ArticleComputational mechanics2020
Characterization of partially observed epidemics through Bayesian inference: application to COVID-19.
Article in Computational mechanics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 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
- Hybrid prediction of infections and deaths due to COVID-19 in two Colombian data series.PloS one · 2023Article
- Forecasting Multi-Wave Epidemics Through Bayesian Inference.Archives of computational methods in engineering : state of the art reviews · 2021Article
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Authors and funding
3 authors.
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Abstract
We demonstrate a Bayesian method for the "real-time" characterization and forecasting of partially observed COVID-19 epidemic. Characterization is the estimation of infection spread parameters using daily counts of symptomatic patients. The method is designed to help guide medical resource allocation in the early epoch of the outbreak. The estimation problem is posed as one of Bayesian inference and solved using a Markov chain Monte Carlo technique. The data used in this study was sourced before the arrival of the second wave of infection in July 2020. The proposed modeling approach, when applied at the country level, generally provides accurate forecasts at the regional, state and country level. The epidemiological model detected the flattening of the curve in California, after public health measures were instituted. The method also detected different disease dynamics when applied to specific regions of New Mexico.
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