Research
Reporting errors in infectious disease outbreaks, with an application to Pandemic Influenza A/H1N1
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* Corresponding author: Laura F White lfwhite@bu.edu
1 Department of Biostatistics, Boston University School of Public Health, 801 Massachusetts Ave, 3rd Floor, Boston MA 02118 USA
2 Harvard School of Public Health, 655 Huntington Ave, Boston MA 02115 USA
Epidemiologic Perspectives & Innovations 2010, 7:12 doi:10.1186/1742-5573-7-12
Published: 15 December 2010Abstract
Background
Effectively responding to infectious disease outbreaks requires a well-informed response. Quantitative methods for analyzing outbreak data and estimating key parameters to characterize the spread of the outbreak, including the reproductive number and the serial interval, often assume that the data collected is complete. In reality reporting delays, undetected cases or lack of sensitive and specific tests to diagnose disease lead to reporting errors in the case counts. Here we provide insight on the impact that such reporting errors might have on the estimation of these key parameters.
Results
We show that when the proportion of cases reported is changing through the study period, the estimates of key epidemiological parameters are biased. Using data from the Influenza A/H1N1 outbreak in La Gloria, Mexico, we provide estimates of these parameters, accounting for possible reporting errors, and show that they can be biased by as much as 33%, if reporting issues are not accounted for.
Conclusions
Failure to account for missing data can lead to misleading and inaccurate estimates of epidemic parameters.