Spatial and Temporal Variation of Japanese encephalitis Disease and Detection of Disease Hotspots: a Case Study of Gorakhpur District, Uttar Pradesh, India

Abstract

Commission VIII, WG VIII/2 KEY WORDS: Japanese Encephalitis, GIS, Spatial analysis, Clustering, Hotspot ABSTRACT:In recent times, Japanese Encephalitis (JE) has emerged as a serious public health problem. In India, JE outbreaks were recently reported in Uttar Pradesh, Gorakhpur. The present study presents an approach to use GIS for analyzing the reported cases of JE in the Gorakhpur district based on spatial analysis to bring out the spatial and temporal dynamics of the JE epidemic. The study investigates spatiotemporal pattern of the occurrence of disease and detection of the JE hotspot. Spatial patterns of the JE disease can provide an understanding of geographical changes. Geospatial distribution of the JE disease outbreak is being investigated since 2005 in this study. The JE incidence data for the years 2005 to 2010 is used. The data is then geo-coded at block level. Spatial analysis is used to evaluate autocorrelation in JE distribution and to test the cases that are clustered or dispersed in space. The Inverse Distance Weighting interpolation technique is used to predict the pattern of JE incidence distribution prevalent across the study area. Moran's I Index (Moran's I) statistics is used to evaluate autocorrelation in spatial distribution. The Getis-Ord Gi*(d) is used to identify the disease areas. The results represent spatial disease patterns from 2005 to 2010, depicting spatially clustered patterns with significant differences between the blocks. It is observed that the blocks on the built up areas reported higher incidences.

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“…This was used to measure and test the way in which cases in each zone were dispersed or clustered over space. For this, GIS model builder spatial analyst tool was used and interpretations were made according to Verma and Gupta (2014). Moreover, the cluster-outlier detection and hotspot analysis were computed using the same approach for the cases in each zone (Tsai, 2012;Verma & Gupta, 2014).…”

“…For this, GIS model builder spatial analyst tool was used and interpretations were made according to Verma and Gupta (2014). Moreover, the cluster-outlier detection and hotspot analysis were computed using the same approach for the cases in each zone (Tsai, 2012;Verma & Gupta, 2014). A significance level with p < .05 was used to indicate significant clusters of local autocorrelations.…”