Linking satellite remote sensing based environmental predictors to disease: An application to the spatiotemporal modelling of schistosomiasis in Ghana.

Wrable, Madeline R.

Liss, Alexander.

Kulinkina, Alexandra V.

Koch, Magaly.

Biritwum, N.K.

Ofosu, A.

Kosinski, Karen C.

Gute, David M.

Naumova, Elena N.

2016

Description
  • 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 environmenta... read more
This object is in collection Subject Permanent URL Citation
  • Wrable, M., Liss, A., Kulinkina, A., Koch, M., Biritwum, N. K., Ofosu, A., Kosinski, K. C., Gute, D. M., and Naumova, E. N.: LINKING SATELLITE REMOTE SENSING BASED ENVIRONMENTAL PREDICTORS TO DISEASE: AN APPLICATION TO THE SPATIOTEMPORAL MODELLING OF SCHISTOSOMIASIS IN GHANA, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B8, 215-221, doi:10.5194/isprs-archives-XLI-B8-215-2016, 2016.
ID:
2801pt54g
To Cite:
DCA Citation Guide    EndNote
Usage:
Detailed Rights