Author: Emma Southall; Michael J. Tildesley; Louise Dyson
Title: Prospects for detecting early warning signals in discrete event sequence data: application to epidemiological incidence data Document date: 2020_4_2
ID: dp4qv77q_7
Snippet: O'Dea et al. [4] incorporate an observation model into a Birth-Death-Immigration 75 (BDI) process to present an analytical study of EWS of disease emergence. This model 76 allows prediction of the behaviour of EWS for dynamics captured by a BDI process but 77 is not suitable for diseases with population-level immunity. O'Dea et al. additionally 78 conducted an investigation into reporting errors in incidence-type data by recording the 79 removal .....
Document: O'Dea et al. [4] incorporate an observation model into a Birth-Death-Immigration 75 (BDI) process to present an analytical study of EWS of disease emergence. This model 76 allows prediction of the behaviour of EWS for dynamics captured by a BDI process but 77 is not suitable for diseases with population-level immunity. O'Dea et al. additionally 78 conducted an investigation into reporting errors in incidence-type data by recording the 79 removal of individuals ("death" component in the BDI process). They describe the 80 probability of a case being reported with either a Binomial or Negative Binomial 81 distribution, allowing for over and under-reporting. In contrast to Brett et al., they 82 conclude that the mean, variance and coefficient of variation (CV) are poor indicators 83 since they are sensitive to reporting errors and insensitive to differences between 84 transmission and recovery rates.
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