Bayesian Detection of Clusters and Discontinuities in Disease Maps

Knorr‐Held, Leonhard; Raßer, Günter

doi:10.1111/j.0006-341x.2000.00013.x

Cited by 259 publications

(196 citation statements)

References 12 publications

(18 reference statements)

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“…26 Lawson proposed a susceptible, infectious, or recovered (SIR) model to analysis the spatio-temporal dynamics of season outbreaks and detailed the modeling process and evaluation of spatio-temporal models. 27 The spatial effects b it = ( i = 1,…, N ; t = 1,…, T ) in equation 5 could be modeled as fixed or varies with time.…”

mentioning

confidence: 99%

Meteorological Factors–Based Spatio-Temporal Mapping and Predicting Malaria in Central China

Huang¹,

Zhou²,

Zhang³

et al. 2011

The American Society of Tropical Medicine and Hygiene

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Despite significant reductions in the overall burden of malaria in the 20th century, this disease still represents a significant public health problem in China, especially in central areas. Understanding the spatio-temporal distribution of malaria is essential in the planning and implementing of effective control measures. In this study, normalized meteorological factors were incorporated in spatio-temporal models. Seven models were established in WinBUGS software by using Bayesian hierarchical models and Markov Chain Monte Carlo methods. M1, M2, and M3 modeled separate meteorological factors, and M3, which modeled rainfall performed better than M1 and M2, which modeled average temperature and relative humidity, respectively. M7 was the best fitting models on the basis of based on deviance information criterion and predicting errors. The results showed that the way rainfall influencing malaria incidence was different from other factors, which could be interpreted as rainfall having a greater influence than other factors.

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mentioning

confidence: 99%

Meteorological Factors–Based Spatio-Temporal Mapping and Predicting Malaria in Central China

Huang¹,

Zhou²,

Zhang³

et al. 2011

The American Society of Tropical Medicine and Hygiene

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“…The proposed local triangular kernel clustering, LTKC algorithm, assigns objects into clusters using Bayesian decision rule [14]. In the Bayesian decision rule, the classconditional density that refers to the density of an object is required to be determined.…”

Section: Local Triangular Kernel Clustering Algorithmmentioning

confidence: 99%

Mining Spatio-Temporal Data of Fatal Accident

Musdholifah¹,

Hadhim²

2013

IJCA

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Traffic accidents are an important concern of today's governments and societies, due to the high cost of human and economical resources involved. Data mining has been proven able to significantly help in improving traffic safety. Among several data mining tasks, clustering technique is mostly applied on spatio-temporal data, especially for the traffic data. A number of traffic related works proposed different clustering techniques for mining the spatio-temporal of traffic accident. However, some difficulties appeared when analyzing these datasets, such as the size of data, the lack of statistical evaluation methods, and interpreting the valuable patterns. With regard to solving this problem, this paper proposes a clustering approach for mining spatio-temporal data of fatal accident using local triangular kernel clustering (LTKC) algorithm. LTKC is kernel-density-based clustering algorithm that has the ability to determine the number of clusters automatically. We also propose three visualization techniques for use to interpret and present the optimal clustering result in an easy-understanding form. From the experimental results, LTKC approach was found to be able to discover responsible clusters within fatal accident data, which had proven by silhouette and Dunn index values close to 1. In addition, using visual techniques, we can state that the clustering results were well-separated and compact clusters.

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“…This model was proposed for general non-linear regression problems and has been used for spatial mapping problems (e.g. Ferreira et al 2002 andKnorr-Held andRaßer 2000). As the intensity λ → 0, we will not get any switches in the ordering and F x will no longer depend on x.…”

Section: Mixtures Of Order-based Dependent Dirichlet Processesmentioning

confidence: 99%

Order-Based Dependent Dirichlet Processes

Griffin

Steel

2006

Journal of the American Statistical Association

263

210

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In this paper we propose a new framework for Bayesian nonparametric modelling with continuous covariates. In particular, we allow the nonparametric distribution to depend on covariates through ordering the random variables building the weights in the stick-breaking representation. We focus mostly on the class of random distributions which induces a Dirichlet process at each covariate value. We derive the correlation between distributions at different covariate values, and use a point process to implement a practically useful type of ordering. Two main constructions with analytically known correlation structures are proposed. Practical and efficient computational methods are introduced. We apply our framework, though mixtures of these processes, to regression modelling, the modelling of stochastic volatility in time series data and spatial geostatistical modelling.

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Bayesian Detection of Clusters and Discontinuities in Disease Maps

Cited by 259 publications

References 12 publications

Meteorological Factors–Based Spatio-Temporal Mapping and Predicting Malaria in Central China

Meteorological Factors–Based Spatio-Temporal Mapping and Predicting Malaria in Central China

Mining Spatio-Temporal Data of Fatal Accident

Order-Based Dependent Dirichlet Processes

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