Spatial analysis in epidemiology / Dirk U. Pfeiffer [and others].

Includes bibliographical references and index.

Providing a practical, comprehensive and up-to-date overview of the use of spatial statistics in epidemiology, this book examines spatial analytical methods in conjunction with GIS and remotely sensed data to provide insights into the patterns and processes that underlie disease transmission.

Print version record.

Abbreviations; Preface; 1 Introduction; 1.1 Framework for spatial analysis; 1.2 Scientific literature and conferences; 1.3 Software; 1.4 Spatial data; 1.5 Book content and structure; 1.5.1 Datasets used; 2 Spatial data; 2.1 Introduction; 2.2 Spatial data and GIS; 2.2.1 Data types; 2.2.2 Data storage and interchange; 2.2.3 Data collection and management; 2.2.4 Data quality; 2.3 Spatial effects; 2.3.1 Spatial heterogeneity and dependence; 2.3.2 Edge effects; 2.3.3 Representing neighbourhood relationships; 2.3.4 Statistical significance testing with spatial data; 2.4 Conclusion.

3 Spatial visualization3.1 Introduction; 3.2 Point data; 3.3 Aggregated data; 3.4 Continuous data; 3.5 Effective data display; 3.5.1 Media, scale, and area; 3.5.2 Dynamic display; 3.5.3 Cartography; 3.6 Conclusion; 4 Spatial clustering of disease and global estimates of spatial clustering; 4.1 Introduction; 4.2 Disease cluster alarms and cluster investigation; 4.3 Statistical concepts relevant to cluster analysis; 4.3.1 Stationarity, isotropy, and first- and second-order effects; 4.3.2 Monte Carlo simulation; 4.3.3 Statistical power of clustering methods; 4.4 Methods for aggregated data.

4.4.1 Moran's I4.4.2 Geary's c; 4.4.3 Tango's excess events test (EET) and maximized excess events test (MEET); 4.5 Methods for point data; 4.5.1 Cuzick and Edwards' k-nearest neighbour test; 4.5.2 Ripley's K-function; 4.5.3 Rogerson's cumulative sum (CUSUM) method; 4.6 Investigating space-time clustering; 4.6.1 The Knox test; 4.6.2 The space-time k-function; 4.6.3 The Ederer-Myers-Mantel (EMM) test; 4.6.4 Mantel's test; 4.6.5 Barton's test; 4.6.6 Jacquez's k nearest neighbours test; 4.7 Conclusion; 5 Local estimates of spatial clustering; 5.1 Introduction; 5.2 Methods for aggregated data.

5.2.1 Getis and Ord's local Gi(d) statistic5.2.2 Local Moran test; 5.3 Methods for point data; 5.3.1 Openshaw's Geographical Analysis Machine (GAM); 5.3.2 Turnbull's Cluster Evaluation Permutation Procedure (CEPP); 5.3.3 Besag and Newell's method; 5.3.4 Kulldorff's spatial scan statistic; 5.3.5 Non-parametric spatial scan statistics; 5.3.6 Example of local cluster detection; 5.4 Detecting clusters around a source (focused tests); 5.4.1 Stone's test; 5.4.2 The Lawson-Waller score test; 5.4.3 Bithell's linear risk score tests; 5.4.4 Diggle's test.

5.4.5 Kulldorff's focused spatial scan statistic5.5 Space-time cluster detection; 5.5.1 Kulldorff's space-time scan statistic; 5.5.2 Example of space-time cluster detection; 5.6 Conclusion; 6 Spatial variation in risk; 6.1 Introduction; 6.2 Smoothing based on kernel functions; 6.3 Smoothing based on Bayesian models; 6.4 Spatial interpolation; 6.5 Conclusion; 7 Identifying factors associated with the spatial distribution of disease; 7.1 Introduction; 7.2 Principles of regression modelling; 7.2.1 Linear regression; 7.2.2 Poisson regression; 7.2.3 Logistic regression; 7.2.4 Multilevel models.

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