Google Maps Based Spatial Clustering Method For Infectious Diseases

Part I Google Maps based spatial clustering method for infectious diseasesObjective:To explore the method for communities (administrative villages) based spatial clustering withGoogle Maps.Methods:The geocoding technology of Google Maps, the algorithm for computation geometry and textparsing were used for data transforming, which converted the reported tuberculosis data during2005-2010for Zhenhai district, Ningbo municipality and simulated data into the communities andadministrative villages based statistics. Then scan statistics implemented by SaTScan wererespectively performed for the neighborhoods based and the communities (administrative villages)based spatial clustering analysis.Results:Both the neighborhoods based and the administrative villages based spatial clusteringanalyses detected the tuberculosis spatial clustering in Jiaochuan neighborhood, Zhenhai districtduring2005-2010, while the administrative villages based analysis pointed out the main clusteringregions were lying in Zhongguanlu village, Wulipai village and Yufan community. Furthermore,the results for the analysis of simulated data showed that the communities based analysis could findthe local clustering regions in Zhabaoshan neighborhood. It was mutually complementary with theneighborhood based analysis that tended to find the large clustering regions.Conclusion:Google Maps and its geocoding technology were applicable to the spatial clustering analysisfor infectious diseases and it was meaningful to detect clustering regions with communities(administrative villages) based spatial clustering methods. Part II The implementation of control charts in Microsoft ExcelObjective:To implement five major control charts (including Shewhart control chart, long-time andshort-time baseline data based cumulative sum control charts, moving average control chart andexponentially weighted moving average control chart) for infectious diseases’ early warning.Methods:Statistics and control limits for early warning were calculated by Excel’s functions.Results:Monthly reported data of the hepatitis B during2006to2011for Zhenhai district, Ningbomunicipality were used as templates, and tables and graphs in Excel could be used for warninganalysis.Conclusion:The Excel program ExcelCC for control chart warning models may be a useful tool forretrospective and prospective warning analysis for infectious diseases.