Modelling local areas of exposure to Schistosoma japonicum in a limited survey data environment

Spatial modelling studies of schistosomiasis (SCH) are now commonplace. Covariate values are commonly extracted at survey locations, where infection does not always take place, resulting in an unknown positional exposure mismatch. The present research aims to: (i) describe the nature of the positional exposure mismatch in modelling SCH helminth infections; (ii) delineate exposure areas to correct for such positional mismatch; and (iii) validate exposure areas using human positive cases

Schistosomiasis, Spatial modeling, Bayesian network, Exposure, Uncertainty, Risk factors