ArticleComputational mechanics2020
Bayesian-based predictions of COVID-19 evolution in Texas using multispecies mixture-theoretic continuum models.
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 16 papers.
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Who cites it
16 citing papers in PubMed.
- Uncertainty quantification in mechanistic epidemic models via cross-entropy approximate Bayesian computation.Nonlinear dynamics · 2023Article
- Towards integration of time-resolved confocal microscopy of a 3D in vitro microfluidic platform with a hybrid multiscale model of tumor angiogenesis.PLoS computational biology · 2023Article
- Modeling nonlocal behavior in epidemics via a reaction-diffusion system incorporating population movement along a network.Computer methods in applied mechanics and engineering · 2022Article
- Delay differential equations for the spatially resolved simulation of epidemics with specific application to COVID-19.Mathematical methods in the applied sciences · 2022Article
- Article
- Modeling the Waves of Covid-19.Acta biotheoretica · 2021Article
- Bayesian inference of heterogeneous epidemic models: Application to COVID-19 spread accounting for long-term care facilities.Computer methods in applied mechanics and engineering · 2021Article
- News Sentiment Informed Time-series Analyzing AI (SITALA) to curb the spread of COVID-19 in Houston.Expert systems with applications · 2021Article
- Spatio-temporal predictive modeling framework for infectious disease spread.Scientific reports · 2021Article
- Assessing the Spatio-temporal Spread of COVID-19 via Compartmental Models with Diffusion in Italy, USA, and Brazil.Archives of computational methods in engineering : state of the art reviews · 2021Article
- Adaptive mesh refinement and coarsening for diffusion-reaction epidemiological models.Computational mechanics · 2021Article
- Visualizing the invisible: The effect of asymptomatic transmission on the outbreak dynamics of COVID-19.Computer methods in applied mechanics and engineering · 2020Article
- Data-driven modeling of COVID-19-Lessons learned.Extreme Mechanics Letters · 2020Review
- Visualizing the invisible: The effect of asymptomatic transmission on the outbreak dynamics of COVID-19.medRxiv : the preprint server for health sciences · 2020Article
- Is it safe to lift COVID-19 travel bans? The Newfoundland story.medRxiv : the preprint server for health sciences · 2020Article
- Is it safe to lift COVID-19 travel bans? The Newfoundland story.Computational mechanics · 2020Article
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Authors and funding
3 authors.
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No grant is acknowledged in the PubMed record.
Abstract
We consider a mixture-theoretic continuum model of the spread of COVID-19 in Texas. The model consists of multiple coupled partial differential reaction-diffusion equations governing the evolution of susceptible, exposed, infectious, recovered, and deceased fractions of the total population in a given region. We consider the problem of model calibration, validation, and prediction following a Bayesian learning approach implemented in OPAL (the Occam Plausibility Algorithm). Our goal is to incorporate COVID-19 data to calibrate the model in real-time and make meaningful predictions and specify the confidence level in the prediction by quantifying the uncertainty in key quantities of interests. Our results show smaller mortality rates in Texas than what is reported in the literature. We predict 7003 deceased cases by September 1, 2020 in Texas with
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