Bayesian modelling of recurrent pipe failures in urban water systems using non-homogeneous Poisson processes with latent structure

Recurrent events are very common in a wide range of scientific disciplines. The majority of statistical models developed to characterise recurrent events are derived from either reliability theory or survival analysis. This thesis concentrates on applications that arise from reliability, which in general involve the study about components or devices where the recurring event is failure. Specifically, interest lies in repairable components that experience a number of failures during their lifetime. The goal is to develop statistical models in order to gain a good understanding about the driving force behind the failures. A particular counting process is adopted, the non-homogenous Poisson process (NHPP), where the rate of occurrence (failure rate) depends on time. The primary application considered in the thesis is the prediction of underground water pipe bursts although the methods described have more general scope. First, a Bayesian mixed effects NHPP model is developed and applied to a network of water pipes using MCMC. The model is then extended to a mixture of NHPPs. Further, a special mixture case, the zero-inflated NHPP model is developed to cope with data involving a large number of pipes that have never failed. The zero-inflated model is applied to the same pipe network. Quite often, data involving recurrent failures over time, are aggregated where for instance the times of failures are unknown and only the total number of failures are available. Aggregated versions of the NHPP model and its zero-inflated version are developed to accommodate aggregated data and these are applied to the aggregated version of the earlier data set. Complex devices in random environments often exhibit what may be termed as state changes in their behaviour. These state changes may be caused by unobserved and possibly non-stationary processes such as severe weather changes. A hidden semi-Markov NHPP model is formulated, which is a NHPP process modulated by an unobserved semi-Markov process. An algorithm is developed to evaluate the likelihood of this model and a Metropolis-Hastings sampler is constructed for parameter estimation. Simulation studies are performed to test implementation and finally an illustrative application of the model is presented. The thesis concludes with a general discussion and a list of possible generalisations and extensions as well as possible applications other than the ones considered

Spatio-temporal modelling of extreme storms

A flexible spatio-temporal model is implemented to analyse extreme extra-tropical cyclones objectively identified over the Atlantic and Europe in 6-hourly re-analyses from 1979-2009. Spatial variation in the extremal properties of the cyclones is captured using a 150 cell spatial regularisation, latitude as a covariate, and spatial random effects. The North Atlantic Oscillation (NAO) is also used as a covariate and is found to have a significant effect on intensifying extremal storm behaviour, especially over Northern Europe and the Iberian peninsula. Estimates of lower bounds on minimum sea-level pressure are typically 10-50 hPa below the minimum values observed for historical storms with largest differences occurring when the NAO index is positive.Comment: Published in at http://dx.doi.org/10.1214/14-AOAS766 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

The importance of spring atmospheric conditions for predictions of the Arctic summer sea ice extent

Recent studies have shown that atmospheric processes in spring play an important role for the initiation of the summer ice melt and therefore may strongly influence the September sea ice concentration (SSIC). Here a simple statistical regression model based on only atmospheric spring parameters is applied in order to predict the SSIC over the major part of the Arctic Ocean. By using spring anomalies of downwelling longwave radiation or atmospheric water vapor as predictor variables, correlation coefficients between observed and predicted SSIC of up to 0.5 are found. These skills of seasonal SSIC predictions are similar to those obtained using more complex dynamical forecast systems, despite the fact that the simple model applied here takes neither information of the sea ice state, oceanic conditions nor feedback mechanisms during summer into account. The results indicate that a realistic representation of spring atmospheric conditions in the prediction system plays an important role for the predictive skills of a model system.Swedish Research Council FORMA

Research note: Urban street tree density and antidepressant prescription rates—A cross-sectional study in London, UK

This is the author’s version of a work that was accepted for publication in Landscape and Urban Planning. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published at doi:10.1016/j.landurbplan.2014.12.005.Abstract: Growing evidence suggests an association between access to urban greenspace and mental health and wellbeing. Street trees may be an important facet of everyday exposure to nature in urban environments, but there is little evidence regarding their role in influencing population mental health. In this brief report, we raise the issue of street trees in the nature-health nexus, and use secondary data sources to examine the association between the density of street trees (trees/km street) in London boroughs and rates of antidepressant prescribing. After adjustment for potential confounders, and allowing for unmeasured area-effects using Bayesian mixed effects models, we find an inverse association, with a decrease of 1.18 prescriptions per thousand population per unit increase in trees per km of street (95% credible interval 0.00, 2.45). This study suggests that street trees may be a positive urban asset to decrease the risk of negative mental health outcomes.European Regional Development Fund Programme 2007 to 2013 and European Social Fund Convergence Programme for Cornwall and the Isles of Scill

The interactions between municipal socioeconomic status and age on hip fracture risk

The final publication is available at Springer via http://dx.doi.org/10.1007/s00198-014-2869-0SUMMARY: Age modifies the effect of area-level socioeconomic status (SES) in the risk of fragility hip fractures (HF). For older individuals, the risk of HF increases as SES increases. For younger individuals, risk of HF increases as SES decreases. Our study may help decision-makers to better direct the implementation of political decisions. INTRODUCTION: The effect of socioeconomic status (SES) on hip fracture (HF) incidence remains unclear. The objective of this study is to evaluate the association between HF incidence and municipality-level SES as well as interactions between age and SES. METHODS: From the Portuguese Hospital Discharge Database, we selected hospitalizations (2000-2010) of patients aged 50+, with HF diagnosis (codes 820.x, ICD9-CM), caused by traumas of low/moderate energy, excluding bone cancer cases and readmissions for aftercare. Municipalities were classified according to SES (deprived to affluent) using 2001 Census data. A spatial Bayesian hierarchical regression model (controlling for data heterogeneity and spatial autocorrelation), using the Poisson distribution, was used to quantify the relative risk (RR) of HF, 95% credible interval (95%CrI), and analyze the interaction between age and SES after adjusting for rural conditions. RESULTS: There were 96,905 HF, 77.3% of which were on women who, on average, were older than men (mean age 81.2±8.5 vs 78.2±10.1 years) at admission (p<0.001). In women, there was a lower risk associated with better SES: RR=0.83 (95%CrI 0.65-1.00) for affluent versus deprived. There was an inverse association between SES and HF incidence rate in the youngest and a direct association in the oldest, for both sexes, but significant only between deprived and affluent in older ages (≥75 years). CONCLUSIONS: Interaction between SES and age may be due to inequalities in lifestyles, access to health systems, and preventive actions. These results may help decision-makers to better understand the epidemiology of hip fractures and to better direct the available funding.Programa Operacional Factores de Competitividade (COMPETE)Fundação para a Ciência e a Tecnologia (FCT

A Bayesian modelling framework to quantify multiple sources of spatial variation for disease mapping.

Spatial connectivity is an important consideration when modelling infectious disease data across a geographical region. Connectivity can arise for many reasons, including shared characteristics between regions and human or vector movement. Bayesian hierarchical models include structured random effects to account for spatial connectivity. However, conventional approaches require the spatial structure to be fully defined prior to model fitting. By applying penalized smoothing splines to coordinates, we create two-dimensional smooth surfaces describing the spatial structure of the data while making minimal assumptions about the structure. The result is a non-stationary surface which is setting specific. These surfaces can be incorporated into a hierarchical modelling framework and interpreted similarly to traditional random effects. Through simulation studies, we show that the splines can be applied to any symmetric continuous connectivity measure, including measures of human movement, and that the models can be extended to explore multiple sources of spatial structure in the data. Using Bayesian inference and simulation, the relative contribution of each spatial structure can be computed and used to generate hypotheses about the drivers of disease. These models were found to perform at least as well as existing modelling frameworks, while allowing for future extensions and multiple sources of spatial connectivity

Transmission parameters estimated for Salmonella typhimurium in swine using susceptible-infectious-resistant models and a Bayesian approach.

This is a freely-available open access publication. Please cite the published version which is available via the DOI link in this record.BACKGROUND: Transmission models can aid understanding of disease dynamics and are useful in testing the efficiency of control measures. The aim of this study was to formulate an appropriate stochastic Susceptible-Infectious-Resistant/Carrier (SIR) model for Salmonella Typhimurium in pigs and thus estimate the transmission parameters between states. RESULTS: The transmission parameters were estimated using data from a longitudinal study of three Danish farrow-to-finish pig herds known to be infected. A Bayesian model framework was proposed, which comprised Binomial components for the transition from susceptible to infectious and from infectious to carrier; and a Poisson component for carrier to infectious. Cohort random effects were incorporated into these models to allow for unobserved cohort-specific variables as well as unobserved sources of transmission, thus enabling a more realistic estimation of the transmission parameters. In the case of the transition from susceptible to infectious, the cohort random effects were also time varying. The number of infectious pigs not detected by the parallel testing was treated as unknown, and the probability of non-detection was estimated using information about the sensitivity and specificity of the bacteriological and serological tests. The estimate of the transmission rate from susceptible to infectious was 0.33 [0.06, 1.52], from infectious to carrier was 0.18 [0.14, 0.23] and from carrier to infectious was 0.01 [0.0001, 0.04]. The estimate for the basic reproduction ration (R0) was 1.91 [0.78, 5.24]. The probability of non-detection was estimated to be 0.18 [0.12, 0.25]. CONCLUSIONS: The proposed framework for stochastic SIR models was successfully implemented to estimate transmission rate parameters for Salmonella Typhimurium in swine field data. R0 was 1.91, implying that there was dissemination of the infection within pigs of the same cohort. There was significant temporal-cohort variability, especially at the susceptible to infectious stage. The model adequately fitted the data, allowing for both observed and unobserved sources of uncertainty (cohort effects, diagnostic test sensitivity), so leading to more reliable estimates of transmission parameters.FC

The impact of climate suitability, urbanisation, and connectivity on the expansion of dengue in 21st century Brazil.

Dengue is hyperendemic in Brazil, with outbreaks affecting all regions. Previous studies identified geographical barriers to dengue transmission in Brazil, beyond which certain areas, such as South Brazil and the Amazon rainforest, were relatively protected from outbreaks. Recent data shows these barriers are being eroded. In this study, we explore the drivers of this expansion and identify the current limits to the dengue transmission zone. We used a spatio-temporal additive model to explore the associations between dengue outbreaks and temperature suitability, urbanisation, and connectivity to the Brazilian urban network. The model was applied to a binary outbreak indicator, assuming the official threshold value of 300 cases per 100,000 residents, for Brazil's municipalities between 2001 and 2020. We found a nonlinear relationship between higher levels of connectivity to the Brazilian urban network and the odds of an outbreak, with lower odds in metropoles compared to regional capitals. The number of months per year with suitable temperature conditions for Aedes mosquitoes was positively associated with the dengue outbreak occurrence. Temperature suitability explained most interannual and spatial variation in South Brazil, confirming this geographical barrier is influenced by lower seasonal temperatures. Municipalities that had experienced an outbreak previously had double the odds of subsequent outbreaks. We identified geographical barriers to dengue transmission in South Brazil, western Amazon, and along the northern coast of Brazil. Although a southern barrier still exists, it has shifted south, and the Amazon no longer has a clear boundary. Few areas of Brazil remain protected from dengue outbreaks. Communities living on the edge of previous barriers are particularly susceptible to future outbreaks as they lack immunity. Control strategies should target regions at risk of future outbreaks as well as those currently within the dengue transmission zone