DeepEVD: Integrating Epidemiological data into deep learning frameworks based on spatio-temporal feature learning for EVD forecasting

Abstract

The paper introduces DeepEVD, an innovative framework that integrates human mobility data to forecast Ebola Virus Disease (EVD) outbreaks. Traditional epidemiological models often struggle to account for the dynamic nature of human movement, which is crucial for understanding EVD transmission. DeepEVD leverages diverse mobility data sources, including phone records, GPS traces, and social media posts, to extract significant spatio-temporal features. It utilises Graph Convolutional Networks (GCN) and Long Short Term Memory (LSTM) networks to establish connections between mobility patterns and EVD cases across both space and time. The framework was tested on real-world datasets from the 2014–2016 West Africa outbreak and the 2015–2016 Sierra Leone outbreak, demonstrating a 5%–10% reduction in forecasting errors compared to baseline methods. Ablation studies reveal the impact of various data sources and feature extraction methods on accuracy. DeepEVD not only delivers state-of-the-art performance, but it also provides actionable insights for EVD prevention and control. Implementation of the proposed DeepEVD can be accessed here https://github.com/afofanah/DeepEVDMob.