  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
that of Gaussian process regression, a mathematical framework comprised 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 covariance
function, models for the mean function have typically been restricted to simple
linear terms. For infectious diseases, known to be driven by complex
interactions between environmental and socio-economic factors, improved
modelling of the mean function can greatly boost predictive power. Here we
present an ensemble approach based on stacked generalisation that allows for
multiple, non-linear algorithmic mean functions to be jointly embedded within
the Gaussian process framework. We apply this method to mapping Plasmodium
falciparum prevalence data in Sub-Saharan Africa and show that the generalised
ensemble approach markedly out-performs any individual method.
