Using ecological variables to predict Ross River virus disease incidence in South Australia

Jingwen Liu, Alana Hansen, Scott Cameron, Craig Williams, Stephen Fricker, Peng Bi

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

Background: Ross River virus (RRV) disease is Australia's most widespread vector-borne disease causing significant public health concern. The aim of this study was to identify the ecological covariates of RRV risk and to develop epidemic forecasting models in a disease hotspot region of South Australia. Methods: Seasonal autoregressive integrated moving average models were used to predict the incidence of RRV disease in the Riverland region of South Australia, an area known to have a high incidence of the disease. The model was developed using data from January 2000 to December 2012 then validated using disease notification data on reported cases for the following year. Results: Monthly numbers of the mosquito Culex annulirostris (β=0.033, p<0.001) and total rainfall (β=0.263, p=0.002) were significant predictors of RRV transmission in the study region. The forecasted RRV incidence in the predictive model was generally consistent with the actual number of cases in the study area. Conclusions: A predictive model has been shown to be useful in forecasting the occurrence of RRV disease, with increased vector populations and rainfall being important factors associated with transmission. This approach may be useful in a public health context by providing early warning of vector-borne diseases in other settings.

Original languageEnglish
Pages (from-to)1045-1053
Number of pages9
JournalTransactions of the Royal Society of Tropical Medicine and Hygiene
Volume115
Issue number9
DOIs
Publication statusPublished or Issued - 1 Sep 2021
Externally publishedYes

Keywords

  • Ross River virus
  • climate change
  • epidemiology
  • mosquito

ASJC Scopus subject areas

  • Parasitology
  • Public Health, Environmental and Occupational Health
  • Infectious Diseases

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