Author: Aronis, John M.; Ferraro, Jeffrey P.; Gesteland, Per H.; Tsui, Fuchiang; Ye, Ye; Wagner, Michael M.; Cooper, Gregory F.
Title: A Bayesian approach for detecting a disease that is not being modeled Document date: 2020_2_28
ID: 0xbozygd_11
Snippet: The dataset includes patient records from one outbreak year (from June 1 of one year through May 31 of the next year), and parameters including the length of a wait period w, and the length of a monitor window m. For each current day c, the baseline window includes data from days 1 through c − (w + m) and the monitor window includes data from days c − m through c. For the experiments reported in this paper we set w = 14, m = 28, and start loo.....
Document: The dataset includes patient records from one outbreak year (from June 1 of one year through May 31 of the next year), and parameters including the length of a wait period w, and the length of a monitor window m. For each current day c, the baseline window includes data from days 1 through c − (w + m) and the monitor window includes data from days c − m through c. For the experiments reported in this paper we set w = 14, m = 28, and start looking for outbreaks on September 1 (day c = 93). Starting with c = 93 ensures that the baseline window includes data from at least 50 days which is sufficient to characterize baseline ILI. See Fig 1.
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