Spatiotemporal clustering of dengue cases in Thiruvananthapuram district, Kerala

Abstract

Background: Dengue cases are increasing in Kerala since 2010. Information on clustering of cases across locations and time periods is vital for disease surveillance and timely control. Objectives: The objective is to study spatiotemporal clustering of dengue cases and their climatic and physioenvironmental correlates in Thiruvananthapuram district during 2010–2014. Methods: Health department data on reported cases of dengue were obtained from January 2011 to June 2014. Cases were individually geocoded, using Google Earth. Moran's I index was estimated to analyze spatial autocorrelation using GeoDa software. Space–time clustering across 178 geo-divisions within the district was analyzed using SaTScan software. Correlation analysis was done for space–time clustering with climatic variables. Results: Definite spatial and temporal trends were found on analysis of a total of 8279 dengue cases. Significant spatial autocorrelation (Moran's I = 0.32, P< 0.01) and space–time clusters with very high log-likelihood ratios (P < 0.01) were found across geo-divisions. Pallichal panchayat was the most likely cluster in every year. The monthly incidence of dengue cases showed a significant positive association (P < 0.05) with a 2-month lag of mean minimum temperature (ρ = 0.39), 1-month lag of rainfall (ρ = 0.33), and 1-month lag of humidity (ρ = 0.38). Dengue occurrences showed an inverse association (P < 0.01) with mean maximum temperatures of the respective months (ρ= -0.48). Conclusion: Spatial analysis using epidemiological tools reveals spatial and temporal clustering of dengue cases within the district and their association with climatic parameters. This information can be used in controlling outbreaks in the future. This work upholds scope and feasibility of geospatial research in public health in India.

Keywords

• Climate
• clustering
• dengue
• geospatial
• Kerala