Author: Monique R. Ambrose; Adam J. Kucharski; Pierre Formenty; Jean-Jacques Muyembe-Tamfum; Anne W. Rimoin; James O. Lloyd-Smith
Title: Quantifying transmission of emerging zoonoses: Using mathematical models to maximize the value of surveillance data Document date: 2019_6_19
ID: f14u2sz5_59
Snippet: However, because we only observe localities with one or more cases in the surveillance data, we 1208 need that conditioning to be reflected in the likelihood. In other words, we now want to express 1209 the likelihood of a particular time-series of cases in a locality conditional on that locality having 1210 . CC-BY 4.0 International license is made available under a The copyright holder for this preprint (which was not peer-reviewed) is the auth.....
Document: However, because we only observe localities with one or more cases in the surveillance data, we 1208 need that conditioning to be reflected in the likelihood. In other words, we now want to express 1209 the likelihood of a particular time-series of cases in a locality conditional on that locality having 1210 . CC-BY 4.0 International license is made available under a The copyright holder for this preprint (which was not peer-reviewed) is the author/funder. It . https://doi.org/10.1101/677021 doi: bioRxiv preprint one or more cases. This can be done for each locality by multiplying its component of the 1211 likelihood by the inverse of the probability (q) of having one or more cases: 1212
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