Daily Forecasting of Regional Epidemics of Coronavirus Disease with Bayesian Uncertainty Quantification, United States
Yen Ting Lin
, 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)
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Figure 8
Figure 8. Predictions of the compartmental model for daily new case counts of coronavirus disease in the Phoenix, Arizona, metropolitan statistical area, United States, January 21–June 18, 2020. A) Model using 1 initial period of social distancing (n = 0). B) Model using an initial period of social distancing and a subsequent period of reduced adherence to social distancing practices (n = 1). C) The marginal posteriors for the social-distancing setpoint parameter p0 inferred in panel A. D) The marginal posteriors for the social-distancing parameters p0 and p1 inferred in panel B.
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