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_49
Snippet: We place weakly informative priors on representing a small number of hypothetical total cases (n=10) distributed across delay bins, loosely representing the probability of reporting delays for each delay d observed in the first two years of data for dengue and the first 6 months of data for ILI (training periods). As a sensitivity, we also placed weak priors on treating all delays with equal probability, but there was no material difference in th.....
Document: We place weakly informative priors on representing a small number of hypothetical total cases (n=10) distributed across delay bins, loosely representing the probability of reporting delays for each delay d observed in the first two years of data for dengue and the first 6 months of data for ILI (training periods). As a sensitivity, we also placed weak priors on treating all delays with equal probability, but there was no material difference in the results (Table S6) .
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