Author: Sarah F. McGough; Michael A. Johansson; Marc Lipsitch; Nicolas A. Menzies
Title: Nowcasting by Bayesian Smoothing: A flexible, generalizable model for real-time epidemic tracking Document date: 2019_6_7
ID: 6kq0ptlg_57
Snippet: We used as a benchmark for comparison the "nowcast" function of the R package "surveillance" by Höhle and colleagues (described in ref. (9)) designed to produce Bayesian nowcasts for epidemics using a hierarchical model for nt, d ≤ T-t | nt,d , or the observed cases conditional on the expected total number of cases. We applied the function assuming a time-homogenous delay distribution and recommended parameterization described by the authors i.....
Document: We used as a benchmark for comparison the "nowcast" function of the R package "surveillance" by Höhle and colleagues (described in ref. (9)) designed to produce Bayesian nowcasts for epidemics using a hierarchical model for nt, d ≤ T-t | nt,d , or the observed cases conditional on the expected total number of cases. We applied the function assuming a time-homogenous delay distribution and recommended parameterization described by the authors in http://staff.math.su.se/hoehle/blog/2016/07/19/nowCast.html, and for comparability, used the same moving window sizes (27 and 104 weeks) to produce nowcasts over the same time periods.
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