Hierarchical Spatio-Temporal Mapping of Disease Rates

Waller, Lance A.; Carlin, Bradley P.; Hong, Xia; Gelfand, Alan E.

doi:10.2307/2965708

Cited by 213 publications

(250 citation statements)

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“…Many other researchers [7,8] proposed and implemented space-time models with different interactions. Spatial and temporal malaria variation is studied in [9] with an investigation of possible geographical expansion of malaria transmission.…”

Section: Introductionmentioning

confidence: 99%

Spatial and temporal patterns of malaria incidence in Mozambique

Zacarias

Andersson

2011

Malar J

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BackgroundThe objective of this study is to analyze the spatial and temporal patterns of malaria incidence as to determine the means by which climatic factors such as temperature, rainfall and humidity affect its distribution in Maputo province, Mozambique.MethodsThis study presents a model of malaria that evolves in space and time in Maputo province-Mozambique, over a ten years period (1999-2008). The model incorporates malaria cases and their relation to environmental variables. Due to incompleteness of climatic data, a multiple imputation technique is employed. Additionally, the whole province is interpolated through a Gaussian process. This method overcomes the misalignment problem of environmental variables (available at meteorological stations - points) and malaria cases (available as aggregates for every district - area). Markov Chain Monte Carlo (MCMC) methods are used to obtain posterior inference and Deviance Information Criteria (DIC) to perform model comparison.ResultsA Bayesian model with interaction terms was found to be the best fitted model. Malaria incidence was associated to humidity and maximum temperature. Malaria risk increased with maximum temperature over 28°C (relative risk (RR) of 0.0060 and 95% Bayesian credible interval (CI) of 0.00033-0.0095) and humidity (relative risk (RR) of 0.00741 and 95% Bayesian CI 0.005141-0.0093). The results would suggest that additional non-climatic factors including socio-economic status, elevation, etc. also influence malaria transmission in Mozambique.ConclusionsThese results demonstrate the potential of climate predictors particularly, humidity and maximum temperature in explaining malaria incidence risk for the studied period in Maputo province. Smoothed maps obtained as monthly average of malaria incidence allowed to visualize months of initial and peak transmission. They also illustrate a variation on malaria incidence risk that might not be related to climatic factors. However, these factors are still determinant for malaria transmission and intensity in the region.

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Section: Introductionmentioning

confidence: 99%

Spatial and temporal patterns of malaria incidence in Mozambique

Zacarias

Andersson

2011

Malar J

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“…For space-time area-based data, there is a large literature in disease mapping, adopting Bayesian modelling to produce smoothed estimates of the area-year-specific disease rates (e.g., Waller, Carlin, Xia & Gelfand 1997;Knorr-Held & Besag 1998;Lagazio, Dreassi & Biggeri 2001;Assunção, Reis & Oliveira 2001;MacNab & Dean 2002;Knorr-Held & Richardson 2003). Gangnon & Clayton (2002, 2004 proposed methods that simultaneously address both the cluster detection problem and the cluster modelling approach.…”

Section: Inferential Approaches For Space-time Clustersmentioning

confidence: 99%

Space‐time cluster identification in point processes

et al. 2007

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Abstract:The authors propose a new type of scan statistic to test for the presence of space-time clusters in point processes data, when the goal is to identify and evaluate the statistical significance of localized clusters. Their method is based only on point patterns for cases; it does not require any specific knowledge of the underlying population. The authors propose to scan the three-dimensional space with a score test statistic under the null hypothesis that the underlying point process is an inhomogeneous Poisson point process with space and time separable intensity. The alternative is that there are one or more localized space-time clusters. Their method has been implemented in a computationally efficient way so that it can be applied routinely. They illustrate their method with space-time crime data from Belo Horizonte, a Brazilian city, in addition to presenting a Monte Carlo study to analyze the power of their new test. Identification d'agrégation spatio-temporelle dans des processus ponctuelsRésumé : Les auteurs proposent une statistique permettant de détecter la présence d'agrégation spatiotemporelle dans des données issues de processus ponctuels, l'objectifétant d'identifier des regroupements et d'évaluer s'ils sont statistiquement significatifs. Leur méthode ne dépend que de la disposition des observations ; elle ne nécessite aucune connaissance particulière de la population sous-jacente. Une statistique de type score mesurée sur l'ensemble de l'espaceà trois dimensions leur permet de tester l'hypothèse nullè a l'effet que le processus ponctuel sous-jacent est un processus de Poisson hétérogène dont les composantes spatiale et temporelle de l'intensité sont séparables. La contre-hypothèse està l'effet qu'il existe un ou des regroupements spatio-temporels localisés. L'implantation informatique de leur méthode est efficace au plan calcul et facile d'emploi. Les auteurs l'illustrentà l'aide de données sur la répartition spatio-temporelle de la criminalité dans la ville brésilienne de Belo Horizonte, en plus de présenter uneétude de Monte-Carlo investigant la puissance de leur test.

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“…To compare our model to Gaussian Markov random ÿeld models with respect to their overall performance and ability to detect disease clusters, we applied one type of model from Waller et al [5] where w ij = 1 if spatial cell j is a neighbour of cell i and 0 otherwise, n i is the number of neighbours of cell i, and t , t = 1; : : : ; T are unknown parameters. Here, we deÿne neighbours of municipality i to be municipalities adjacent to municipality i in the Dirichlet tessellation of the municipality centroids.…”

Section: Model Comparisonmentioning

confidence: 99%

“…Beyond purely spatial Gaussian Markov random ÿeld models, a considerable number of spatio-temporal models [4][5][6][7][8][9][10][11] have been developed, mainly for the purpose of mapping spatio-temporal disease, detecting space-time interactions, or predicting disease risks. As with the purely spatial models discussed above, these models do not directly model clusters either and those clusters with elevated risks or steeper trends over time can only be visually detected or summarized from the estimated disease risks.…”

Section: Introductionmentioning

confidence: 99%

A cluster model for space–time disease counts

Yan

Clayton

2006

Statistics in Medicine

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Modelling disease clustering over space and time can be helpful in providing indications of possible exposures and planning corresponding public health practices. Though a considerable number of studies focus on modelling spatio-temporal patterns of disease, most of them do not directly model a spatio-temporal clustering structure and could be ineffective for detecting clusters. In this paper, we extend a purely spatial cluster model to accommodate space-time clustering. Inference is performed in a Bayesian framework using reversible jump Markov chain Monte Carlo. This idea is illustrated using data on female breast cancer mortality from Japan. A hierarchical parametric space-time model for mapping disease is used for comparison.

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Hierarchical Spatio-Temporal Mapping of Disease Rates

Cited by 213 publications

References 0 publications

Spatial and temporal patterns of malaria incidence in Mozambique

Spatial and temporal patterns of malaria incidence in Mozambique

Space‐time cluster identification in point processes

A cluster model for space–time disease counts

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