Selected article for: "cc ND international license and epidemic incidence"
Author: KV Parag; CA Donnelly
Title: Optimising Renewal Models for Real-Time Epidemic Prediction and Estimation
  • Document date: 2019_11_8
    • ID: 9t5ncsig_28
    Snippet: . CC-BY-NC-ND 4.0 International license is made available under a The copyright holder for this preprint (which was not peer-reviewed) is the author/funder.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/10.1101/835181 doi: bioRxiv preprint Fig. 6 . Between 8 − 10% of incidence data points are better covered by using 277 k * over k = 7. This improvement is apparent in the right graphs of (a) and 278 (b) in Fig. 7 , where the k = 7 case produces stiffer incidence predict.....
    Document: . CC-BY-NC-ND 4.0 International license is made available under a The copyright holder for this preprint (which was not peer-reviewed) is the author/funder.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/10.1101/835181 doi: bioRxiv preprint Fig. 6 . Between 8 − 10% of incidence data points are better covered by using 277 k * over k = 7. This improvement is apparent in the right graphs of (a) and 278 (b) in Fig. 7 , where the k = 7 case produces stiffer incidence predictions that 279 cannot properly reproduce the observed epi-curve. Weekly windows misjudge 280 the SARS epidemic peak, predict a multimodal SARS incidence curve that is 281 not reflected by the actual data and systematically underestimate influenza case 282 counts when it matters most (i.e. around the high incidence phase).

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