beta‐Poisson model. Notwithstanding the required conditions and , issues related to the validity and approximation accuracy of this approximate formula have remained largely ignored in practice, partly because these conditions are too general to provide clear guidance. Consequently, this study proposes a probability measure Pr(0 < r < 1 | , ) as a validity measure (r is a random variable that follows a gamma distribution; and are the maximum likelihood estimates of α and β in the approximate model); and the constraint conditions for as a rule of thumb to ensure an accurate approximation (e.g., Pr(0 < r < 1 | , ) >0.99) . This validity measure and rule of thumb were validated by application to all the completed beta‐Poisson models (related to 85 data sets) from the QMRA community portal (QMRA Wiki). The results showed that the higher the probability Pr(0 < r < 1 | , ), the better the approximation. The results further showed that, among the total 85 models examined, 68 models were identified as valid approximate model applications, which all had a near perfect match to the corresponding exact beta‐Poisson model dose–response curve.

Quantitative microbial risk assessment (QMRA) is widely accepted for characterizing the microbial risks associated with food, water, and wastewater. Single‐hit dose‐response models are the most commonly used dose‐response models in QMRA. Denoting as the probability of infection at a given mean dose d, a three‐parameter generalized QMRA beta‐Poisson dose‐response model, , is proposed in which the minimum number of organisms required for causing infection, Kmin, is not fixed, but a random variable following a geometric distribution with parameter . The single‐hit beta‐Poisson model, , is a special case of the generalized model with Kmin = 1 (which implies ). The generalized beta‐Poisson model is based on a conceptual model with greater detail in the dose‐response mechanism. Since a maximum likelihood solution is not easily available, a likelihood‐free approximate Bayesian computation (ABC) algorithm is employed for parameter estimation. By fitting the generalized model to four experimental data sets from the literature, this study reveals that the posterior median estimates produced fall short of meeting the required condition of = 1 for single‐hit assumption. However, three out of four data sets fitted by the generalized models could not achieve an improvement in goodness of fit. These combined results imply that, at least in some cases, a single‐hit assumption for characterizing the dose‐response process may not be appropriate, but that the more complex models may be difficult to support especially if the sample size is small. The three‐parameter generalized model provides a possibility to investigate the mechanism of a dose‐response process in greater detail than is possible under a single‐hit model.