Reporting Errors in Infectious Disease Outbreaks, with an Application to Pandemic Influenza A/H1N1

Overview

Journal Epidemiol Perspect Innov

Publisher Biomed Central

Specialty Public Health

Date 2010 Dec 17

PMID 21159178

Citations 20

Authors

Laura F White

Marcello Pagano

Affiliations

Soon will be listed here.

Abstract

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.

Citing Articles

Generative Bayesian modeling to nowcast the effective reproduction number from line list data with missing symptom onset dates.

Lison A, Abbott S, Huisman J, Stadler T PLoS Comput Biol. 2024; 20(4):e1012021.

PMID: 38626217 PMC: 11051644. DOI: 10.1371/journal.pcbi.1012021.

Comparing the transmission potential from sequence and surveillance data of 2009 North American influenza pandemic waves.

Duvvuri V, Hicks J, Damodaran L, Grunnill M, Braukmann T, Wu J Infect Dis Model. 2023; 8(1):240-252.

PMID: 36844759 PMC: 9944206. DOI: 10.1016/j.idm.2023.02.003.

Integration of publicly available case-based data for real-time coronavirus disease 2019 risk assessment, Japan.

Ninomiya K, Kanamori M, Ikeda N, Jindai K, Ko Y, Otani K Western Pac Surveill Response J. 2022; 13(1):1-6.

PMID: 35494411 PMC: 9016265. DOI: 10.5365/wpsar.2022.13.1.889.

Near real-time surveillance of the SARS-CoV-2 epidemic with incomplete data.

De Salazar P, Lu F, Hay J, Gomez-Barroso D, Fernandez-Navarro P, Martinez E PLoS Comput Biol. 2022; 18(3):e1009964.

PMID: 35358171 PMC: 9004750. DOI: 10.1371/journal.pcbi.1009964.

Investigating healthcare practitioners' attitudes towards the COVID-19 outbreak in Saudi Arabia: A general qualitative framework for managing the pandemic.

Alanezi F, Aljahdali A, Alyousef S, Alshaikh W, Mushcab H, AlThani B Inform Med Unlocked. 2020; 22:100491.

PMID: 33319030 PMC: 7722503. DOI: 10.1016/j.imu.2020.100491.

References

Becker N, Wang D, Clements M . Type and quantity of data needed for an early estimate of transmissibility when an infectious disease emerges. Euro Surveill. 2010; 15(26). View

Fraser C, Donnelly C, Cauchemez S, Hanage W, Van Kerkhove M, Hollingsworth T . Pandemic potential of a strain of influenza A (H1N1): early findings. Science. 2009; 324(5934):1557-61. PMC: 3735127. DOI: 10.1126/science.1176062. View

White L, Wallinga J, Finelli L, Reed C, Riley S, Lipsitch M . Estimation of the reproductive number and the serial interval in early phase of the 2009 influenza A/H1N1 pandemic in the USA. Influenza Other Respir Viruses. 2009; 3(6):267-76. PMC: 2782458. DOI: 10.1111/j.1750-2659.2009.00106.x. View

White L, Pagano M . A likelihood-based method for real-time estimation of the serial interval and reproductive number of an epidemic. Stat Med. 2007; 27(16):2999-3016. PMC: 3951165. DOI: 10.1002/sim.3136. View

Kenah E, Lipsitch M, Robins J . Generation interval contraction and epidemic data analysis. Math Biosci. 2008; 213(1):71-9. PMC: 2365921. DOI: 10.1016/j.mbs.2008.02.007. View

Wallinga J, Lipsitch M . How generation intervals shape the relationship between growth rates and reproductive numbers. Proc Biol Sci. 2007; 274(1609):599-604. PMC: 1766383. DOI: 10.1098/rspb.2006.3754. View

Gerberding J, Hughes J, Koplan J . Bioterrorism preparedness and response: clinicians and public health agencies as essential partners. JAMA. 2002; 287(7):898-900. DOI: 10.1001/jama.287.7.898. View

. Influenza A(H1N1)v virus infections in Belgium, May-June 2009. Euro Surveill. 2009; 14(28). DOI: 10.2807/ese.14.28.19270-en. View

Wallinga J, Teunis P . Different epidemic curves for severe acute respiratory syndrome reveal similar impacts of control measures. Am J Epidemiol. 2004; 160(6):509-16. PMC: 7110200. DOI: 10.1093/aje/kwh255. View

10.

Fraser C, Riley S, Anderson R, Ferguson N . Factors that make an infectious disease outbreak controllable. Proc Natl Acad Sci U S A. 2004; 101(16):6146-51. PMC: 395937. DOI: 10.1073/pnas.0307506101. View

11.

White L, Pagano M . Transmissibility of the influenza virus in the 1918 pandemic. PLoS One. 2008; 3(1):e1498. PMC: 2204055. DOI: 10.1371/journal.pone.0001498. View

12.

. Update: novel influenza A (H1N1) virus infection - Mexico, March-May, 2009. MMWR Morb Mortal Wkly Rep. 2009; 58(21):585-9. PMC: 5856103. View

13.

Pyhala R, Aho K, Visakorpi R . Seroepidemiology of H1N1 influenza: striking differences in the attack rate among young people. Acta Pathol Microbiol Scand B. 1979; 87B(3):161-4. DOI: 10.1111/j.1699-0463.1979.tb02420.x. View

14.

Garske T, Legrand J, Donnelly C, Ward H, Cauchemez S, Fraser C . Assessing the severity of the novel influenza A/H1N1 pandemic. BMJ. 2009; 339:b2840. DOI: 10.1136/bmj.b2840. View

15.

Becker N . Estimation for discrete time branching processes with application to epidemics. Biometrics. 1977; 33(3):515-22. View

16.

Svensson A . A note on generation times in epidemic models. Math Biosci. 2006; 208(1):300-11. DOI: 10.1016/j.mbs.2006.10.010. View

17.

Eubank S . Network based models of infectious disease spread. Jpn J Infect Dis. 2005; 58(6):S9-13. View