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

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 region of New Mexico

Authors:
 [1];  [1];  [1]
  1. Sandia National Lab. (SNL-CA), Livermore, CA (United States)
Publication Date:
Research Org.:
Sandia National Lab. (SNL-CA), Livermore, CA (United States)
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA)
OSTI Identifier:
1670756
Report Number(s):
SAND2020-8283J
Journal ID: ISSN 0178-7675; 689860
Grant/Contract Number:  
AC04-94AL85000
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
Computational Mechanics
Additional Journal Information:
Journal Name: Computational Mechanics; Journal ID: ISSN 0178-7675
Publisher:
Springer
Country of Publication:
United States
Language:
English
Subject:
60 APPLIED LIFE SCIENCES

Citation Formats

Safta, Cosmin, Ray, Jaideep, and Sargsyan, Khachik. Characterization of partially observed epidemics through Bayesian inference: application to COVID-19. United States: N. p., 2020. Web. doi:10.1007/s00466-020-01897-z.
Safta, Cosmin, Ray, Jaideep, & Sargsyan, Khachik. Characterization of partially observed epidemics through Bayesian inference: application to COVID-19. United States. doi:10.1007/s00466-020-01897-z.
Safta, Cosmin, Ray, Jaideep, and Sargsyan, Khachik. Wed . "Characterization of partially observed epidemics through Bayesian inference: application to COVID-19". United States. doi:10.1007/s00466-020-01897-z.
@article{osti_1670756,
title = {Characterization of partially observed epidemics through Bayesian inference: application to COVID-19},
author = {Safta, Cosmin and Ray, Jaideep and Sargsyan, Khachik},
abstractNote = {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},
doi = {10.1007/s00466-020-01897-z},
journal = {Computational Mechanics},
issn = {0178-7675},
number = ,
volume = ,
place = {United States},
year = {2020},
month = {10}
}

Journal Article:
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