Selected article for: "critical transition and incidence rate"
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_60
    Snippet: The AUC score gives a predictive measure between different indicators, which we use 439 to assess their performances. The closer the AUC is to 0.5 signifies the worst the 440 statistic's performance at anticipating a critical transition. This is analogous with 441 randomly selecting simulations that are the null and disease elimination (Model 1, Fig. 442 3(a)) or disease emergence (Model 3, Fig. 3(b) ). In particular, skewness is a poor 443 indic.....
    Document: The AUC score gives a predictive measure between different indicators, which we use 439 to assess their performances. The closer the AUC is to 0.5 signifies the worst the 440 statistic's performance at anticipating a critical transition. This is analogous with 441 randomly selecting simulations that are the null and disease elimination (Model 1, Fig. 442 3(a)) or disease emergence (Model 3, Fig. 3(b) ). In particular, skewness is a poor 443 indicator because of its inability to identify disease elimination with any type of disease 444 data it is applied to (rate of incidence, incidence and prevalence). Identifying emergence 445 with skewness in prevalence or RoI data (red and orange bars respectively) is also very 446 poor and its predictive ability is only slightly increased with incidence (green bars).

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