title: Predicting outbreak detection in public health surveillance: quantitative analysis to enable evidence-based method selection.
abstract: Public health surveillance is critical for accurate and timely outbreak detection and effective epidemic control. A wide range of statistical algorithms is used for surveillance, and important differences have been noted in the ability of these algorithms to detect outbreaks. The evidence about the relative performance of these algorithms, however, remains limited and mainly qualitative. Using simulated outbreak data, we developed and validated quantitative models for predicting the ability of commonly used surveillance algorithms to detect different types of outbreaks. The developed models accurately predict the ability of different algorithms to detect different types of outbreaks. These models enable evidence-based algorithm selection and can guide research into algorithm development.
authors.affiliation: McGill University, Montreal, QC, Canada.
authors.forename: David L
authors.initials: DL
authors.lastname: Buckeridge
authors.forename: Anna
authors.initials: A
authors.lastname: Okhmatovskaia
authors.forename: Samson
authors.initials: S
authors.lastname: Tu
authors.forename: Martin
authors.initials: M
authors.lastname: O'Connor
authors.forename: Csongor
authors.initials: C
authors.lastname: Nyulas
authors.forename: Mark A
authors.initials: MA
authors.lastname: Musen
mesh_terms: D000465:Algorithms; D003141:Communicable Diseases / Q000453:epidemiology*; D018563:Disease Notification / Q000379:methods*; D004196:Disease Outbreaks / Q000706:statistics & numerical data*; D019317:Evidence-Based Medicine; D006801:Humans; D011159:Population Surveillance / Q000379:methods*; D016016:Proportional Hazards Models; D018570:Risk Assessment / Q000379:methods; D012307:Risk Factors; D014481:United States