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Table 1.

Transition probabilities in prevalence theory for all models.

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Table 2.

Model notation and parameter values shared among all models.

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Table 2 Expand

Fig 1.

Comparing predictions to simulations for variance.

For each model (SIS with social distancing; SIS with vaccination; SIS emergence) we calculate the variance between 500 homogeneous realisations at every time step (daily). Each figure shows: Poisson process distribution (green line); dynamic predictions (red line) and Gillespie simulations (blue line). The bottom left panel also shows the dynamical prediction from O’Dea et al. which was derived for this specific system (lilac line).

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Fig 2.

Variance calculated on the rate of incidence.

For each model (SIS with social distancing (panel a); SIS with vaccination (panel b); SIS emergence(panel c)) we calculate the variance on the rate of incidence, RoI between 500 homogeneous realisations at every time step (daily). Each figure shows: dynamic solution (orange line); rolling RoI calculated from new cases nc (blue line) and true RoI calculated from the production of prevalence, susceptible and effective contact rate (, purple line).

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Fig 2 Expand

Fig 3.

AUC scores for different EWS.

We compare the performance of 5 common statistical indicators for SIS with social distancing (a) for disease elimination and SIS emergence (b). The Rate of Incidence is taken to be the “rolling” RoI.

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