ArticleJournal of the Royal Society, Interface2017
Improved prediction accuracy for disease risk mapping using Gaussian process stacked generalization.
Article in Journal of the Royal Society, Interface, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 55 papers, 2 of them syntheses that pooled it.
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Who cites it
55 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Twenty-year trends in antimicrobial resistance from aquaculture and fisheries in Asia.Nature communications · 2021Pooled it
- Geospatial estimation of reproductive, maternal, newborn and child health indicators: a systematic review of methodological aspects of studies based on household surveys.International journal of health geographics · 2020Pooled it
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- Avian influenza risk mapping in India using machine learning.Frontiers in veterinary science · 2026Article
- Why the growth of arboviral diseases necessitates a new generation of global risk maps and future projections.PLoS computational biology · 2025Article
- Climate Change and Malaria: A Call for Robust Analytics.medRxiv : the preprint server for health sciences · 2025Article
- Estimating the subnational prevalence of antimicrobial resistant Salmonella enterica serovars Typhi and Paratyphi A infections in 75 endemic countries, 1990-2019: a modelling study.The Lancet. Global health · 2024Article
- Mapping heterogeneity in family planning indicators in Burkina Faso, Kenya, and Nigeria, 2000-2020.BMC medicine · 2024Article
- Global surveillance of antimicrobial resistance in food animals using priority drugs maps.Nature communications · 2024Article
- Comparison of new computational methods for spatial modelling of malaria.Malaria journal · 2023Article
- Evaluating COVID-19-Related Disruptions to Effective Malaria Case Management in 2020-2021 and Its Potential Effects on Malaria Burden in Sub-Saharan Africa.Tropical medicine and infectious disease · 2023Article
- Analysis of noisy transient signals based on Gaussian process regression.Biophysical journal · 2023Article
- PredictingFrontiers in microbiology · 2023Article
- Mapping age- and sex-specific HIV prevalence in adults in sub-Saharan Africa, 2000-2018.BMC medicine · 2022Article
- Bayesian Hierarchical Stacking: Some Models Are (Somewhere) Useful.Bayesian analysis · 2022Article
- Gaussian process forecastsEcology and evolution · 2022Article
- Explaining a series of models by propagating Shapley values.Nature communications · 2022Article
- Mapping local variation in household overcrowding across Africa from 2000 to 2018: a modelling study.The Lancet. Planetary health · 2022Article
- Improving disaggregation models of malaria incidence by ensembling non-linear models of prevalence.Spatial and spatio-temporal epidemiology · 2022Article
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Authors and funding
6 authors.
Funding
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 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 generalization that allows for multiple nonlinear algorithmic mean functions to be jointly embedded within the Gaussian process framework. We apply this method to mapping
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