A. Lawson, A. Biggeri, D. Böhning, E. Lesaffre, J. Viel et al., Disease mapping and risk assessment for public health, 1999.

A. B. Lawson and D. Denison, Spatial Cluster Modelling, 2010.

S. D. Walter, Visual and statistical assessment of spatial clustering in mapped data, Stat Med, vol.12, pp.1275-91, 1993.

A. B. Lawson, Bayesian disease mapping: hierarchical modeling in spatial epidemiology, 2013.

M. Colonna and E. Sauleau, How to interpret and choose a Bayesian spatial model and a Poisson regression model in the context of describing small area cancer risks variations, Rev Epidemiol Sante Publique, vol.61, pp.559-67, 2013.

M. Kulldorff and N. Nagarwalla, Spatial disease clusters: detection and inference, Stat Med, vol.14, pp.799-810, 1995.

A. Guttmann, L. Ouchchane, X. Li, I. Perthus, J. Gaudart et al., Performance map of a cluster detection test using extended power, Int J Health Geogr, vol.12, p.47, 2013.
URL : https://hal.archives-ouvertes.fr/inserm-00903889

L. Huang, L. W. Pickle, and B. Das, Evaluating spatial methods for investigating global clustering and cluster detection of cancer cases, Stat Med, vol.27, pp.5111-5153, 2008.

R. F. Potthoff and M. Whittinghill, Testing for Homogeneity: II. The Poisson Distribution, Biometrika, vol.53, p.183, 1966.

A. D. Cliff and J. K. Ord, Spatial autocorrelation, vol.5, 1973.

M. Kulldorff, A spatial scan statistic, Commun Stat-Theory Methods, vol.26, pp.1481-96, 1997.

J. Gaudart, B. Poudiougou, S. Ranque, and O. Doumbo, Oblique decision trees for spatial pattern detection: optimal algorithm and application to malaria risk, BMC Med Res Methodol, vol.5, p.22, 2005.
URL : https://hal.archives-ouvertes.fr/inserm-00090279

J. Landier, J. Gaudart, C. K. Lo-seen, D. Guégan, J. Eyangoh et al., Spatio-temporal Patterns and Landscape-Associated Risk of Buruli Ulcer in Akonolinga, Cameroon, PLoS Negl Trop Dis, vol.8, p.3123, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01307475

N. H. Anderson and D. M. Titterington, Some Methods for Investigating Spatial Clustering, with Epidemiological Applications, J R Stat Soc Ser A Stat Soc, vol.160, pp.87-105, 1997.

S. Liang, B. P. Carlin, and A. E. Gelfand, Analysis of Minnesota colon and rectum cancer point patterns with spatial and nonspatial covariate information, Ann Appl Stat, vol.3, pp.943-62, 2009.

G. M. Jacquez and D. A. Greiling, Local clustering in breast, Int J Health Geogr, vol.2, p.3, 2003.

G. D. Johnson, Small area mapping of prostate cancer incidence in New York State (USA) using fully Bayesian hierarchical modelling, Int J Health Geogr, vol.3, p.29, 2004.

T. Cassetti, L. Rosa, F. Rossi, L. , D. 'alò et al., Cancer incidence in men: a cluster analysis of spatial patterns, BMC Cancer, vol.8, p.344, 2008.

Y. Mao, J. Hu, A. Ugnat, R. Semenciw, S. Fincham et al., Socioeconomic status and lung cancer risk in Canada, Int J Epidemiol, vol.30, pp.809-826, 2001.

P. Botella-rocamora, M. A. Martinez-beneito, and S. Banerjee, A unifying modeling framework for highly multivariate disease mapping: A unifying modeling framework for highly multivariate disease mapping, Stat Med, vol.34, pp.1548-59, 2015.

P. Townsend, Deprivation, J Soc Policy, vol.16, pp.125-171, 1987.

D. Levy and V. Roux, une région très urbaine, une croissance équilibrée. Sud INSEE l'essentiel, 2006.

A. H. Auchincloss, S. Y. Gebreab, C. Mair, D. Roux, and A. V. , A Review of Spatial Methods in Epidemiology, vol.33, pp.107-129, 2000.

C. R. Muirhead, Methods for detecting disease clustering, with consideration of childhood leukaemia, Stat Methods Med Res, vol.15, pp.363-83, 2006.

I. Jung and M. Kulldorff, Theoretical properties of tests for spatial clustering of count data, Can J Stat, vol.35, pp.433-479, 2007.

J. Besag, J. York, and A. Mollié, Bayesian image restoration, with two applications in spatial statistics, Ann Inst Stat Math, vol.43, pp.1-20, 1991.

J. Gaudart, N. Graffeo, D. Coulibaly, G. Barbet, S. Rebaudet et al., SPODT: An R Package to Perform Spatial Partitioning, J Stat Softw, vol.63, pp.1-23, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01208245

L. A. Waller and C. A. Gotway, Applied spatial statistics for public health data, vol.368, 2004.

M. Kulldorff, L. Huang, L. Pickle, and L. Duczmal, An elliptic spatial scan statistic, Stat Med, vol.25, pp.3929-3972, 2006.

A. Gelman, Prior distributions for variance parameters in hierarchical models, Bayesian Anal, vol.1, pp.515-549, 2006.

F. Gerber and R. Furrer, Pitfalls in the Implementation of Bayesian Hierarchical Modeling of Areal Count Data: An Illustration Using BYM and Leroux Models, J Stat Softw, vol.63, 2015.

D. Lemke, V. Mattauch, O. Heidinger, E. Pebesma, and H. Hense, Detecting cancer clusters in a regional population with local cluster tests and Bayesian smoothing methods: a simulation study, Int J Health Geogr, vol.12, p.54, 2013.

D. J. Spiegelhalter, N. G. Best, B. P. Carlin, and A. Van-der-linde, Bayesian measures of model complexity and fit, J R Stat Soc Ser B Stat Methodol, vol.64, pp.583-639, 2002.

M. A. Mmartinez-beneito, A general modelling framework for multivariate disease mapping, Biometrika, vol.100, pp.539-53, 2013.

Y. C. Macnab, On Gaussian Markov random fields and Bayesian disease mapping, Stat Methods Med Res, vol.20, pp.49-68, 2011.

X. Jin, S. Banerjee, and B. P. Carlin, Order-free co-regionalized areal data models with application to multiple-disease mapping, J R Stat Soc Ser B Stat Methodol, vol.69, pp.817-855, 2007.

A. E. Gelfand and P. Vounatsou, Proper multivariate conditional autoregressive models for spatial data analysis, Biostatistics, vol.4, pp.11-16, 2003.

R. Bivand and . Package, Spatial Dependence: Weighting Schemes, Statistics and Models. R package version 0, pp.5-56, 2013.

V. Gómez-rubio, J. Ferrándiz-ferragud, and A. López-quílez, Detecting clusters of disease with R, J Geogr Syst, vol.7, pp.189-206, 2005.

V. Gómez-rubio and A. López-quílez, Statistical methods for the geographical analysis of rare diseases, Rare Dis. Epidemiol, pp.151-71, 2010.

S. Sturtz, U. Ligges, and A. Gelman, R2WinBUGS : A Package for Running WinBUGS from R, J Stat Softw, vol.12, 2005.

A. Guttmann, X. Li, J. Gaudart, Y. Gérard, J. Demongeot et al., Spatial heterogeneity of type I error for local cluster detection tests, Int J Health Geogr, vol.13, p.15, 2014.
URL : https://hal.archives-ouvertes.fr/inserm-00998280

R. M. Assunção and E. A. Reis, A new proposal to adjust Moran's I for population density, Stat Med, vol.18, pp.2147-62, 1999.

R. L. Sherman, K. A. Henry, S. L. Tannenbaum, D. J. Feaster, E. Kobetz et al., Applying Spatial Analysis Tools in Public Health: An Example Using SaTScan to Detect Geographic Targets for Colorectal Cancer Screening Interventions, Prev Chronic Dis, vol.11, 2014.

J. C. Wakefield, N. G. Best, and L. A. Waller, Bayesian approaches to disease mapping, Spatial epidemiology: methods and applications, pp.104-127, 2000.

G. Aamodt, S. O. Samuelsen, and A. Skrondal, A simulation study of three methods for detecting disease clusters, Int J Health Geogr, vol.5, p.15, 2006.

A. Ozonoff, C. Jeffery, J. Manjourides, F. White, L. Pagano et al., Effect of spatial resolution on cluster detection: a simulation study, Int J Health Geogr, vol.6, p.52, 2007.

C. Jeffery, A. Ozonoff, L. F. White, M. Nuno, and M. Pagano, Power to Detect Spatial Disturbances under Different Levels of Geographic Aggregation, J Am Med Inform Assoc, vol.16, pp.847-54, 2009.

S. Goujon-bellec, C. Demoury, A. Guyot-goubin, D. Hémon, and J. Clavel, Detection of clusters of a rare disease over a large territory: performance of cluster detection methods, Int J Health Geogr, vol.10, p.53, 2011.
URL : https://hal.archives-ouvertes.fr/inserm-00637064

G. D. Alton, D. L. Pearl, K. G. Bateman, B. Mcnab, and O. Berke, Comparison of covariate adjustment methods using space-time scan statistics for food animal syndromic surveillance, BMC Vet Res, vol.9, p.231, 2013.

A. Tran, Mapping Disease Incidence in Suburban Areas using Remotely Sensed Data, Am J Epidemiol, vol.156, pp.662-670, 2002.

G. Lyratzopoulos, J. M. Barbiere, B. Rachet, M. Baum, M. R. Thompson et al., Changes over time in socioeconomic inequalities in breast and rectal cancer survival in England and Wales during a 32-year period (1973-2004): the potential role of health care, Ann Oncol, vol.22, pp.1661-1667, 2011.

M. P. Coleman, D. Forman, H. Bryant, J. Butler, B. Rachet et al., (the International Cancer Benchmarking Partnership): an analysis of population-based cancer registry data, Lancet, vol.377, issue.10, pp.62231-62234, 1995.

D. G. Clayton, L. Bernardinelli, and C. Montomoli, Spatial correlation in ecological analysis, Int J Epidemiol, vol.22, pp.1193-202, 1993.

J. Hughes and M. Haran, Dimension reduction and alleviation of confounding for spatial generalized linear mixed models, J R Stat Soc Ser B Stat Methodol, vol.75, pp.139-59, 2013.

B. J. Reich, J. S. Hodges, and V. Zadnik, Effects of Residual Smoothing on the Posterior of the Fixed Effects in Disease-Mapping Models, Biometrics, vol.62, pp.1197-206, 2006.