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 2

Selection for stable and fluctuating epidemics.

Left graphs compare estimates (blue with 95% confidence intervals) at the APE window length k* to those when k is set to its upper and lower limits. Right graphs give corresponding one-step-ahead predictions given the window of data I_τ(s−1) (blue with 95% prediction intervals). Dashed lines are the true R_s numbers (left) and dots are the true I_s counts (right). The panels examine (A) stable (constant) and (B) periodically varying changes in R_s.

doi: https://doi.org/10.1371/journal.pcbi.1007990.g002