Climatic, landscape, and host features are critical components in shaping outbreaks of vector-borne diseases. However, the relationship between the outbreaks of vector-borne pathogens and their environmental drivers is typically complicated, nonlinear, and may vary by taxonomic units below the species level (e.g., strain or serotype). Here, we aim to untangle how these complex forces shape the risk of outbreaks of Bluetongue virus (BTV); a vector-borne pathogen that is continuously emerging and re-emerging across Europe, with severe economic implications. We tested if the ecological predictors of BTV outbreak risk were serotype-specific by examining the most prevalent serotypes recorded in Europe (1, 4, and 8). We used a robust machine learning (ML) pipeline and 23 relevant environmental features to fit predictive models to 24,245 outbreaks reported in 25 European countries between 2000 and 2019. Our ML models demonstrated high predictive performance for all BTV serotypes (accuracies > 0.87) and revealed strong nonlinear relationships between BTV outbreak risk and environmental and host features. Serotype-specific analysis suggests, however, that each of the major serotypes (1, 4, and 8) had a unique outbreak risk profile. For example, temperature and midge abundance were as the most important characteristics shaping serotype 1, whereas for serotype 4 goat density and temperature were more important. We were also able to identify strong interactive effects between environmental and host characteristics that were also serotype specific. Our ML pipeline was able to reveal more in-depth insights into the complex epidemiology of BTVs and can guide policymakers in intervention strategies to help reduce the economic implications and social cost of this important pathogen.

中文翻译：

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环境、载体还是宿主？使用机器学习来解开驱动虫媒病毒爆发的机制

气候、景观和宿主特征是形成媒介传播疾病爆发的关键组成部分。然而，病媒传播病原体的爆发与其环境驱动因素之间的关系通常是复杂的、非线性的，并且可能因物种水平以下的分类单位（例如菌株或血清型）而异。在这里，我们旨在解开这些复杂的力量如何影响蓝舌病毒 (BTV) 爆发的风险；一种媒介传播的病原体，在欧洲不断出现和重新出现，具有严重的经济影响。我们通过检查欧洲记录的最普遍的血清型（1、4 和 8）来测试 BTV 爆发风险的生态预测因子是否具有血清型特异性。我们使用了强大的机器学习 (ML) 管道和 23 个相关的环境特征来将预测模型拟合到 24 个，2000 年至 2019 年间在 25 个欧洲国家报告了 245 次暴发。我们的 ML 模型显示出对所有 BTV 血清型的高预测性能（准确度 > 0.87），并揭示了 BTV 暴发风险与环境和宿主特征之间的强非线性关系。然而，血清型特异性分析表明，每种主要血清型（1、4 和 8）都有独特的暴发风险特征。例如，温度和蠓丰度是影响血清型 1 的最重要特征，而对于血清型 4，山羊密度和温度更为重要。我们还能够确定环境和宿主特征之间的强烈交互作用，这些特征也是血清型特异性的。