Barry Rowlingson

Bio: Barry Rowlingson is an academic researcher from Lancaster University. The author has contributed to research in topics: Population & Point process. The author has an hindex of 21, co-authored 46 publications receiving 2712 citations. Previous affiliations of Barry Rowlingson include Fylde College, Lancaster University.

Spatial point pattern analysis and its application in geographical epidemiology

Abstract: This paper reviews a number of methods for the exploration and modelling of spatial point patterns with particular reference to geographical epidemiology (the geographical incidence of disease). Such methods go well beyond the conventional ‘nearest-neighbour’ and ‘quadrat’ analyses which have little to offer in an epidemiological context because they fail to allow for spatial variation in population density. Correction for this is essential if the aim is to assess the evidence for ‘clustering’ of cases of disease. We examine methods for exploring spatial variation in disease risk, spatial and space-time clustering, and we consider methods for modelling the raised incidence of disease around suspected point sources of pollution. All methods are illustrated by reference to recent case studies including child cancer incidence, Burkitt’s lymphoma, cancer of the larynx and childhood asthma. An Appendix considers a range of possible software environments within which to apply these methods. The links to modern geographical information systems are discussed.

Spatial and spatio-temporal Log-Gaussian Cox processes:extending the geostatistical paradigm

Abstract: In this paper we first describe the class of log-Gaussian Cox processes (LGCPs) as models for spatial and spatio-temporal point process data. We discuss inference, with a particular focus on the computational challenges of likelihood-based inference. We then demonstrate the usefulness of the LGCP by describing four applications: estimating the intensity surface of a spatial point process; investigating spatial segregation in a multi-type process; constructing spatially continuous maps of disease risk from spatially discrete data; and real-time health surveillance. We argue that problems of this kind fit naturally into the realm of geostatistics, which traditionally is defined as the study of spatially continuous processes using spatially discrete observations at a finite number of locations. We suggest that a more useful definition of geostatistics is by the class of scientific problems that it addresses, rather than by particular models or data formats.

Point process methodology for on-line spatio-temporal disease surveillance

Abstract: We formulate the problem of on-line spatio-temporal disease surveillance in terms of predicting spatially and temporally localised excursions over a pre-specified threshold value for the spatially and temporally varying intensity of a point process in which each point represents an individual case of the disease in question. Our point process model is a non-stationary log-Gaussian Cox process in which the spatio-temporal intensity, (x,t), has a multiplicative decomposition into two deterministic components, one describing purely spatial and the other purely temporal variation in the normal disease incidence pattern, and an unobserved stochastic component representing spatially and temporally localised departures from the normal pattern. We give methods for estimating the parameters of the model, and for making probabilistic predictions of the current intensity. We describe an application to on-line spatio-temporal surveillance of non-specific gastroenteric disease in the county of Hampshire, UK. The results are presented as maps of exceedance probabilities, P{R(x,t)c|data}, where R(x,t) is the current realisation of the unobserved stochastic component of (x,t) and c is a pre-specified threshold. These maps are updated automatically in response to each day's incident data using a web-based reporting system. Copyright © 2005 John Wiley & Sons, Ltd.

Predicting malaria infection in Gambian children from satellite data and bed net use surveys: the importance of spatial correlation in the interpretation of results.

Abstract: In line with the renewed World Health Organization Global Malaria Control Strategy, we have advocated the use of satellite imagery by control services to provide environmental information for malaria stratification, monitoring, and early warning. To achieve this operationally, appropriate methodologies must be developed for integrating environmental and epidemiologic data into models that can be used by decision-makers for improved resource allocation. Using methodologies developed for the Famine Early Warning Systems and spatial statistics, we show a significant association between age related malaria infection in Gambian children and the amount of seasonal environmental greenness as measured using the normalized difference vegetation index derived from satellite data. The resulting model is used to predict changes in malaria prevalence rates in children resulting from different bed net control scenarios.

Out of the Crisis

Abstract: According to W. Edwards Deming, American companies require nothing less than a transformation of management style and of governmental relations with industry. In Out of the Crisis, originally published in 1982, Deming offers a theory of management based on his famous 14 Points for Management. Management's failure to plan for the future, he claims, brings about loss of market, which brings about loss of jobs. Management must be judged not only by the quarterly dividend, but by innovative plans to stay in business, protect investment, ensure future dividends, and provide more jobs through improved product and service. In simple, direct language, he explains the principles of management transformation and how to apply them.

Combining Pattern Classifiers: Methods and Algorithms

Abstract: (2005). Combining Pattern Classifiers: Methods and Algorithms. Technometrics: Vol. 47, No. 4, pp. 517-518.

Applied Multivariate Statistical Analysis

Abstract: (2005). Applied Multivariate Statistical Analysis. Technometrics: Vol. 47, No. 4, pp. 517-517.

Model-based Geostatistics

Abstract: An overview of model-based geostatistics.- Gaussian models for geostatistical data.- Generalized linear models for geostatistical data.- Classical parameter estimation.- Spatial prediction.- Bayesian inference.- Geostatistical design.