Skip to main content
    • Aa
    • Aa

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)
  • DOI:
  • Published online: 23 April 2014

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.

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:
Linked references
Hide All

This list contains references from the content that can be linked to their source. For a full set of references and notes please see the PDF or HTML where available.

1.World Health Organisation. Influenza (seasonal), 2009 ( Accessed April 2013.

2.FS Dawood , 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. DEKarageorgopoulos , Age distribution of cases of 2009 (H1N1) pandemic influenza in comparison with seasonal influenza. PLoS ONE 2011; 6: e21690.

4. HSIzurieta , Influenza and the rates of hospitalization for respiratory disease among infants and young children. New England Journal of Medicine 2000; 342: 232239.

5. SBalasegaram , Patterns of early transmission of pandemic influenza in London – link with deprivation. Influenza and Other Respiratory Viruses 2012; 6: e35e41.

6. JHStark , Local spatial and temporal processes of influenza in Pennsylvania, USA: 2003–2009. PLoS ONE 2012; 7: e34245.

10. HJClothier , A comparison of data sources for the surveillance of seasonal and pandemic influenza in Victoria. Communicable Diseases Intelligence 2006; 30: 345.

11.H Clothier , 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.JR Ortiz , Monitoring influenza activity in the United States: a comparison of traditional surveillance systems with Google flu trends. PLoS ONE 2011; 6: e18687.

13.Wilson J Gaines , Utilizing spatiotemporal analysis of influenza-like illness and rapid tests to focus swine-origin influenza virus intervention. Health & Place 2010; 16: 12301239.

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.

22.EL Tay , Exploring a proposed WHO method to determine thresholds for seasonal influenza surveillance. PLoS ONE 2013; 8: e77244.

23.SG Sullivan , EL Tay , H Kelly . Variable definitions of the influenza season and their impact on vaccine effectiveness estimates. Vaccine 2013; 31: 42804283.

24.AM Presanis , The severity of pandemic H1N1 influenza in the United States, from April to July 2009: a Bayesian analysis. PLoS Medicine 2009; 6: e1000207.

25.A Presanis , Changes in severity of 2009 pandemic A/H1N1 influenza in England: a Bayesian evidence synthesis. British Medical Journal 2011; 343: d5408.

30. National Health Performance Authority. My healthy communities, 2013 (

Recommend this journal

Email your librarian or administrator to recommend adding this journal to your organisation's collection.

Epidemiology & Infection
  • ISSN: 0950-2688
  • EISSN: 1469-4409
  • URL: /core/journals/epidemiology-and-infection
Please enter your name
Please enter a valid email address
Who would you like to send this to? *


Type Description Title
Supplementary Materials

Thomas Supplementary Material
Supplementary Material

 Unknown (1.4 MB)
1.4 MB