Using information theory to optimise epidemic models for real-time prediction and estimation
Fig 1
Optimal window selection using APE.
(A) An observed incidence curve (blue dots) is sequentially and causally predicted over time s ≤ t using effective reproduction number estimates based on two possible windows lengths of k1 and k2 (blue shaded). Predictive distributions are summarised by red error bars (shown only for times t1 and t2, respectively for k1 and k2), (B) The true reproduction number (Rs, dashed black) is estimated under each window length as (blue) and
(grey). Large windows (k1) smooth over fluctuations. Small ones (k2) recover more changes but are noisy. (C) The APE assesses k1 and k2 via the log-loss of their sequential predictions (i.e. from red error bars across time). The window with the smaller APE is better supported by this incidence curve. See Methods for more mathematical details.