Objective: To characterize the frequency, incidence and severity of dengue fever in Suriname and to detect historic clusters of disease by integrating epidemiological data into a spatial visualization platform.
Methods: The frequency, incidence and severity of all reported dengue fever (DF) and dengue haemorrhagic fever (DHF) cases in Suriname from 2001 to 2012 were calculated and stratified by demographic factors. Using a geographic information systems (GIS) platform, we visualized the distribution of DF cases and used Moran’s I to detect autocorrelation. Furthermore, a retrospective spatial Poisson probability model was used to identify local clusters of DF within Suriname. Local clusters were divided into neighbourhoods and individual DF cases were mapped to the street level.
Results: In Suriname, cases of DF emerge in cyclical patterns (three to five years) with seasonal peaks following the short and the long rainy season. Chi-squared analysis indicated a statistically significant (p < 0.05) difference between age group, ethnicity and district and the onset of DHF. The spatial analysis detected spatial autocorrelation and four statistically significant (p < 0.05) clusters were identified in the two most populated districts of Paramaribo and Wanica.
Conclusion: In Suriname, identification of demographic and environmental risk factors that contribute to the development of DHF is essential to determine how preventive action can be more effectively allocated. The integration of epidemiological data into a GIS platform allowed for the identification of historic epidemiological clusters of dengue which will be used to guide environmental health studies in Suriname.