First Author, Year | Gabriel Bédubourg, 2017 | Michael L Jackson, 2007 | Angela Noufaily, 2019 |
---|---|---|---|
Characteristics | |||
Simulated/ Real Dataset | Simulated | Real & Simulated | Simulated |
Type of Surveillance System | Weekly Health Surveillance | Daily Syndromic Surveillance | Daily Syndromic Surveillance |
Measures to Assess the Performance of the Algorithms | FPRa, PODb, POD1weekc, Sensitivity, Specificity, PPVd, NPVe, F1 | Sensitivity, Specificity, PPV, Timeliness | POD, Sensitivity, Specificity, PPV, Timeliness |
Number of Algorithms Included | 21 | 6 | 3 |
List of Algorithms Included | Farrington Flexible, Original Farrington, CDC (historical limits), CUSUM, CUSUM Rossi, CUSUM GLM, CUSUM, GLM Rossi, Bayes 1, Bayes 2, Bayes 3, RKI 1, RKI 2, RKI 3, GLR Negative Binomial, GLR Poisson, EARS C1, EARS C2, EARSÂ C3 OutbreakP, periodic Poisson regression, periodic negative binomial regression | Three control chart-based algorithms commonly referred to as C1, C2, and C3, GLM, EWMA9, EWMA4 | RAMMIE, EARS, Farrington Flexible |
Result of Performance Measures | Farrington Flexiblef: (1.0%, 99.0%, 43.3%, 34.0%, 20.5%, 95.0%, 58.3%, 0.30) Original Farrington: (2.3%, 97.7%, 56.9%, 45.5%, 29.0%, 95.4%, 45.0%, 0.35) Periodic Poisson GLM: (3.3%, 96.8%, 67.8%, 56.6%, 35.6%, 95.8%, 42.3%, 0.39) GLR Poisson: (15.15%, 84.5%, 75.5%, 60.3%, 45.9%, 95.9%, 16.4%, 0.24) | GLM model was more sensitive than the other algorithms and detected 54% (95% CI = 52%–56%) of the simulated epidemics when run at an alert rate of 0.01 | Amongst the algorithm variants that have a high specificity (i.e. > 90%), Farrington Flexible has the highest sensitivity and specificity, but it is not most timely |