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 10

Selection for SARS (2003).

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 SARS (2003) data [10] while (B) smooths outliers (with a 5-day moving average) in the data as in [16].

doi: https://doi.org/10.1371/journal.pcbi.1007990.g010