ArticleiScience2025
Unpacking Ross River virus trends in Queensland: Regional insights and predictive modeling.
Article in iScience, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Ross River virus transmission, infection, and disease: two and a half decades of research progress-an updated cross-disciplinary review.Clinical microbiology reviews · 2026Review
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.