Fig 3. Delays in detecting upward and downward changes in R.

We characterise the discrepancies between detecting resurgence and control against the steepness or rate, θ, of changes in transmissibility (R_s), which we model using logistic functions (panel A, steepness increases from blue to red). We compare differences in the probability of detecting resurgence (P(R_s>1)) or control (P(R_s≤1) under filtered and smoothed estimates (see main text) first crossing thresholds of 0.5 (Δt₅₀) and 0.95 (Δt₉₅) for five infectious diseases (panel B plots their assumed generation time distributions from [2,46,47]). We simulate 1000 epidemics from each disease using renewal models and estimate R_s with EpiFilter [15]. Panels C and D (here colours match panel B, Δt is normalised by the mean generation times of the diseases) show that delays in detecting resurgence (dots with colours indicating the disease) are at least 5–10 times longer than for detecting control (diamonds with equivalent colours). Our ability to infer even symmetrical transmissibility changes is fundamentally asymmetric, largely due to the differences in case incidence at which those changes usually tend to occur.