Selected article for: "disease elimination and prevalence incidence"
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_48
    Snippet: As expected by our Poisson process analysis, the variance of this system should be 343 the same as the mean of the system. Therefore for disease elimination models, we 344 should expect a decreasing variance (along with a decreasing mean) when calculated on 345 incidence data, in contrast to an increasing variance with prevalence data. Likewise 346 with disease emergence models we expect an increasing variance to correspond to the 347 increasing .....
    Document: As expected by our Poisson process analysis, the variance of this system should be 343 the same as the mean of the system. Therefore for disease elimination models, we 344 should expect a decreasing variance (along with a decreasing mean) when calculated on 345 incidence data, in contrast to an increasing variance with prevalence data. Likewise 346 with disease emergence models we expect an increasing variance to correspond to the 347 increasing mean. This demonstrates that our analysis of incidence has successfully 348 predicted the time-varying variance for these different systems.

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