Author: KV Parag; CA Donnelly
Title: Optimising Renewal Models for Real-Time Epidemic Prediction and Estimation Document date: 2019_11_8
ID: 9t5ncsig_31
Snippet: We might suspect that certain artefacts of the data could resolve this issue, 295 rendering a more believable combination of estimated reproduction number and 296 predicted incidence. Particularly, the influenza data seems considerably more 297 affected by the smaller look-back windows. These k * = 2 windows are needed 298 to help predictions get close to the peak incidence values of the data. However, 299 these peaks seem reminiscent of outliers.....
Document: We might suspect that certain artefacts of the data could resolve this issue, 295 rendering a more believable combination of estimated reproduction number and 296 predicted incidence. Particularly, the influenza data seems considerably more 297 affected by the smaller look-back windows. These k * = 2 windows are needed 298 to help predictions get close to the peak incidence values of the data. However, 299 these peaks seem reminiscent of outliers and in the original analysis of [17] they 300 were attributed to possible recollection bias in patients that were questioned.
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