Skip to main content
×
×
Home

Predictive modelling of Ross River virus notifications in southeastern Australia

  • Z. CUTCHER (a1) (a2), E. WILLIAMSON (a1) (a3), S. E. LYNCH (a4), S. ROWE (a2), H. J. CLOTHIER (a1) and S. M. FIRESTONE (a5)...
Summary
SUMMARY

Ross River virus (RRV) is a mosquito-borne virus endemic to Australia. The disease, marked by arthritis, myalgia and rash, has a complex epidemiology involving several mosquito species and wildlife reservoirs. Outbreak years coincide with climatic conditions conducive to mosquito population growth. We developed regression models for human RRV notifications in the Mildura Local Government Area, Victoria, Australia with the objective of increasing understanding of the relationships in this complex system, providing trigger points for intervention and developing a forecast model. Surveillance, climatic, environmental and entomological data for the period July 2000–June 2011 were used for model training then forecasts were validated for July 2011–June 2015. Rainfall and vapour pressure were the key factors for forecasting RRV notifications. Validation of models showed they predicted RRV counts with an accuracy of 81%. Two major RRV mosquito vectors (Culex annulirostris and Aedes camptorhynchus) were important in the final estimation model at proximal lags. The findings of this analysis advance understanding of the drivers of RRV in temperate climatic zones and the models will inform public health agencies of periods of increased risk.

Copyright
Corresponding author
*Author for correspondence: Dr S. M. Firestone, Asia-Pacific Centre for Animal Health, Faculty of Veterinary and Agricultural Sciences, The University of Melbourne, Parkville, Victoria 3010, Australia. (Email: Simon.Firestone@unimelb.edu.au)
References
Hide All
1. Knope K, et al. Arboviral diseases and malaria in Australia, 2010–11: annual report of the National Arbovirus and Malaria Advisory Committee. Communicable Diseases Intelligence Quarterly Report 2013; 37: E1.
2. Westley-Wise VJ, et al. Ross River virus infection on the north coast of New South Wales. Australian and New Zealand Journal of Public Health 1996; 20: 8792.
3. Jacups SP, Whelan PI, Currie BJ. Ross River virus and Barmah Forest virus infections: a review of history, ecology, and predictive models, with implications for tropical northern Australia. Vector-Borne and Zoonotic Diseases 2008; 8: 283298.
4. Harley D, Sleigh A, Ritchie S. Ross River virus transmission, infection, and disease: a cross-disciplinary review. Clinical Microbiology Reviews 2001; 14: 909932.
5. Australian Government Department of Health. National notifiable diseases surveillance system (NNDSS). (http://www9.health.gov.au/cda/source/cda-index.cfm) Accessed 11 February 2015.
6. Russell RC. Ross River virus: ecology and distribution. Annual Review of Entomology 2002; 47: 131.
7. El-Hage CM, McCluskey MJ, Azuolas JK. Disease suspected to be caused by Ross River virus infection of horses. Australian Veterinary Journal 2008; 86: 367370.
8. van den Hurk AF, et al. Evolution of mosquito-based arbovirus surveillance systems in Australia. Journal of Biomedicine and Biotechnology 2012; volume 2012, Article ID 325659, 8 pp. doi:10.1155/2012/325659.
9. Lynch S, et al. Victorian Arbovirus Disease Control Program Annual Report 2014–2015. The State of Victoria Department of Economic Development, Jobs, Transport and Resources: Melbourne, 2015.
10. Forbes JA. Murray Valley Encephalitis 1974, Also, The Epidemic Variance Since 1914 and Predisposing Rainfall Patterns. Sydney: Australasian Medical Publishing Company, 1978.
11. Nicholls N. A method for predicting murray valley encephalitis in Southeast Australia using the Southern Oscillation. Australian Journal of Experimental Biology and Medical Science 1986. 64.
12. Bi P, et al. Defining the local ecology of Ross River virus infections as a basis for risk assessment and better prevention strategies in South Australia: final report prepared for the S.A. Department of Health. The University of Adelaide: Adelaide, 2006.
13. Woodruff RE, et al. Early warning of Ross River virus epidemics: combining surveillance data on climate and mosquitoes. Epidemiology 2006; 17: 569575.
14. Gatton ML, Kay BH, Ryan PA. Environmental predictors of Ross River virus disease outbreaks in Queensland, Australia. American Journal of Tropical Medicine and Hygiene 2005. 72: 792799.
15. Williams CR, Fricker SR, Kokkinn MJ. Environmental and entomological factors determining Ross River virus activity in the River Murray Valley of South Australia. Australian and New Zealand Journal of Public Health 2009; 33: 284288.
16. Woodruff RE, et al. Predicting Ross River virus epidemics from regional weather data. Epidemiology 2002; 13: 384393.
17. Hu W, et al. Rainfall, mosquito density and the transmission of Ross River virus: a time-series forecasting model. Ecological Modelling 2006; 196: 505514.
18. Werner A, et al. Environmental drivers of Ross River virus in southeastern Tasmania, Australia: towards strengthening public health interventions. Epidemiology and Infection 2012; 140: 359371.
19. Akaike H. Information theory and an extension of the maximum likelihood principle. In: Selected Papers of Hirotugu Akaike. New York: Springer, 1998, pp. 199213.
20. Armitage P, Berry G, Matthews JNS. Statistical Methods in Medical Research, 4th edn. Oxford: Wiley-Blackwell, 2001, pp. 356360.
21. Grimshaw JM, et al. Applying psychological theories to evidence-based clinical practice: identifying factors predictive of lumbar spine x-ray for low back pain in UK primary care practice. Implementation Science 2011; 6: 55.
22. Dohoo I, Martin W, Stryhn H. Veterinary Epidemiologic Research, 2nd edn. Charlottetown, Prince Edward Island, Canada: VER Inc., 2009.
23. Dickey DA, Fuller WA. Distribution of the estimators for autoregressive time series with a unit root. Journal of the American Statistical Association 1979; 74: 427431.
24. Farrington C, et al. A statistical algorithm for the early detection of outbreaks of infectious disease. Journal of the Royal Statistical Society, Series A 1996; 1: 547563.
25. Axelsen JB, et al. Multiannual forecasting of seasonal influenza dynamics reveals climatic and evolutionary drivers. Proceedings of the National Academy of Sciences USA 2014; 111: 95389542.
26. Yohannes K, et al. Australia's notifiable diseases status, 2004, annual report of the National Notifiable Diseases Surveillance System. Communicable Disease Intelligence 2006; 30: 179.
27. R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. (http://www.R-project.org/). Accessed 15 September 2014.
28. Venables WN, Ripley BD. Modern Applied Statistics with S, 4th edn. New York: Springer, 2002.
29. Stevenson M, et al. epiR: an R package for the analysis of epidemiological data. R package version 0.9–69. 2015.
30. Moons KG, et al. Risk prediction models: I. Development, internal validation, and assessing the incremental value of a new (bio) marker. Heart 2012; 98: 683690.
31. Australian Government Bureau of Meteorology. El Niño – Detailed Australian Analysis (http://www.bom.gov.au/climate/enso/enlist/). Accessed 20 November 2015.
32. Rahmstorf S. A semi-empirical approach to projecting future sea-level rise. Science 2007; 315: 368370.
33. Lombard A, et al. Contribution of thermal expansion to present-day sea-level change revisited. Global and Planetary Change 2005; 47: 116.
34. Woodruff RE, Bambrisk H. Climate change impacts on the burden of Ross River virus disease. In: Garnaut R, ed. Garnaut Climate Change Review. Port Melbourne: Cambridge University Press, 2008.
35. Russell RC. Mosquito-borne disease and climate change in Australia: time for a reality check. Australian Journal of Entomology 2009; 48: 17.
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? *
×

Keywords:

Type Description Title
WORD
Supplementary materials

Cutcher supplementary material
Tables S1-S2

 Word (42 KB)
42 KB

Metrics

Full text views

Total number of HTML views: 18
Total number of PDF views: 166 *
Loading metrics...

Abstract views

Total abstract views: 520 *
Loading metrics...

* Views captured on Cambridge Core between 21st November 2016 - 18th January 2018. This data will be updated every 24 hours.