Gaussian process emulators for spatial individual‐level models of infectious disease

DOI10.1002/cjs.11304zbMath1353.92008OpenAlexW2555286646MaRDI QIDQ5507371

Rob Deardon, Gyanendra Pokharel

Publication date: 19 December 2016

Published in: Canadian Journal of Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1002/cjs.11304

zbMATH Keywords

Gaussian processemulatorsspatial modelsdisease modelstomato spotted wilt virus

Mathematics Subject Classification ID

Epidemiology (92D30) Applications of statistics to biology and medical sciences; meta analysis (62P10) General biostatistics (92B15) Bayesian inference (62F15)

Related Items (2)

Dynamic Bayesian influenza forecasting in the United States with hierarchical discrepancy (with discussion) ⋮ Epidemics, the Ising-model and percolation theory: a comprehensive review focused on Covid-19

Uses Software

• mlegp
• pomp

Cites Work

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• Bayesian emulation of complex multi-output and dynamic computer models
• Design and analysis of computer experiments. With comments and a rejoinder by the authors
• Linearized forms of individual-level models for large-scale spatial infectious disease systems
• Computer model validation with functional output
• Bayesian Calibration of Computer Models
• Estimating the Transmission Dynamics of Streptococcus pneumoniae from Strain Prevalence Data
• Adaptive approximate Bayesian computation
• Bayesian Forecasting for Complex Systems Using Computer Simulators
• A dynamic modelling strategy for Bayesian computer model emulation

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