Application of Gis in Temporal and Spatial Analyses of Dengue Fever Outbreak : Case of Rio de Janeiro, Brazil

Abstract: Since Dengue fever (DF) and its related forms, Dengue Hemorrhagic fever (DHF) and Dengue Shock Syndrome (DSS) have become important health concerns worldwide, it is also imperative to develop methods which will help in the analysis of the incidences. Dengue fever cases are growing in number as it also invades widely, affecting larger number of countries and crossing climatic boundaries. Considering that the disease as of now has neither an effective vaccine nor a cure, monitoring in order to prevent or control is the resorted alternative. GIS and its related technologies offer a wealth of interesting capabilities towards achieving this goal. The intention of this study was to develop methods to describe dengue fever outbreaks taking Rio de Janeiro, Brazil as a case study. Careful study of Census data with appropriate attributes was made to find out their potential influence on dengue fever incidence in the various regions or census districts. Dengue incidence data from year 2000 to year 2008 reported by the municipal secretariat of Rio was used to extract the necessary census districts. Base map files in MapInfo format were converted to shape files.  Using ArcGIS it was possible to merge the dengue fever incidence data with the available base map file of the City of Rio according to corresponding census districts. Choropleth maps were then created using different attributes from which patterns and trends could be used to describe the characteristic of the outbreak with respect to the socio-economic conditions. Incidence data were also plotted in Excel to see temporal variations. Cluster analysis were performed with the Moran I technique on critical periods and years of dengue outbreak. Using the square root of dengue incidence from January to April 2002 and 2008, inverse distance was selected as the conceptualised spatial relationship, Euclidean distance as the distance method. More detailed analyses were then done on the selected critical years of dengue outbreak, (years 2002 and 2008), to investigate the influence of socio-economic variables on dengue incidence per census district.   Dengue incidence rate appeared to be higher during the rainy and warmer months between December and May. Outbreaks of dengue occurred in years 2002 and 2008 over the study period of year 2000 to 2008. Some factors included in the census data were influential in the dengue prevalence according to districts. Satisfactory results can be achieved by using this strategy as a quick method for assessing potential dengue attack, spread and possible enabling conditions. The method has the advantage where there is limited access to field work, less financial means for acquisition of data and other vital resources. A number of difficulties were encountered during the study however and leaves areas where further work can be done for improvements. More variables would be required in order to make a complete and comprehensive description of influential conditions and factors.  There is still a gap in the analytical tools required for multi-dimensional investigations as the ones encountered in this study.  It is vital to integrate ‘GPS’ and ‘Remote Sensing’ in order to obtain a variety of up-to-date data with higher resolution.