In silico surveillance: evaluating outbreak detection with simulation models.

ABSTRACT:

Background

Detecting outbreaks is a crucial task for public health officials, yet gaps remain in the systematic evaluation of outbreak detection protocols. The authors' objectives were to design, implement, and test a flexible methodology for generating detailed synthetic surveillance data that provides realistic geographical and temporal clustering of cases and use to evaluate outbreak detection protocols.

Methods

A detailed representation of the Boston area was constructed, based on data about individuals, locations, and activity patterns. Influenza-like illness (ILI) transmission was simulated, producing 100 years of in silico ILI data. Six different surveillance systems were designed and developed using gathered cases from the simulated disease data. Performance was meas

SUBMITTER: Lewis B 

PROVIDER: S-EPMC3691709 | biostudies-literature | 2013 Jan

REPOSITORIES: biostudies-literature