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We used both Bayesian estimation implemented via Markov chain Monte Carlo methods, and a combination of Bayesian and geo-statistical methods. Risk maps were generated using the model results. Of over 60 potential explanatory variables, the large majority were strongly associated with malaria prevalence in uni-variate analysis.

Learning Outcomes

Different co-variate selection methods resulted in several very different multi-variate regression models. Though good overall fit could be achieved, the best-fitting models did not produce the most plausible risk maps. The choice of co-variates had a greater impact on the risk map outcome than using spatial versus non-spatial statistical methods.

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Different model outcomes will be presented and the issue of biological plausibility versus statistical accuracy will be discussed. You may be trying to access this site from a secured browser on the server.

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Subscribe Register Login. Your Name: optional. Provides a comprehensive overview of the main statistical methods used in spatial epidemiology. Updated to include a new emphasis on bio-terrorism and disease surveillance. Emphasizes the importance of space-time modelling and outlines the practical application of the method.

Statistical Methods in Spatial Epidemiology - Oxford Scholarship

Discusses the wide range of software available for analyzing spatial data, including WinBUGS, SaTScan and R, and features an accompanying website hosting related software. Contains numerous data sets, each representing a different approach to the analysis, and provides an insight into various modelling techniques. Reviews "…the second edition is a substantial improvement on what was already a valuable, well structured and comprehensive reference…" Biometrics , September Author Bios Professor Andrew B.

Lawson is a respected and well-known academic. He has published many papers in leading journals, and a number of books on spatial statistics, including five for Wiley. Free Access.

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Spatial epidemiology plays a relevant role in this matter and we present here a review of this subject, including a discussion of the literature in terms of the level of geographic data aggregation, risk factors and methods used to analyse the spatial distribution of patterns and spatial clusters. For this purpose, we performed a websearch in the Pubmed and Web of Science databases including studies published between and We found papers from 63 journals and noted that spatial epidemiology of cancer has been addressed with more emphasis during the last decade with research based on data mostly extracted from cancer registries and official mortality statistics.

This review is expected to help promote research in this area through the identification of relevant knowledge gaps.

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