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 ∼ 1500 dimensions after discretization. To infer these parameters, we use reported data on the number of confirmed, hospitalized, and deceased cases with suitable post-processing in both a deterministic inversion approach with appropriate regularization as a first step, followed by Bayesian inversion with proper prior distributions. To address the curse of dimensionality and the ill-posedness of the high-dimensional inference problem, we propose use of a dimension-independent projected Stein variational gradient descent method, and demonstrate the intrinsic low-dimensionality of the inverse problem. We present inference results with quantified uncertainties for both New Jersey and Texas, which experienced different epidemic phases and patterns. Moreover, we also present forecasting and validation results based on the empirical posterior samples of our inference for the future trajectory of COVID-19.
journal_name
Comput Methods Appl Mech Engjournal_title
Computer methods in applied mechanics and engineeringauthors
Chen P,Wu K,Ghattas Odoi
10.1016/j.cma.2021.114020keywords:
["Bayesian inference","COVID-19","Compartmental model","Epidemics","Forecast under uncertainty","Long-term care facility"]subject
Has Abstractpub_date
2021-11-01 00:00:00pages
114020eissn
0045-7825issn
1879-2138pii
S0045-7825(21)00351-0journal_volume
385pub_type
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