Peter W. Gething

TL;DR: This paper takes an important health metric in Kenya (the proportion of outpatient treatments for malaria (MP) from the national HMIS database and predicts the values of MP at facilities where monthly records are missing, using three different kriging methodologies to make cross-validation predictions of MP.

TL;DR: Predicting years of schooling across five by five kilometre grids generates estimates of average educational attainment by age and sex at subnational levels, improving the ability of decision-makers to plan the precisely targeted interventions that will be necessary to deliver progress during the era of the Sustainable Development Goals.

TL;DR: Bayesian geostatistics (BG) is a method for finding a large sample of maps that can explain a dataset, in which maps that do a better job of explaining the data are more likely to be represented.

TL;DR: The maps showed that malaria transmission in Somalia varied from hypo- to meso-endemic, and even after including the selected covariates in the model, there still remained a considerable amount of unexplained spatial variation in parasite prevalence, indicating effects of other factors not captured in the study.

TL;DR: A Bayesian statistical procedure is developed combining functional regression-based model emulation with Markov Chain Monte Carlo sampling to calibrate three selected microsimulation models against a purpose-built data set of age-structured prevalence and incidence counts, which allows the generation of ensemble forecasts of the prevalence–incidence relationship stratified by age.