Skip to main content
SpringerLink
Log in

Rapid mapping of the spatial and temporal intensity of influenza

  • Original Article
  • Published:
European Journal of Clinical Microbiology & Infectious Diseases Aims and scope Submit manuscript

Abstract

Surveillance of influenza epidemics is a priority for risk assessment and pandemic preparedness, yet representation of their spatiotemporal intensity remains limited. Using the epidemic of influenza type A in 2016 in Australia, we demonstrated a simple but statistically sound adaptive method of mapping epidemic evolution over space and time. Weekly counts of persons with laboratory confirmed influenza type A infections in Australia in 2016 were analysed by official national statistical region. Weekly standardised epidemic intensity was represented by a standard score (z-score) calculated using the standard deviation of below-median counts in the previous 52 weeks. A geographic information system (GIS) was used to present the epidemic progression. There were 79,628 notifications of influenza A infections included. Of these, 79,218 (99.5%) were allocated to a geographical area. The GIS maps indicated areas of elevated epidemic intensity across Australia by week and area that were consistent with the observed start, peak and decline of the epidemic when compared with counts aggregated at the state and territory level. This simple, adaptable approach could improve local level epidemic intelligence in a variety of settings and for other diseases. It may also facilitate increased understanding of geographic epidemic dynamics.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

References

  1. World Health Organization (2013) Global epidemiological surveillance standards for influenza. World Health Organization, Geneva http://www.who.int/influenza/resources/documents/influenza_surveillance_manual/en/. Cited 24 Jan 2018

  2. Muscatello DJ, Chughtai AA, Heywood A, Gardner LM, Heslop DJ, MacIntyre CR (2017) Translation of real-time infectious disease modeling into routine public health practice. Emerg Infect Dis 23(5):e161720

    Article  PubMed Central  Google Scholar 

  3. CDC (2018) Fluview. United States Centers for Disease Control and Prevention, Atlanta, https://www.cdc.gov/flu/weekly/index.htm. Cited 2 Feb 2018

  4. Australian Department of Health (2017) Australian Influenza Surveillance Report and Activity Updates. Australian Government Department of Health, Canberra, http://www.health.gov.au/flureport. Cited 15 Feb 2017

  5. Vega T, Lozano JE, Meerhoff T, Snacken R, Beauté J, Jorgensen P, Ortiz de Lejarazu R, Domegan L, Mossong J, Nielsen J, Born R, Larrauri A, Brown C (2015) Influenza surveillance in Europe: comparing intensity levels calculated using the moving epidemic method. Influenza Other Respir Viruses 9(5):234–246

    Article  PubMed  PubMed Central  Google Scholar 

  6. Bangert M, Gil H, Oliva J, Delgado C, Vega T, De Mateo S, Larrauri A (2016) Pilot study to harmonize the reported influenza intensity levels within the Spanish influenza sentinel surveillance system (SISSS) using the moving epidemic method (MEM). Epidemiol Infect 145(4):715–722

    Article  PubMed  Google Scholar 

  7. Pelat C, Bonmarin I, Ruello M, Fouillet A, Caserio-Schönemann C, Levy-Bruhl D, Le Strat Y, Regional Influenza study group (2017) Improving regional influenza surveillance through a combination of automated outbreak detection methods: the 2015/16 season in France. Euro Surveill 22 (32):pii=30593

  8. Communicable Diseases Network of Australia (2015) Introduction to the National Notifiable Diseases Surveillance System http://www.health.gov.au/internet/main/publishing.nsf/Content/cda-surveil-nndss-nndssintro.htm. Cited 10 Apr 2019

  9. Muscatello DJ, Amin J, MacIntyre CR, Newall AT, Rawlinson WD, Sintchenko V, Gilmour R, Thackway S (2014) Inaccurate ascertainment of morbidity and mortality due to influenza in administrative databases: a population-based record linkage study. PLoS One 9(5):e98446

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Kelly HA, Grant KA, Tay EL, Franklin L, Hurt AC (2013) The significance of increased influenza notifications during spring and summer of 2010–11 in Australia. Influenza Other Respir Viruses 7(6):1136–1141

    Article  PubMed  Google Scholar 

  11. Sonesson C, Bock D (2003) A review and discussion of prospective statistical surveillance in public health. J R Stat Soc Ser A (Statistics in Society) 166(1):5–21

    Article  Google Scholar 

  12. Oakland JS (1996) Statistical process control. Butterworth-Heinemann, Oxford, pp 181–189

    Google Scholar 

  13. Woodall WH (2006) The use of control charts in health-care and public-health surveillance. J Qual Technol 38(2):89–104

    Article  Google Scholar 

  14. Australian Bureau of Statistics (2017) Australian statistical geography standard (ASGS). Australian Bureau of Statistics, Canberra, http://www.abs.gov.au/websitedbs/D3310114.nsf/home/Australian+Statistical+Geography+Standard+(ASGS) . Cited 29 Jan 2018

  15. National Center for Immunization and Respiratory Diseases (2017) Overview of influenza surveillance in the United States. Centers for Disease Control and Prevention, Atlanta, https://www.cdc.gov/flu/weekly/overview.htm. Cited 27 Jan 2018

Download references

Acknowledgments

National Notifiable Diseases Surveillance System data on influenza were provided by the Office of Health Protection, Department of Health, on behalf of the Communicable Diseases Network Australia.

Funding

This research was supported by the 2017 Small Scale Research Support Scheme of the School of Public Health and Community Medicine, University of New South Wales.

Author information

Authors and Affiliations

  1. School of Public Health and Community Medicine, University of New South Wales, Sydney, NSW, 2052, Australia

    David J. Muscatello, Robert Neil F. Leong, Robin M. Turner & Anthony T. Newall

  2. University of Otago, PO Box 56, Dunedin, 9054, New Zealand

    Robin M. Turner

Authors
  1. David J. Muscatello
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Robert Neil F. Leong
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Robin M. Turner
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Anthony T. Newall
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to David J. Muscatello.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

ESM 1

(MP4 1517 kb)

ESM 2

(PDF 5.52 MB)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Muscatello, D.J., Leong, R.N.F., Turner, R.M. et al. Rapid mapping of the spatial and temporal intensity of influenza. Eur J Clin Microbiol Infect Dis 38, 1307–1312 (2019). https://doi.org/10.1007/s10096-019-03554-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10096-019-03554-7

Keywords

Search

Navigation