Unpacking Ross River virus trends in Queensland: Regional insights and predictive modeling

Ross River virus (RRV) transmission cycle varies across Queensland, Australia. Identifying key exposures associated with regional RRV incidence can aid disease prevention and control. Using RRV notification data from 2001 to 2020, we developed negative binomial generalized linear models to predict RRV incidence across three Queensland regions categorized by thermal climate zones (Hot, Warm, and Dry). Predictors and RRV trends differed across regions, indicating unique transmission patterns: seasonally driven in the Hot region, outbreak driven in the Warm region, and a mixed transmission pattern in the Dry region. The important predictors included recent RRV cases, normalized difference vegetation index (vegetation cover density), accessibility/remoteness index of Australia scores (indicating remoteness), and land use proportions. These findings enhance the understanding of local RRV transmission dynamics, highlight primary environmental drivers of transmission, and inform targeted disease management strategies. This approach may also benefit studies on other vector-borne diseases with complex transmission cycles in varying environments and climates.

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Publication Details

Journal
iScience
Published
2025-08-26
DOI
https://doi.org/10.1016/j.isci.2025.113446
Citations
1
Primary Topic
Viral Infections and Vectors
Type
article
Field-Weighted Citation Impact
1.40

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article

Unpacking Ross River virus trends in Queensland: Regional insights and predictive modeling

Wei Qian, Cameron Hurst, Elvina Viennet, David Harley et al.
1 citations
iScience
Viral Infections and Vectors
1.40
article

Unpacking Ross River virus trends in Queensland: Regional insights and predictive modeling

Wei Qian, Cameron Hurst, Elvina Viennet, David Harley, Kathryn Glass
article en
1 citations

Abstract

Ross River virus (RRV) transmission cycle varies across Queensland, Australia. Identifying key exposures associated with regional RRV incidence can aid disease prevention and control. Using RRV notification data from 2001 to 2020, we developed negative binomial generalized linear models to predict RRV incidence across three Queensland regions categorized by thermal climate zones (Hot, Warm, and Dry). Predictors and RRV trends differed across regions, indicating unique transmission patterns: seasonally driven in the Hot region, outbreak driven in the Warm region, and a mixed transmission pattern in the Dry region. The important predictors included recent RRV cases, normalized difference vegetation index (vegetation cover density), accessibility/remoteness index of Australia scores (indicating remoteness), and land use proportions. These findings enhance the understanding of local RRV transmission dynamics, highlight primary environmental drivers of transmission, and inform targeted disease management strategies. This approach may also benefit studies on other vector-borne diseases with complex transmission cycles in varying environments and climates.

iScienceVol. 28(10)
Thammasat University (TH), Australian National University (AU), Queensland University of Technology (AU), The University of Queensland (AU), University of the Sunshine Coast (AU), Khon Kaen University (TH), Australian Red Cross Lifeblood (AU), Mater Research (AU), Shanghai University of Traditional Chinese Medicine (CN)
QIMR Berghofer Medical Research Institute, Griffith University, Queensland University of Technology
Climate action
Openalex Percentile: Top 17%
Viral Infections and Vectors
1.40
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