in silico Surveillance: evaluating outbreak detection with simulation models
Abrams, Allyson M
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CitationLewis, Bryan, Stephen Eubank, Allyson M Abrams, and Ken Kleinman. 2013. “in silico Surveillance: evaluating outbreak detection with simulation models.” BMC Medical Informatics and Decision Making 13 (1): 12. doi:10.1186/1472-6947-13-12. http://dx.doi.org/10.1186/1472-6947-13-12.
AbstractBackground: 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 measured by inserting test outbreaks into the surveillance streams and analyzing the likelihood and timeliness of detection. Results: Detection of outbreaks varied from 21% to 95%. Increased coverage did not linearly improve detection probability for all surveillance systems. Relaxing the decision threshold for signaling outbreaks greatly increased false-positives, improved outbreak detection slightly, and led to earlier outbreak detection. Conclusions: Geographical distribution can be more important than coverage level. Detailed simulations of infectious disease transmission can be configured to represent nearly any conceivable scenario. They are a powerful tool for evaluating the performance of surveillance systems and methods used for outbreak detection.
Citable link to this pagehttp://nrs.harvard.edu/urn-3:HUL.InstRepos:11708629
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