A Likelihood Approach for Real-Time Calibration of Stochastic Compartmental Epidemic Models
Fig 4
Median integrated relative errors (mIRE) in estimating model parameters and infection prevalence as well as prediction for simulated epidemics in a population of 10 000 with 50% − 70% attack rate.
A) Epidemic scenarios used to evaluate the performance of calibration methods. Each scenario consists of 50 stochastic trajectories obtained by simulating the model in Fig 3 using the Gillespie algorithm. B) results for estimating R0, C) results for estimating Reff, D) results for estimating the mean duration of infectiousness, E) results for estimating the infection prevalence, F) results for predicting the next week diagnoses, G) results for predicting the diagnoses 3 weeks from now, H) results for predicting the diagnoses over the next 3 weeks, I) results for predicting the attack rate. In each panel: MSS: Multiple Shooting for Stochastic systems (the method proposed here); I.Poi: Independent Poisson (Benchmark Method A); PF: Particle Filter (Benchmark Method B); EnKF: Ensemble Kalman Filter (Benchmark Method C). P-values are from Wilcoxon Signed-Rank test evaluating the hypothesis that the median of relative errors for the MSS approach is smaller than that of I.Poi (first row), PF (second row), and EnKF (third row); p-values smaller than 0.001 are displayed as ***, p-values in between 0.001 and 0.01 as ** and between 0.01 and 0.05 as *. The values of mIRE for some scenarios fall above the vertical axis range and are not displayed.
