Improved prediction accuracy for disease risk mapping using Gaussian process stacked generalization.

ABSTRACT: Maps of infectious disease-charting spatial variations in the force of infection, degree of endemicity and the burden on human health-provide an essential evidence base to support planning towards global health targets. Contemporary disease mapping efforts have embraced statistical modelling approaches to properly acknowledge uncertainties in both the available measurements and their spatial interpolation. The most common such approach is Gaussian process regression, a mathematical framework composed of two components: a mean function harnessing the predictive power of multiple independent variables, and a covariance function yielding spatio-temporal shrinkage against residual variation from the mean. Though many techniques have been developed to improve the flexibility and fitting of the

SUBMITTER: Bhatt S 

PROVIDER: S-EPMC5636278 | biostudies-literature | 2017 Sep

REPOSITORIES: biostudies-literature