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_52
Snippet: In Fig. 2(a) and (b) we observe that the analytical prediction fits well with the 374 stochastic simulations of βSI N (purple line) for Model 1 and Model 2 respectively. This 375 demonstrates that this theory approximates the behaviour of the system well. Indeed, 376 we observe that approximating the rate of incidence by smoothing Gillespie simulations 377 of new cases and then calculating the variance of this quantity ("approximated" RoI) 378 p.....
Document: In Fig. 2(a) and (b) we observe that the analytical prediction fits well with the 374 stochastic simulations of βSI N (purple line) for Model 1 and Model 2 respectively. This 375 demonstrates that this theory approximates the behaviour of the system well. Indeed, 376 we observe that approximating the rate of incidence by smoothing Gillespie simulations 377 of new cases and then calculating the variance of this quantity ("approximated" RoI) 378 predicts a similar increasing behaviour before the critical transition, shown in Fig. 2 (a) 379 (blue line). This corresponds to the same peak as the analytical prediction and "true" 380 simulations. However, it fails to capture the magnitude of the behaviour earlier on in 381 the dynamics.
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