Using information theory to optimise epidemic models for real-time prediction and estimation

doi:10.1371/journal.pcbi.1007990

Using information theory to optimise epidemic models for real-time prediction and estimation

Fig 9

Selection for pandemic influenza (1918).

Left graphs compare estimates (blue with 95% confidence intervals) at optimal APE window length k* to weekly sliding windows (k = 7), which were recommended in [10]. Right graphs give corresponding one-step-ahead predictions (blue with 95% prediction intervals). Dashed lines are the R = 1 threshold (left) and dots are the true incidence I_s (right). Panel (A) directly uses the empirical influenza (1918) data [10] while (B) smooths outliers in the data as in [16].

doi: https://doi.org/10.1371/journal.pcbi.1007990.g009