Objective: Using simulated outbreaks to choose the optimal model and its related parameters on measles so as to provide technical support for developing an Auto Warning System (AWS).
Methods: AEGIS-Cluster Creation Tool was applied to simulate a range of unique outbreak signals. Then these simulations were added to the actual daily counts of measles from the National Disease Surveillance System, between 2005 and 2007. Exponential weighted moving average (EWMA), C1-MILD (C1), C2-MEDIUM (C2), C3-ULTRA (C3) and space-time permutation scan statistic model were comprehensively applied to detect these simulations. Tools for evaluation as Youden' s index and detection time were calculated to optimize parameters before an optimal model was finally chosen.
Results: EWMA (lamda = 0.6, k = 1.0), CI (k = 0.1, H=3sigma), C2 (k = 0.1, H=3sigma), C3 (k = 1.0, H=4sigma) and space-time permutation scan statistic (maximum temporal cluster size=7 d, maximum spatial cluster size = 5 km) appeared to be the optimal parameters among these models. Youden's index of EWMA was 90.8% and detection time being 0.121 d. Youden's index of C1 was 88.7% and detection time being 0.142 d. Youden's index of C2 was 92.9% and detection time being 0.121 d. Youden's index of C3 was 87.9% and detection time being 0.058 d. Youden's index of space-time permutation scan statistic was 94.3% and detection time being 0.176 d.
Conclusion: Among these five early warning detection models, space-time permutation scan statistic model had the highest efficacy.