The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLI-B8
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B8, 215–221, 2016
https://doi.org/10.5194/isprs-archives-XLI-B8-215-2016
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B8, 215–221, 2016
https://doi.org/10.5194/isprs-archives-XLI-B8-215-2016

  22 Jun 2016

22 Jun 2016

LINKING SATELLITE REMOTE SENSING BASED ENVIRONMENTAL PREDICTORS TO DISEASE: AN APPLICATION TO THE SPATIOTEMPORAL MODELLING OF SCHISTOSOMIASIS IN GHANA

M. Wrable1, A. Liss1, A. Kulinkina1, M. Koch2, N. K. Biritwum3, A. Ofosu4, K. C. Kosinski5, D. M. Gute1, and E. N. Naumova6 M. Wrable et al.
  • 1Dept. of Civil and Environmental Engineering, Tufts University, 200 College Ave, Medford, USA
  • 2Center for Remote Sensing, Boston University, Boston, USA
  • 3Neglected Tropical Disease Control Program, Ghana Health Service, Accra, Ghana
  • 4Policy, Planning, Monitoring, and Evaluation Division, Ghana Health Service, Accra, Ghana
  • 5Dept. of Community Health, Tufts University, 574 Boston Avenue, Medford, USA
  • 6Friedman School of Nutrition Science and Policy, Tufts University, 150 Harrison Ave, Boston, USA

Keywords: Remote Sensing, Environmental Variables, Climate Classification, Spatiotemporal Analysis, Schistosomiasis Risk

Abstract. 90% of the worldwide schistosomiasis burden falls on sub-Saharan Africa. Control efforts are often based on infrequent, small-scale health surveys, which are expensive and logistically difficult to conduct. Use of satellite imagery to predictively model infectious disease transmission has great potential for public health applications. Transmission of schistosomiasis requires specific environmental conditions to sustain freshwater snails, however has unknown seasonality, and is difficult to study due to a long lag between infection and clinical symptoms. To overcome this, we employed a comprehensive 8-year time-series built from remote sensing feeds. The purely environmental predictor variables: accumulated precipitation, land surface temperature, vegetative growth indices, and climate zones created from a novel climate regionalization technique, were regressed against 8 years of national surveillance data in Ghana. All data were aggregated temporally into monthly observations, and spatially at the level of administrative districts. The result of an initial mixed effects model had 41% explained variance overall. Stratification by climate zone brought the R2 as high as 50% for major zones and as high as 59% for minor zones. This can lead to a predictive risk model used to develop a decision support framework to design treatment schemes and direct scarce resources to areas with the highest risk of infection. This framework can be applied to diseases sensitive to climate or to locations where remote sensing would be better suited than health surveys.