Characterization of partially observed epidemics through Bayesian inference [electronic resource] : application to COVID-19

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Tác giả:

Ngôn ngữ: eng

Ký hiệu phân loại: 614.5 Incidence of and public measures to prevent specific diseases and kinds of diseases

Thông tin xuất bản: Washington, D.C. : Oak Ridge, Tenn. : United States. National Nuclear Security Administration ; Distributed by the Office of Scientific and Technical Information, U.S. Dept. of Energy, 2020

Mô tả vật lý: Size: p. 1109-1129 : , digital, PDF file.

Bộ sưu tập: Metadata

ID: 259814

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
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