Hotspot Detection of Dengue Fever Outbreaks Using DBSCAN Algorithm

To control infectious diseases, epidemiologic information and useful clustering techniques can be integrated to determine the potential areas of disease outbreaks based on daily surveillance information. In this paper, we present the application of DBSCAN in disease surveillance. The algorithm depends heavily on density based concept of clustering. It is designed to identify clusters of different shapes and patterns. In 2015, Delhi had its worst outbreak since 2006 with over 15,000 cases (By WHO facts 2016) and therefore, to reduce the extravagant risk of dengue transmission in Delhi, perform active surveillance and intercession measures are needed to control the potential outbreaks of dengue. We did a in depth study and did experimental evaluation to known the effectiveness and efficiency of DBSCAN using case information of reported Dengue Fever (DF) incidences for the study period 2011-2013 which was maintained in the health department of Municipal Corporation of Delhi (MCD). The present study has been found to be successful in determining the hotspot of DF. Silhouette Coefficient has been calculated so that the accuracy of cluster detection can be determined. These findings have important public health implications for the control and prevention of DF incidences.