Trevor C. Bailey

Spatial point pattern analysis and its application in geographical epidemiology

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.

Mechanisms governing organizational performance in complex decision-making environments

Abstract The present experiment tested level of organizational performance as a function of perceived self-efficacy, task complexity, assigned and self-set goals, and analytic strategies in managerial decision-making in a stimulated organization. Organizational performance and analytic strategies were assessed across 16 trials and self-reactive influences operating through perceived selfefficacy, self-set goals, and acceptance of assigned goals were assessed at several points in the simulation. Assigned challenging goals had a positive effect on level of performance in the low complexity condition but not in the condition of high organizational complexity. The latter finding was explained in terms of the temporal and social complexity of the linkages between managerial effort and collective accomplishment and the multifaceted nature of goal-setting in complex social environments. The self-reactive influences had comparable effects on managerial performance across the differing levels of goal assignment and organizational complexity. In path analyses, perceived self-efficacy was positively related to effective use of analytic strategies for discovering optimal managerial rules and level of personal goals. Both perceived self-efficacy and analytic strategies contributed to managerial success in raising organizational performance. Self-set goals also contributed to performance in the earlier trials of the simulation but not in the later trials, following a change in task demands which reduced the generalizability of prior standards.

Wavelet Analysis and its Statistical Applications

Summary. In recent years there has been a considerable development in the use of wavelet methods in statistics. As a result, we are now at the stage where it is reasonable to consider such methods to be another standard tool of the applied statistician rather than a research novelty. With that in mind, this paper gives a relatively accessible introduction to standard wavelet analysis and provides a review of some common uses of wavelet methods in statistical applications. It is primarily orientated towards the general statistical audience who may be involved in analysing data where the use of wavelets might be effective, rather than to researchers who are already familiar with the field. Given that objective, we do not emphasize mathematical generality or rigour in our exposition of wavelets and we restrict our discussion to the more frequently employed wavelet methods in statistics. We provide extensive references where the ideas and concepts discussed can be followed up in greater detail and generality if required. The paper first establishes some necessary basic mathematical background and terminology relating to wavelets. It then reviews the more well-established applications of wavelets in statistics including their use in nonparametric regression, density estimation, inverse problems, changepoint problems and in some specialized aspects of time series analysis. Possible extensions to the uses of wavelets in statistics are then considered. The paper concludes with a brief reference to readily available software packages for wavelet analysis.

Improved biclustering of microarray data demonstrated through systematic performance tests

A new algorithm is presented for fitting the plaid model, a biclustering method developed for clustering gene expression data. The approach is based on speedy individual differences clustering and uses binary least squares to update the cluster membership parameters, making use of the binary constraints on these parameters and simplifying the other parameter updates. The performance of both algorithms is tested on simulated data sets designed to imitate (normalised) gene expression data, covering a range of biclustering configurations. Empirical distributions for the components of these data sets, including non-systematic error, are derived from a real set of microarray data. A set of two-way quality measures is proposed, based on one-way measures commonly used in information retrieval, to evaluate the quality of a retrieved bicluster with respect to a target bicluster in terms of both genes and samples. By defining a one-to-one correspondence between target biclusters and retrieved biclusters, the performance of each algorithm can be assessed. The results show that, using appropriately selected starting criteria, the proposed algorithm out-performs the original plaid model algorithm across a range of data sets. Furthermore, through the rigorous assessment of the plaid model a benchmark for future evaluation of biclustering methods is established.

Interactive spatial data analysis in medical geography

Interactive spatial data analysis involves the use of software environments that permit the visualization, exploration and, perhaps, modelling of geographically-referenced data. Such systems are of obvious value in epidemiological research, both of an environmental and geographical nature. There is an increasing number of such software environments available on a variety of platforms and operating systems. This paper considers the use of the proprietary Geographical Information System, ARC/INFO, in a spatial analysis context, showing how the spatial analytic tools that may be added to it can be exploited by geographical epidemiologists; such tools include those for modelling possible raised incidence of disease around suspected sources of pollution. The paper also reviews the use of systems such as S-Plus and XLISP-STAT, statistical programming environments to which spatial analysis functions or libraries may be added. The use of INFO-MAP, a system designed to aid in the teaching of interactive spatial data analysis, is also highlighted. The various software environments are illustrated with reference to examples concerned with: clustering of childhood leukaemia in part of Lancashire, England; Burkitts lymphoma in Uganda; larynx cancer in Lancashire; and childhood mortality in Auckland, New Zealand.

Spatio-temporal modelling of climate-sensitive disease risk: Towards an early warning system for dengue in Brazil

This paper considers the potential for using seasonal climate forecasts in developing an early warning system for dengue fever epidemics in Brazil. In the first instance, a generalised linear model (GLM) is used to select climate and other covariates which are both readily available and prove significant in prediction of confirmed monthly dengue cases based on data collected across the whole of Brazil for the period January 2001 to December 2008 at the microregion level (typically consisting of one large city and several smaller municipalities). The covariates explored include temperature and precipitation data on a 2.5^^ox2.5^^o longitude-latitude grid with time lags relevant to dengue transmission, an El Nino Southern Oscillation index and other relevant socio-economic and environmental variables. A negative binomial model formulation is adopted in this model selection to allow for extra-Poisson variation (overdispersion) in the observed dengue counts caused by unknown/unobserved confounding factors and possible correlations in these effects in both time and space. Subsequently, the selected global model is refined in the context of the South East region of Brazil, where dengue predominates, by reverting to a Poisson framework and explicitly modelling the overdispersion through a combination of unstructured and spatio-temporal structured random effects. The resulting spatio-temporal hierarchical model (or GLMM-generalised linear mixed model) is implemented via a Bayesian framework using Markov Chain Monte Carlo (MCMC). Dengue predictions are found to be enhanced both spatially and temporally when using the GLMM and the Bayesian framework allows posterior predictive distributions for dengue cases to be derived, which can be useful for developing a dengue alert system. Using this model, we conclude that seasonal climate forecasts could have potential value in helping to predict dengue incidence months in advance of an epidemic in South East Brazil.

Biclustering Models for Structured Microarray Data

Microarrays have become a standard tool for investigating gene function and more complex microarray experiments are increasingly being conducted. For example, an experiment may involve samples from several groups or may investigate changes in gene expression over time for several subjects, leading to large three-way data sets. In response to this increase in data complexity, we propose some extensions to the plaid model, a biclustering method developed for the analysis of gene expression data. This model-based method lends itself to the incorporation of any additional structure such as external grouping or repeated measures. We describe how the extended models may be fitted and illustrate their use on real data.

Chloroplasts play a central role in plant defence and are targeted by pathogen effectors.

Microbe associated molecular pattern (MAMP) receptors in plants recognize MAMPs and activate basal defences; however a complete understanding of the molecular and physiological mechanisms conferring immunity remains elusive. Pathogens suppress active defence in plants through the combined action of effector proteins. Here we show that the chloroplast is a key component of early immune responses. MAMP perception triggers the rapid, large-scale suppression of nuclear encoded chloroplast-targeted genes (NECGs). Virulent Pseudomonas syringae effectors reprogramme NECG expression in Arabidopsis, target the chloroplast and inhibit photosynthetic CO2 assimilation through disruption of photosystem II. This activity prevents a chloroplastic reactive oxygen burst. These physiological changes precede bacterial multiplication and coincide with pathogen-induced abscisic acid (ABA) accumulation. MAMP pretreatment protects chloroplasts from effector manipulation, whereas application of ABA or the inhibitor of photosynthetic electron transport, DCMU, abolishes the MAMP-induced chloroplastic reactive oxygen burst, and enhances growth of a P. syringae hrpA mutant that fails to secrete effectors.

Dengue outlook for the World Cup in Brazil: an early warning model framework driven by real-time seasonal climate forecasts

BACKGROUND With more than a million spectators expected to travel among 12 different cities in Brazil during the football World Cup, June 12-July 13, 2014, the risk of the mosquito-transmitted disease dengue fever is a concern. We addressed the potential for a dengue epidemic during the tournament, using a probabilistic forecast of dengue risk for the 553 microregions of Brazil, with risk level warnings for the 12 cities where matches will be played. METHODS We obtained real-time seasonal climate forecasts from several international sources (European Centre for Medium-Range Weather Forecasts [ECMWF], Met Office, Meteo-France and Centro de Previsão de Tempo e Estudos Climáticos [CPTEC]) and the observed dengue epidemiological situation in Brazil at the forecast issue date as provided by the Ministry of Health. Using this information we devised a spatiotemporal hierarchical Bayesian modelling framework that enabled dengue warnings to be made 3 months ahead. By assessing the past performance of the forecasting system using observed dengue incidence rates for June, 2000-2013, we identified optimum trigger alert thresholds for scenarios of medium-risk and high-risk of dengue. FINDINGS Our forecasts for June, 2014, showed that dengue risk was likely to be low in the host cities Brasília, Cuiabá, Curitiba, Porto Alegre, and São Paulo. The risk was medium in Rio de Janeiro, Belo Horizonte, Salvador, and Manaus. High-risk alerts were triggered for the northeastern cities of Recife (p(high)=19%), Fortaleza (p(high)=46%), and Natal (p(high)=48%). For these high-risk areas, particularly Natal, the forecasting system did well for previous years (in June, 2000-13). INTERPRETATION This timely dengue early warning permits the Ministry of Health and local authorities to implement appropriate, city-specific mitigation and control actions ahead of the World Cup. FUNDING European Commissions Seventh Framework Research Programme projects DENFREE, EUPORIAS, and SPECS; Conselho Nacional de Desenvolvimento Científico e Tecnológico and Fundação de Amparo à Pesquisa do Estado do Rio de Janeiro.

Some Unanswered Questions about Goal Effects: A Recommended Change in Research Methods

Goal setting has been described to managers as a “motivational technique that works” (Locke and Latham, 1984), despite a lack of theoretical explanations in the organisational behaviour literature about why, how or when it works. Recent advances have been made in the development of goal effects theory which attempt to deal with these issues (e.g. Locke et al., 1981; Naylor and Ilgen, 1984). This paper complements those theoretical developments through a discussion of how existing laboratory and field studies have tended to overlook learning, task and chronic effects in the study of goal effects. The pros and cons of using a research game for the study of goal effects are discussed and a research game which has been developed to study goal, feedback and task effects on individual performance is described.