Skip to main content Accessibility help
×
Home

Quantifying differences in the epidemic curves from three influenza surveillance systems: a nonlinear regression analysis

  • E. G. THOMAS (a1), J. M. McCAW (a1) (a2), H. A. KELLY (a3) (a4), K. A. GRANT (a3) and J. McVERNON (a1) (a2)...

Summary

Influenza surveillance enables systematic collection of data on spatially and demographically heterogeneous epidemics. Different data collection mechanisms record different aspects of the underlying epidemic with varying bias and noise. We aimed to characterize key differences in weekly incidence data from three influenza surveillance systems in Melbourne, Australia, from 2009 to 2012: laboratory-confirmed influenza notified to the Victorian Department of Health, influenza-like illness (ILI) reported through the Victorian General Practice Sentinel Surveillance scheme, and ILI cases presenting to the Melbourne Medical Deputising Service. Using nonlinear regression, we found that after adjusting for the effects of geographical region and age group, characteristics of the epidemic curve (including season length, timing of peak incidence and constant baseline activity) varied across the systems. We conclude that unmeasured factors endogenous to each surveillance system cause differences in the disease patterns recorded. Future research, particularly data synthesis studies, could benefit from accounting for these differences.

  • View HTML
    • Send article to Kindle

      To send this article to your Kindle, first ensure no-reply@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about sending to your Kindle. Find out more about sending to your Kindle.

      Note you can select to send to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be sent to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

      Find out more about the Kindle Personal Document Service.

      Quantifying differences in the epidemic curves from three influenza surveillance systems: a nonlinear regression analysis
      Available formats
      ×

      Send article to Dropbox

      To send this article to your Dropbox account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your <service> account. Find out more about sending content to Dropbox.

      Quantifying differences in the epidemic curves from three influenza surveillance systems: a nonlinear regression analysis
      Available formats
      ×

      Send article to Google Drive

      To send this article to your Google Drive account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your <service> account. Find out more about sending content to Google Drive.

      Quantifying differences in the epidemic curves from three influenza surveillance systems: a nonlinear regression analysis
      Available formats
      ×

Copyright

Corresponding author

* Author for correspondence: Miss E. G. Thomas, Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Victoria, Australia. (Email: emma.thomas@unimelb.edu.au)

References

Hide All
1. World Health Organisation. Influenza (seasonal), 2009 (http://www.who.int/mediacentre/factsheets/fs211/en/). Accessed April 2013.
2. Dawood, FS, et al. Estimated global mortality associated with the first 12 months of 2009 pandemic influenza A H1N1 virus circulation: a modelling study. Lancet Infectious Diseases 2012; 12: 687695.
3. Karageorgopoulos, DE, et al. Age distribution of cases of 2009 (H1N1) pandemic influenza in comparison with seasonal influenza. PLoS ONE 2011; 6: e21690.
4. Izurieta, HS, et al. Influenza and the rates of hospitalization for respiratory disease among infants and young children. New England Journal of Medicine 2000; 342: 232239.
5. Balasegaram, S, et al. Patterns of early transmission of pandemic influenza in London – link with deprivation. Influenza and Other Respiratory Viruses 2012; 6: e35e41.
6. Stark, JH, et al. Local spatial and temporal processes of influenza in Pennsylvania, USA: 2003–2009. PLoS ONE 2012; 7: e34245.
7. Grant, KA, et al. Continued dominance of pandemic A (H1N1) 2009 influenza in Victoria, Australia in 2010. Western Pacific Surveillance and Response Journal 2011; 2: 1018.
8. World Health Organisation. WHO global technical consultation: global standards and tools for influenza surveillance. Geneva, Switzerland: World Health Organisation, 2011. WHO reference number WHO/HSE/GIP/2011.1.
9. Uphoff, H, Cohen, J. Some aspects regarding the interpretation of influenza surveillance data. Medical Microbiology and Immunology 2002; 191: 145149.
10. Clothier, HJ, et al. A comparison of data sources for the surveillance of seasonal and pandemic influenza in Victoria. Communicable Diseases Intelligence 2006; 30: 345.
11. Clothier, H, et al. Geographic representativeness for sentinel infuenza surveillance: implications for routine surveillance and pandemic preparedness. Australian and New Zealand Journal of Public Health 2006; 30: 337341.
12. Ortiz, JR, et al. Monitoring influenza activity in the United States: a comparison of traditional surveillance systems with Google flu trends. PLoS ONE 2011; 6: e18687.
13. Gaines, Wilson J, et al. Utilizing spatiotemporal analysis of influenza-like illness and rapid tests to focus swine-origin influenza virus intervention. Health & Place 2010; 16: 12301239.
14. Australian Bureau of Statistics. Australian statistical geography standard (ASGS): volume 1 – Main structure and greater capital city statistical areas. Canberra, Australia: Australian Bureau of Statistics, 2010; ABS publication number 1270.0.55.001.
15. Lester, R, Moran, R. Pandemic H1N1 2009 influenza (human swine flu) – the Victorian Government's response. Victorian Infectious Diseases Bulletin 2009; 12: 4345.
16. Australian Government Department of Health and Ageing. Review of Australia's health sector response to pandemic (H1N1) 2009: lessons identified. Canberra, Australia: Australian Government Department of Health and Ageing, 2011.
17. Grant, KA, et al. Higher proportion of older influenza A(H1N1)pdm09 cases in Victoria, 2011. Victorian Infectious Diseases Bulletin 2012; 15: 4955.
18. Melbourne Medical Deputising Service. MMDS – Melbourne Medical Deputising Service, 2013. http://www.mmds.com.au/service-area.
19. Fielding, J, et al. Pandemic H1N1 influenza surveillance in Victoria, Australia, April–September, 2009. Eurosurveillance 2009; 14: 19368.
20. Kelly, HA, et al. The significance of increased influenza notifications during spring and summer of 2010–11 in Australia. Influenza and Other Respiratory Viruses 2012; 7: 11351141.
21. World Health Organisation. WHO interim global epidemiological surveillance standards for influenza. Geneva, Switzerland: World Health Organisation, 2012.
22. Tay, EL, et al. Exploring a proposed WHO method to determine thresholds for seasonal influenza surveillance. PLoS ONE 2013; 8: e77244.
23. Sullivan, SG, Tay, EL, Kelly, H. Variable definitions of the influenza season and their impact on vaccine effectiveness estimates. Vaccine 2013; 31: 42804283.
24. Presanis, AM, et al. The severity of pandemic H1N1 influenza in the United States, from April to July 2009: a Bayesian analysis. PLoS Medicine 2009; 6: e1000207.
25. Presanis, A, et al. Changes in severity of 2009 pandemic A/H1N1 influenza in England: a Bayesian evidence synthesis. British Medical Journal 2011; 343: d5408.
26. Lambert, SB, et al. Influenza surveillance in Australia: we need to do more than count. Medical Journal of Australia 2010; 193: 4345.
27. Turner, J, Kelly, H. A medical locum service as a site for sentinel influenza surveillance. Eurosurveillance 2005; 10: 96.
28 Australian Bureau of Statistics. Regional population growth, Australia, 2012. Canberra, Australia: Australian Bureau of Statistics, 2013. ABS publication number 3218.0.
29. Australian Bureau of Statistics. Census of population and housing: Socio-economic indexes for areas (SEIFA), Australia, 2011. Canberra, Australia: Australian Bureau of Statistics, 2013. ABS publication number 2033.0.55.001.
30. National Health Performance Authority. My healthy communities, 2013 (www.myhealthycommunities.gov.au/).

Keywords

Related content

Powered by UNSILO
Type Description Title
PDF
Supplementary materials

Thomas Supplementary Material
Supplementary Material

 PDF (1.4 MB)
1.4 MB

Quantifying differences in the epidemic curves from three influenza surveillance systems: a nonlinear regression analysis

  • E. G. THOMAS (a1), J. M. McCAW (a1) (a2), H. A. KELLY (a3) (a4), K. A. GRANT (a3) and J. McVERNON (a1) (a2)...

Metrics

Full text views

Total number of HTML views: 0
Total number of PDF views: 0 *
Loading metrics...

Abstract views

Total abstract views: 0 *
Loading metrics...

* Views captured on Cambridge Core between <date>. This data will be updated every 24 hours.

Usage data cannot currently be displayed.