Author: KV Parag; CA Donnelly
Title: Optimising Renewal Models for Real-Time Epidemic Prediction and Estimation Document date: 2019_11_8
ID: 9t5ncsig_19
Snippet: λ τ (s) +Λs+1 . These relations explicate how the current 165 estimate of R τ (s) influences our ability to predict upcoming incidence points. 166 Setting s = t in all the above expressions will give the prediction statistics for 167 the next (unobserved) time-point beyond the present. 168 Importantly, Eq. (4) controls the APE metric through the shape of its dis-169 tribution. We explicitly compute this to derive Eq. (5), with I s+1 as the (t.....
Document: λ τ (s) +Λs+1 . These relations explicate how the current 165 estimate of R τ (s) influences our ability to predict upcoming incidence points. 166 Setting s = t in all the above expressions will give the prediction statistics for 167 the next (unobserved) time-point beyond the present. 168 Importantly, Eq. (4) controls the APE metric through the shape of its dis-169 tribution. We explicitly compute this to derive Eq. (5), with I s+1 as the (true) 170 observed incidence at time s + 1, which is evaluated within the context of the 171 predictive space of x, and B s+1 := log Is+1+α τ (s) −1 Is+1 as a binomial term.
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