ArticleComputer methods in applied mechanics and engineering2021
Bayesian inference of heterogeneous epidemic models: Application to COVID-19 spread accounting for long-term care facilities.
Article in Computer methods in applied mechanics and engineering, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- A scalable variational method for estimating the latent infection-rate field of an outbreak.PloS one · 2026Article
- Detecting outbreaks using a spatial latent field.PloS one · 2025Article
- A vaccination-based COVID-19 model: Analysis and prediction using Hamiltonian Monte Carlo.Heliyon · 2024Article
- Inference on a Multi-Patch Epidemic Model with Partial Mobility, Residency, and Demography: Case of the 2020 COVID-19 Outbreak in Hermosillo, Mexico.Entropy (Basel, Switzerland) · 2023Article
- Data-driven contact network models of COVID-19 reveal trade-offs between costs and infections for optimal local containment policies.Cities (London, England) · 2022Article
- A general framework for quantifying uncertainty at scale.Communications engineering · 2022Article
Corrections and comments
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Authors and funding
3 authors.
Funding
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Abstract
We propose a high dimensional Bayesian inference framework for learning heterogeneous dynamics of a COVID-19 model, with a specific application to the dynamics and severity of COVID-19 inside and outside long-term care (LTC) facilities. We develop a heterogeneous compartmental model that accounts for the heterogeneity of the time-varying spread and severity of COVID-19 inside and outside LTC facilities, which is characterized by time-dependent stochastic processes and time-independent parameters in
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