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

IF 4.1 2区综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES

iScience Pub Date : 2025-08-26 DOI:10.1016/j.isci.2025.113446

Wei Qian , Kathryn Glass , David Harley , Elvina Viennet , Cameron Hurst

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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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解开罗斯河病毒在昆士兰的趋势：区域见解和预测建模

罗斯河病毒（RRV）在澳大利亚昆士兰州的传播周期各不相同。确定与区域RRV发病率相关的关键暴露有助于疾病预防和控制。利用2001年至2020年的RRV报告数据，我们建立了负二项广义线性模型来预测昆士兰州三个按热气候带（热、暖、干）分类的RRV发病率。预测指标和RRV趋势在不同地区有所不同，表明独特的传播模式：炎热地区由季节性驱动，温暖地区由疫情驱动，干旱地区为混合传播模式。重要的预测因子包括最近的RRV病例、归一化植被指数（植被覆盖密度）、澳大利亚得分的可达性/偏远性指数（指示偏远性）和土地利用比例。这些发现增强了对当地RRV传播动态的理解，突出了传播的主要环境驱动因素，并为有针对性的疾病管理策略提供了信息。这种方法也可能有利于研究在不同环境和气候中具有复杂传播周期的其他病媒传播疾病。

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来源期刊

iScience Multidisciplinary-Multidisciplinary

CiteScore

7.20

自引率

1.70%

发文量

1972

审稿时长

6 weeks

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