Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-8, 163-166, 2014
http://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XL-8/163/2014/
doi:10.5194/isprsarchives-XL-8-163-2014
© Author(s) 2014. This work is distributed
under the Creative Commons Attribution 3.0 License.
 
27 Nov 2014
Characterization of the Temporal and Spatial Dynamics of the Dengue Epidemic in Northern Sri Lanka
S. Anno1, K. Imaoka2, T. Tadono2, T. Igarashi3, S. Sivaganesh4, S. Kannathasan5, V. Kumaran5, and S. Surendran5 1Shibaura Institute of Technology, Tokyo, Japan
2Japan Aerospace Exploration Agency, Tsukuba, Japan
3Remote Sensing Technology Center of Japan, Tokyo, Japan
4Regional Epidemiologist, Jaffna, Sri Lanka
5University of Jaffna, Jaffna, Sri Lanka
Keywords: Dengue, Ecological and socio-economic and demographic factors, Local Moran LISA statistics, Temporal analysis, Spatial association analysis, Spatial statistical analysis Abstract. Dengue outbreaks are affected by biological, ecological, socio-economic and demographic factors that vary over time and space. These factors have been examined separately, with limited success, and still require clarification. The present study aimed to investigate the spatial and temporal relationships between these factors and dengue outbreaks in the northern region of Sri Lanka. Remote sensing (RS) data gathered from a plurality of satellites: TRMM TMI, Aqua AMSR-E, GCOM-W AMSR2, DMSP SSM/I, DMSP SSMIS, NOAA-19 AMSU, MetOp-A AMSU and GEO IR were used to develop an index comprising rainfall. Humidity (total precipitable water, or vertically integrated water vapor amount) and temperature (surface temperature) data were acquired from the JAXA Satellite Monitoring for Environmental Studies (JASMES) portal which were retrieved and processed from the Aqua/MODIS and Terra/MODIS data. RS data gathered by ALOS/AVNIR-2 were used to detect urbanization, and a digital land cover map was used to extract land cover information. Other data on relevant factors and dengue outbreaks were collected through institutions and extant databases. The analyzed RS data and databases were integrated into geographic information systems, enabling both spatial association analysis and spatial statistical analysis. Our findings show that the combination of ecological factors derived from RS data and socio-economic and demographic factors is suitable for predicting spatial and temporal patterns of dengue outbreaks.
Conference paper (PDF, 565 KB)


Citation: Anno, S., Imaoka, K., Tadono, T., Igarashi, T., Sivaganesh, S., Kannathasan, S., Kumaran, V., and Surendran, S.: Characterization of the Temporal and Spatial Dynamics of the Dengue Epidemic in Northern Sri Lanka, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-8, 163-166, doi:10.5194/isprsarchives-XL-8-163-2014, 2014.

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