The occurrence of West Nile Virus (WNV) represents one of the most common mosquito-borne zoonosis viral infections. Its circulation is usually associated with climatic and environmental conditions suitable for vector proliferation and virus replication. On top of that, several statistical models have been developed to shape and forecast WNV circulation: in particular, the recent massive availability of Earth Observation (EO) data, coupled with the continuous advances in the field of Artificial Intelligence, offer valuable opportunities. In this paper, we seek to predict WNV circulation by feeding Deep Neural Networks (DNNs) with satellite images, which have been extensively shown to hold environmental and climatic features. Notably, while previous approaches analyze each geographical site independently, we propose a spatial-aware approach that considers also the characteristics of close sites. Specifically, we build upon Graph Neural Networks (GNN) to aggregate features from neighbouring places, and further extend these modules to consider multiple relations, such as the difference in temperature and soil moisture between two sites, as well as the geographical distance. Moreover, we inject time-related information directly into the model to take into account the seasonality of virus spread. We design an experimental setting that combines satellite images - from Landsat and Sentinel missions - with ground truth observations of WNV circulation in Italy. We show that our proposed Multi-Adjacency Graph Attention Network (MAGAT) consistently leads to higher performance when paired with an appropriate pre-training stage. Finally, we assess the importance of each component of MAGAT in our ablation studies.

翻译：西尼罗河病毒（WNV）的爆发是最常见的蚊媒人畜共患病毒性感染之一。该病毒的传播通常与有利于媒介增殖和病毒复制的气候环境条件相关。此外，已有多种统计模型被开发用于刻画和预测WNV传播：特别是近年来地球观测（EO）数据的海量可用性，结合人工智能领域的持续进步，为相关研究提供了宝贵机遇。本文旨在通过向深度神经网络（DNNs）输入卫星影像来预测WNV传播——大量研究表明卫星影像蕴含环境与气候特征。值得注意的是，与过去独立分析每个地理位点的方法不同，我们提出了一种空间感知方法，同时考虑邻近位点的特征。具体而言，我们基于图神经网络（GNN）聚合邻近地点的特征，并进一步扩展这些模块以考虑多重关系，例如两地间的温差、土壤湿度差异以及地理距离。此外，我们直接将时间相关信息注入模型，以考虑病毒传播的季节性。我们设计了一种实验方案，将来自Landsat和Sentinel任务的卫星影像与意大利WNV传播的地面实况观测数据相结合。研究表明，我们提出的多邻接图注意力网络（MAGAT）在配合适当的预训练阶段时，能够持续获得更优性能。最后，我们通过消融研究评估了MAGAT各组件的相对重要性。