Fig 2. Resurgence and control dynamics of Ebola virus.

Using renewal models with the generation time from [46], we simulate 1000 realisations of Ebola virus epidemics (t = 300) with step (A panels) and seasonally (B panels) changing transmissibility (true R_s in black). Top panels show posterior mean estimates from the filtered (E_p[R_s], blue) and smoothed (E_q[R_s], red) distributions from every realisation (computed using EpiFilter [15]). Middle panels average the Kullback-Liebler divergences from those simulations, D(p_s|q_s), and bottom panels display the overall filtered ($\mathrm{P}\left(R_s>1\right|I_1^s)$, blue), and smoothed ($\mathrm{P}\left(R_s>1\right|I_1^t)$, red) probabilities of resurgence. We find fundamental and striking delays in detecting resurgence, often an order of magnitude longer than those for detecting control or suppression in transmission (see lags between red and blue curves in all relevant panels). Note that the initial rise in $\mathrm{P}\left(R_s>1\right|I_1^s)$ of panel A, which precedes the transition in R_s, is due to the influence of the prior distribution (which has a mean above 1) in a period with very few cases. We present the incidence curves that underlie the simulations here in Fig C in the S1 Appendix.