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_7
Snippet: Because 281 the total number of localities assumed to be under surveillance has a substantial impact on 282 parameter estimates, we developed a modified version of the likelihood function that accounts 283 for localities that were under surveillance but never observed in the dataset. This approach 284 calculates the likelihood of the observed dataset conditional on the fact that only localities with 285 one or more cases are included (details on .....
Document: Because 281 the total number of localities assumed to be under surveillance has a substantial impact on 282 parameter estimates, we developed a modified version of the likelihood function that accounts 283 for localities that were under surveillance but never observed in the dataset. This approach 284 calculates the likelihood of the observed dataset conditional on the fact that only localities with 285 one or more cases are included (details on the modified likelihood function can be found in 286
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