Voronoi distance based prospective space-time scans for point data sets: a dengue fever cluster analysis in a southeast Brazilian town

详细信息    查看全文

• 作者：Luiz H Duczmal (1)
  Gladston JP Moreira (2) (6)
  Denise Burgarelli (3)
  Ricardo HC Takahashi (3)
  Flávia CO Magalh?es (4)
  Emerson C Bodevan (5)
  
• 刊名：International Journal of Health Geographics
• 出版年：2011
• 出版时间：December 2011
• 年：2011
• 卷：10
• 期：1
• 全文大小：662KB
• 参考文献：1. Lawson A, Biggeri A, B?hning¨ D: / Disease mapping and risk assessment for public health. New York: John Wiley and Sons; 1999.
  2. Duczmal LH, Buckeridge DL: A workflow spatial scan statistic. / Statistics in Medicine 2006, 25:743-54. CrossRef
  3. Kulldorff M, Heffernan R, Hartman J, Assun??o R, Mostashari F: A Space Time Permutation Scan Statistic for Disease Outbreak Detection. / PLoS Med 2005.,2(3):
  4. Kulldorff M, Huang M, Pickle L, Duczmal LH: An elliptic scan statistic. / Statistics in Medicine 2006,25(22):3929-943. CrossRef
  5. Kulldorff M, Mostashari F, Duczmal LH, Katherine YW, Kleinman K, Platt R: Multivariate scan statistics for disease surveillance. / Statistics in Medicine 2007,26(8):1824-833. CrossRef
  6. Neill DB: An empirical comparison of spatial scan statistics for outbreak detection. / International Journal of Health Geographics 2009, 8:20. CrossRef
  7. Kulldorff M: A spatial scan statistic. / Communications in Statistics: Theory and Methods 1997, 26:1481-496. CrossRef
  8. Kulldorff M: Spatial scan statistics: Models, calculations and applications. In / Scan Statistics and Applications. Edited by: Glaz, Balakrishnan. Boston: Birkhauser; 1999:303-22. CrossRef
  9. TerraSeer [http://www.terraseer.com]
  10. Kulldorff M, Nagarwalla N: Spatial disease clusters: Detection and inference. / Statistics in Medicine 1995,14(8):799-10. CrossRef
  11. Sahajpal R, Ramaraju G, Bhatt V: Applying niching genetic algorithms for multiple cluster discovery in spatial analysis. / Int Conf Intelligent Sensing and Information Processing 2004.
  12. Conley J, Gahegan M, Macgill J: A genetic approach to detecting clusters in point-data sets. / Geographical Analysis 2005, 37:286-14. CrossRef
  13. Duczmal LH, Assun??o R: A simulated annealing strategy for the detection of arbitrarily shaped spatial clusters. / Computational Statistics & Data Analysis 2004,45(2):269-86. CrossRef
  14. Patil GP, Taillie C: Upper level set scan statistic for detecting arbitrarily shaped hotspots. / Environmental and Ecological Statistics 2004, 11:183-97. CrossRef
  15. Tango T, Takahashi K: A flexibly shaped spatial scan statistic for detecting clusters. / International Journal of Health Geographics 2005, 4:11. CrossRef
  16. Duczmal LH, Kulldorff M, Huang L: Evaluation of spatial scan statistics for irregularly shaped clusters. / Journal of Computational and Graphical Statistics 2006,15(2):428-42. CrossRef
  17. Duczmal LH, Can?ado ALF, Takahashi RHC: Geographic delineation of disease clusters through multi-objective optimization. / Journal of Computational & Graphical Statistics 2008, 17:243-62. CrossRef
  18. Neill DB: Fast and flexible outbreak detection by linear-time subset scanning [abstract]. / Advances in Disease Surveillance 2008, 5:48.
  19. Neill DB: Fast subset sums for multivariate Bayesian scan statistics [abstract]. / Proceedings of the 2009 International Society for Disease Surveillance Annual Conference 2010.
  20. Duczmal LH, Can?ado ALF, Takahashi RHC, Bessegato LF: A genetic algorithm for irregularly shaped spatial scan statistics. / Computational Statistics and Data Analysis 2007, 52:43-2. CrossRef
  21. Yiannakoulias N, Rosychuk R, Hodgson J: Adaptations for finding irregularly shaped disease clusters. / International Journal of Health Geographics 2007.,6(28):
  22. Duarte A, Can?ado A, Duczmal L, Ferreira S: Internal cohesion and geometric shape of spatial clusters. / Environmental and Ecological Statistics 2010, 17:203-29. CrossRef
  23. Can?ado A, Duarte A, Duczmal L, Ferreira S, Fonseca C, Gontijo E: Penalized likelihood and multi-objective spatial scans for the detection and inference of irregular clusters. / International Journal of Health Geographics 2010, 9:1-7. CrossRef
  24. Kulldorff M: Prospective time periodic geographical disease surveillance using a scan statistic. / Journal of The Royal Statistical Society Series A 2001, 164:61-2.
  25. Iyengar VS: Space-Time Clusters with Flexible Shapes. / MMWR Morb Mortal Wkly Rep 2005, (Suppl 54):71-6.
  26. Takahashi K, Kulldorff M, Tango T, Yin K: A flexibly shaped space-time scan statistic for disease outbreak detection and monitoring. / International Journal of Health Geographics 2008, 7:14. CrossRef
  27. Demattei C, Cucala L: Multiple spatio-temporal cluster detection for case event data: an ordering-based approach. / Communications in Statistics-Theory and Methods 2011,40(2):358-72. CrossRef
  28. Robertson C, Nelson T: Review of software for space-time disease surveillance. / International Journal of Health Geographics 2010, 9:1-. CrossRef
  29. Dwass M: Modified Randomization Tests for Nonparametric Hypotheses. / Ann Math Statist 1957, 28:181-87. CrossRef
  30. Assun??o R, Costa M, Tavares A, Ferreira S: Fast detection of arbitrarily shaped disease clusters. / Statistics in Medicine 2006, 25:723-42. CrossRef
  31. Wieland SC, Brownstein JS, Berger B, Mandl KD: Density-equalizing Euclidean minimum spanning trees for the detection of all disease cluster shapes. / Proceedings of the National Academy of Sciences 2007,104(22):9404-409. CrossRef
  32. Kulldorff M, Tango T, Park PJ: Power comparisons for disease clustering tests. / Computational Statistics & Data Analysis 2003,42(4):665-84. CrossRef
  33. Pessanha JEM: Dengue in Belo Horizonte: a population-based seroepidemiological survey (2006-007), Study of virus vectors (2007). Evaluation of the national dengue control plan(2008). (In Portuguese). [http://hdl.handle.net/1843/ECJS-85FHKN] / PhD thesis. Universidade Federal de Minas Gerais; 2010.
  34. Portal da Saúde [http://portal.saude.gov.br/portal/saude/cidadao/area.cfm?id_area=149]
  35. Chang AY, Parrales ME, Jimenez J, Sobieszczyk ME, Hammer SM, Copenhaver DJ, Kulkarni RP: Combining Google Earth and GIS mapping technologies in a dengue surveillance system for developing countries. / International Journal of Health Geographics 2009, 8:49. CrossRef
  
• 作者单位：Luiz H Duczmal (1)
  Gladston JP Moreira (2) (6)
  Denise Burgarelli (3)
  Ricardo HC Takahashi (3)
  Flávia CO Magalh?es (4)
  Emerson C Bodevan (5)
  
  1. Department of Statistics, Universidade Federal de Minas Gerais, Campus Pampulha, Belo Horizonte/MG, Brazil
  2. Department of Mathematics, Universidade Federal de Ouro Preto, Ouro Preto/MG, Brazil
  6. Department of Electrical Engineering, Universidade Federal de Minas Gerais, Campus Pampulha, Belo Horizonte/MG, Brazil
  3. Department of Mathematics, Universidade Federal de Minas Gerais, Campus Pampulha, Belo Horizonte/MG, Brazil
  4. Medical Doctor, Prefeitura de Belo Horizonte/MG, Brazil
  5. Department of Mathematics and Statistics, Universidade Federal dos Vales do Jequitinhonha e Mucuri, Diamantina/MG, Brazil
  
• ISSN：1476-072X

文摘

Background The Prospective Space-Time scan statistic (PST) is widely used for the evaluation of space-time clusters of point event data. Usually a window of cylindrical shape is employed, with a circular or elliptical base in the space domain. Recently, the concept of Minimum Spanning Tree (MST) was applied to specify the set of potential clusters, through the Density-Equalizing Euclidean MST (DEEMST) method, for the detection of arbitrarily shaped clusters. The original map is cartogram transformed, such that the control points are spread uniformly. That method is quite effective, but the cartogram construction is computationally expensive and complicated. Results A fast method for the detection and inference of point data set space-time disease clusters is presented, the Voronoi Based Scan (VBScan). A Voronoi diagram is built for points representing population individuals (cases and controls). The number of Voronoi cells boundaries intercepted by the line segment joining two cases points defines the Voronoi distance between those points. That distance is used to approximate the density of the heterogeneous population and build the Voronoi distance MST linking the cases. The successive removal of edges from the Voronoi distance MST generates sub-trees which are the potential space-time clusters. Finally, those clusters are evaluated through the scan statistic. Monte Carlo replications of the original data are used to evaluate the significance of the clusters. An application for dengue fever in a small Brazilian city is presented. Conclusions The ability to promptly detect space-time clusters of disease outbreaks, when the number of individuals is large, was shown to be feasible, due to the reduced computational load of VBScan. Instead of changing the map, VBScan modifies the metric used to define the distance between cases, without requiring the cartogram construction. Numerical simulations showed that VBScan has higher power of detection, sensitivity and positive predicted value than the Elliptic PST. Furthermore, as VBScan also incorporates topological information from the point neighborhood structure, in addition to the usual geometric information, it is more robust than purely geometric methods such as the elliptic scan. Those advantages were illustrated in a real setting for dengue fever space-time clusters.