Predicting the drivers of incursion and expansion of vector‐borne diseases as part of early‐warning strategies (EWS) is a major challenge for geographically extensive diseases where spread is mediated by spatial heterogeneity in climate and other environmental drivers. Geospatial data on these environmental drivers are increasingly available affording opportunities for application to a predictive disease ecology paradigm provided the data can be synthesized and harmonized with fine‐scale, highly resolved data on vector and host responses to their environment. Here, we apply a multi‐scale big data–model integration approach using human‐guided machine learning to objectively evaluate the importance of a large suite of spatially distributed environmental variables (>400) to develop EWS for vesicular stomatitis (VS), a common viral vector‐borne vesicular disease affecting livestock throughout the Americas. Two temporally and phylogenetically distinct events were used to develop disease occurrence–environment relationships in incursion (2004) and expansion years (2005), and then to test those relationships (2014, 2015) at two scales: (1) local and (2) landscape to regional. Our results show that VS occurrence at a local scale of individual landowners was related to conditions that can be monitored (rainfall, temperatures, streamflow) or modified (vegetation). On‐site green vegetation during the month of occurrence and higher rainfall four months prior combined with either cool daytime (expansion) or nighttime (incursion) temperatures one month prior were indicators of VS occurrence. Distance to running water (incursion) and host density based on neighboring ranches (expansion) with infected animals were also important in individual years. At landscape‐to‐regional scales, conditions that favor specific VSV biological vectors were indicated, either black flies in incursion years or biting midges in expansion years. Changes in viral genetic lineage were less important to patterns in VS occurrence than factors affecting the host–vector–environment interactions. In combination with our onset map based on latitude, elevation, and long‐term annual precipitation, this year‐ and scale‐specific information can be used to develop strategies to minimize effects of future VS events. This big data approach coupled with expert knowledge and machine learning can be applied to other emerging diseases for improvement in understanding, prediction, and management of vector‐borne diseases.

中文翻译：

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大数据模型集成和AI用于媒介传播疾病的预测

预测媒介传播疾病的入侵和扩散驱动因素作为预警策略（EWS）的一部分，对于地理分布广泛的疾病而言是一项重大挑战，在这些疾病中，传播是由气候和其他环境驱动因素的空间异质性介导的。这些环境驱动因素的地理空间数据越来越多，这为将其应用于疾病和环境的媒介和宿主响应的精细，高度解析的数据进行了综合和协调，从而为预测疾病的生态学范式提供了机会。在这里，我们使用人类指导的机器学习应用多尺度的大数据模型集成方法，客观地评估了一大套空间分布的环境变量（> 400）对于开发水疱性口炎（VS）的预警系统的重要性，一种常见的病毒载体传播的水疱病，在整个美洲影响牲畜。使用两个时间和系统发育上不同的事件来发展入侵（2004）和扩展年（2005）的疾病发生-环境关系，然后以两个尺度检验这些关系（2014、2015）：（1）局部和（2）景观到区域。我们的结果表明，在各个土地所有者的地方规模上发生的VS与可以监测的条件（降雨，温度，水流）或经过修改的条件（植被）有关。在发生月份的现场绿色植被和在四个月之前的较高降雨，再加上一个月之前的白天（扩张）或夜间（入侵）温度，是VS发生的指标。在各个年份中，与流水的距离（入侵）和基于受感染动物的邻近牧场的宿主密度（扩展）也很重要。在景观到区域尺度上，表明了有利于特定VSV生物载体的条件，无论是入侵年的黑蝇还是扩张年的ing虫。病毒遗传谱系的变化对VS发生方式的重要性不如影响宿主-载体-环境相互作用的因素重要。结合我们基于纬度，海拔和长期年降水量的发病图，可以将本年度和规模特定的信息用于制定策略，以最大程度地减少未来VS事件的影响。这种大数据方法与专家知识和机器学习相结合，可以应用于其他新出现的疾病，以增进了解，