Spotting Virus from Satellites: Modeling the Circulation of West Nile Virus Through Graph Neural Networks

TL;DR

This paper introduces a graph neural network approach that leverages satellite imagery and environmental data to predict West Nile Virus circulation, considering spatial and temporal factors for improved accuracy.

Contribution

It proposes a novel multi-relational graph neural network model that integrates satellite data, environmental features, and seasonality for virus prediction, advancing beyond site-independent models.

Findings

MAGAT outperforms baseline models in predicting WNV circulation.

Pre-training significantly improves model performance.

Incorporating multiple relations enhances spatial awareness in predictions.

Abstract

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.