Analysis and prediction of Ross River virus transmission in New South Wales, Australia.

Published

Journal Article

BACKGROUND: Ross River virus (RRV) disease is the most widespread mosquito-borne disease in Australia. The disease is maintained in enzootic cycles between mosquitoes and reservoir hosts. During outbreaks and in endemic regions, RRV transmission can be sustained between vectors and reservoir hosts in zoonotic cycles with spillover to humans. Symptoms include arthritis, rash, fever and fatigue and can persist for several months. The prevalence and associated morbidity make this disease a medically and economically important mosquito-borne disease in Australia. METHODS: Climate, environment, and RRV vector and reservoir host information were used to develop predictive models in four regions in NSW over a 13-year period (1991-2004). Polynomial distributed lag (PDL) models were used to explore long-term influences of up to 2 years ago that could be related to RRV activity. RESULTS: Each regional model consisted of a unique combination of predictors for RRV disease highlighting the differences in the disease ecology and epidemiology in New South Wales (NSW). Events up to 2 years before were found to influence RRV activity. The shorter-term associations may reflect conditions that promote virus amplification in RRV vectors whereas long-term associations may reflect RRV reservoir host breeding and herd immunity. The models indicate an association between host populations and RRV disease, lagged by 24 months, suggesting two or more generations of susceptible juveniles may be necessary for an outbreak. Model sensitivities ranged from 60.4% to 73.1%, and model specificities ranged from 57.9% to 90.7%. This was the first study to include reservoir host data into statistical RRV models; the inclusion of host parameters was found to improve model fit significantly. CONCLUSION: The research presents the novel use of a combination of climate, environment, and RRV vector and reservoir host information in statistical predictive models. The models have potential for public health decision-making.

Full Text

Cited Authors

  • Ng, V; Dear, K; Harley, D; McMichael, A

Published Date

  • June 2014

Published In

Volume / Issue

  • 14 / 6

Start / End Page

  • 422 - 438

PubMed ID

  • 24745350

Pubmed Central ID

  • 24745350

Electronic International Standard Serial Number (EISSN)

  • 1557-7759

International Standard Serial Number (ISSN)

  • 1530-3667

Digital Object Identifier (DOI)

  • 10.1089/vbz.2012.1284

Language

  • eng