Reporting errors in infectious disease outbreaks, with an application to Pandemic Influenza A/H1N1

Laura F White,Marcello Pagano

doi:10.1186/1742-5573-7-12

Laura F White, Marcello Pagano

Open Access

https://doi.org/10.1186/1742-5573-7-12

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Abstract

BackgroundEffectively 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.ResultsWe 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.ConclusionsFailure to account for missing data can lead to misleading and inaccurate estimates of epidemic parameters.

Highlights

Responding to infectious disease outbreaks requires a well-informed response
The recent outbreak of pandemic strain Influenza A H1N1, as well as other infectious disease outbreaks that have taken place recently illustrate the need for a rapid public health response and the ability to collect and analyze data efficiently
Allowing the reporting fraction to vary according to a logistic function yields estimates of the reproductive number and the serial interval that are consistently less than the estimates from the original data

Summary

Introduction

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. Unnecessary panic and disruption to society is more likely to be avoided and appropriately measured public health responses are more likely to take place when we have accurate information on the virulence and pathogenicity of an emerging disease. For these reasons, quantitative methods have been developed and continue to be developed to facilitate the assimilation of emerging data. Other quantities of interest include the case fatality rate [10] and the attack rate in subpopulations, such as age groups

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