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

Journal Article · · Computational Mechanics
 [1];  [1];  [1]
  1. Sandia National Lab. (SNL-CA), Livermore, CA (United States)
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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 region of New Mexico

Research Organization:
Sandia National Laboratories (SNL-CA), Livermore, CA (United States)
Sponsoring Organization:
USDOE National Nuclear Security Administration (NNSA)
Grant/Contract Number:
AC04-94AL85000
OSTI ID:
1670756
Report Number(s):
SAND2020--8283J; 689860
Journal Information:
Computational Mechanics, Journal Name: Computational Mechanics Journal Issue: 5 Vol. 66; ISSN 0178-7675
Publisher:
SpringerCopyright Statement
Country of Publication:
United States
Language:
English

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60 APPLIED LIFE SCIENCES