Characterization of partially observed epidemics through Bayesian inference: application to COVID-19

DOI10.1007/s00466-020-01897-zzbMath1465.92129OpenAlexW3092539403WikidataQ100517747 ScholiaQ100517747MaRDI QIDQ2221735

Jaideep Ray, Cosmin Safta, Khachik V. Sargsyan

Publication date: 2 February 2021

Published in: Computational Mechanics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s00466-020-01897-z

zbMATH Keywords

Markov chain Monte Carloinfection rateBayesian frameworkCOVID-19pseudo-marginal MCMCincubation model

Mathematics Subject Classification ID

Epidemiology (92D30)

Uses Software

• GitHub
• Gibbsit
• Covasim

Cites Work

• The pseudo-marginal approach for efficient Monte Carlo computations
• Ensemble samplers with affine invariance
• Several new numerical methods for compressible shear-layer simulations
• Bayesian Updating and Model Class Selection for Hysteretic Structural Models Using Stochastic Simulation
• A Method for Obtaining Short-Term Projections and Lower Bounds on the Size of the AIDS Epidemic
• Using High‐Order Methods on Adaptively Refined Block‐Structured Meshes: Derivatives, Interpolations, and Filters
• An adaptive Metropolis algorithm

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