WDCIP: spatio-temporal AI-driven disease control intelligent platform for combating COVID-19 pandemic

Abstract

ABSTRACT The outbreak and subsequent recurring waves of COVID −19 pose threats on the emergency management and people’s daily life, while the large-scale spatio-temporal epidemiological data have sure come in handy in epidemic surveillance. Nonetheless, some challenges remain to be addressed in terms of multi-source heterogeneous data fusion, deep mining, and comprehensive applications. The Spatio-Temporal Artificial Intelligence (STAI) technology, which focuses on integrating spatial related time-series data, artificial intelligence models, and digital tools to provide intelligent computing platforms and applications, opens up new opportunities for scientific epidemic control. To this end, we leverage STAI and long-term experience in location-based intelligent services in the work. Specifically, we devise and develop a STAI-driven digital infrastructure, namely, WAYZ Disease Control Intelligent Platform (WDCIP), which consists of a systematic framework for building pipelines from automatic spatio-temporal data collection, processing to AI-based analysis and inference implementation for providing appropriate applications serving various epidemic scenarios. According to the platform implementation logic, our work can be performed and summarized from three aspects: (1) a STAI-driven integrated system; (2) a hybrid GNN-based approach for hierarchical risk assessment (as the core algorithm of WDCIP); and (3) comprehensive applications for social epidemic containment. This work makes a pivotal contribution to facilitating the aggregation and full utilization of spatio-temporal epidemic data from multiple sources, where the real-time human mobility data generated by high-precision mobile positioning plays a vital role in sensing the spread of the epidemic. So far, WDCIP has accumulated more than 200 million users who have been served in life convenience and decision-making during the pandemic.