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Volume 27, Number 3—March 2021
Research

Daily Forecasting of Regional Epidemics of Coronavirus Disease with Bayesian Uncertainty Quantification, United States

Yen Ting LinComments to Author , Jacob Neumann, Ely F. Miller, Richard G. Posner, Abhishek Mallela, Cosmin Safta, Jaideep Ray, Gautam Thakur, Supriya Chinthavali, and William S. Hlavacek
Author affiliations: Los Alamos National Laboratory, Los Alamos, New Mexico, USA (Y.T. Lin, W.S. Hlavacek); Northern Arizona University, Flagstaff, Arizona, USA (J. Neumann, E.F. Miller, R.G. Posner); University of California, Davis, California, USA (A. Mallela); Sandia National Laboratories, Livermore, California, USA (C. Safta, J. Ray); Oak Ridge National Laboratory, Oak Ridge, Tennessee, USA (G. Thakur, S. Chinthavali)

Main Article

Figure 6

Matrix of 1- and 2-dimensional projections of the 7-dimensional posterior samples obtained for the adjustable parameters associated with the compartmental model (n = 0) for daily new case counts of coronavirus disease in the New York City, New York, metropolitan statistical area, United States, January 21–June 21, 2020. Plots of marginal posteriors (1-dimensional projections) are shown on the diagonal from top left to bottom right. Other plots are 2-dimensional projections indicating the correlations between parameter estimates. Brightness indicates higher probability density. A compact bright area indicates absence of or relatively low correlation. An extended, asymmetric bright area indicates relatively high correlation.

Figure 6. Matrix of 1- and 2-dimensional projections of the 7-dimensional posterior samples obtained for the adjustable parameters associated with the compartmental model (n = 0) for daily new case counts of coronavirus disease in the New York City, New York, metropolitan statistical area, United States, January 21–June 21, 2020. Plots of marginal posteriors (1-dimensional projections) are shown on the diagonal from top left to bottom right. Other plots are 2-dimensional projections indicating the correlations between parameter estimates. Brightness indicates higher probability density. A compact bright area indicates absence of or relatively low correlation. An extended, asymmetric bright area indicates relatively high correlation.

Main Article

Page created: January 20, 2021
Page updated: February 21, 2021
Page reviewed: February 21, 2021
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