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.

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

Remote sensing.
Tufts University. Department of Civil and Environmental Engineering.
Permanent URL
Original publication
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: tufts:18595
To Cite: DCA Citation Guide
Usage: Detailed Rights