Impact of Seasonal Patterns on Outbreak Detection in Syndromic Surveillance
Author Information
Author(s): Burr Tom, Graves Todd, Klamann Richard, Michalak Sarah, Picard Richard, Hengartner Nicolas
Primary Institution: Los Alamos National Laboratory
Hypothesis
The study aims to evaluate the impact of inconsistent seasonal effects on performance assessments in detecting anomalous counts in syndromic surveillance data.
Conclusion
The assumption that 'one season fits all' is violated, leading to optimistic performance claims for some forecasting methods.
Supporting Evidence
- The study found that moving average methods performed well on real data.
- Detection probabilities were generally lower for real data compared to data simulated from the nonhierarchical model.
- The hierarchical model provided more realistic estimates of outbreak detection probabilities.
Takeaway
The study shows that different years can have different patterns in disease outbreaks, and using a one-size-fits-all approach can lead to mistakes in detecting these outbreaks.
Methodology
Synthetic outbreaks were injected into real and simulated data to evaluate detection probabilities using various forecasting methods.
Potential Biases
The nonhierarchical model may lead to optimistic performance assessments compared to real data.
Limitations
The study is based on one data set and one type of simulated outbreak, which may not generalize to other contexts.
Participant Demographics
Data collected from the Emergency Center of the University Hospital, Albuquerque NM.
Statistical Information
Confidence Interval
± 2
Digital Object Identifier (DOI)
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