The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Publications Copernicus
Articles | Volume XL-8
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-8, 163–166, 2014
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-8, 163–166, 2014

  27 Nov 2014

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 S. Anno et al.
  • 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.