Spatial mapping of temporal risk to improve prevention measures: A case study of dengue epidemic in Lahore. (June 2017)

• Record Type: Journal Article
• Authors: Hafeez, Sidrah; Amin, Muhammad; Munir, Bilal Ahmed
• Is Part Of: Spatial and spatio-temporal epidemiology. Volume 21(2017)
• Page Start: 77
• Page End: 85
• Publication Date: 2017-06
• Subjects: Spatial autocorrelation -- Risk clusters -- Spatial epidemiology -- Infectious diseases -- GIS; Epidemiology -- Statistical methods -- Periodicals; Epidemiology -- Periodicals; 614.4072
• DOI: 10.1016/j.sste.2017.04.001
• Languages: English
• ISSNs: 1877-5845

Abstract

Abstract : Background: Dengue is identified as serious vector born infectious disease by WHO, threating around 2.5 billion people around the globe. Pakistan is facing dengue epidemic since 1994 but 2010 and 2011 dengue outbreaks were worst. During 2011 dengue outbreak 22, 562 cases were reported and 363 died due to this fatal infection in Pakistan. In this study, Lahore District was chosen as it was severely affected in 2011 dengue outbreak with 14, 000 reported cases and 300 deaths. There is no vaccine developed yet for the disease control, so only effective early warning, prevention and control measures can reduce the potential disease risk. Methods: This study proposes a method for detecting spatial autocorrelation of temporal dynamics of disease using Local Index of Spatial Autocorrelation (LISA) using three temporal indices: (a) how often the dengue cases occur, frequency index; (b) how long the epidemic wave prevails, duration index; (c) how significant dengue cases occur in successive periods, severity index. Overlay analysis of LISA value for each temporal index resulted in eight risk types. Results: The mapping of spatio-temporal risk indices and their overlay analysis identified that 10.6% area of Lahore (184.3 km 2 and population density 119, 110 persons/km 2 ) had high values for frequency, duration, and severity index ( p < 0.05) and 16% area (having 25% population) is at potential risk of dengue. Conclusion: Spatial risk identification by using local spatial-autocorrelation helps in identifying other possible causes of disease risk and further strategic planning for prevention and control measures.