Introduction to the Special Track on Artificial Intelligence and COVID-19

The human race is facing one of the most meaningful public health emergencies in the modern era caused by the COVID-19 pandemic. This pandemic introduced various challenges, from lock-downs with significant economic costs to fundamentally altering the way of life for many people around the world. The battle to understand and control the virus is still at its early stages yet meaningful insights have already been made. The uncertainty of why some patients are infected and experience severe symptoms, while others are infected but asymptomatic, and others are not infected at all, makes managing this pandemic very challenging. Furthermore, the development of treatments and vaccines relies on knowledge generated from an ever evolving and expanding information space. Given the availability of digital data in the modern era, artificial intelligence (AI) is a meaningful tool for addressing the various challenges introduced by this unexpected pandemic. Some of the challenges include: outbreak prediction, risk modeling including infection and symptom development, testing strategy optimization, drug development, treatment repurposing, vaccine development, and others. © 2023 AI Access Foundation. All rights reserved.

Hierarchical Spatio-Temporal Graph Neural Networks for Pandemic Forecasting

The spread of COVID-19 throughout the world has led to cataclysmic consequences on the global community, which poses an urgent need to accurately understand and predict the trajectories of the pandemic. Existing research has relied on graph-structured human mobility data for the task of pandemic forecasting. To perform pandemic forecasting of COVID-19 in the United States, we curate Large-MG, a large-scale mobility dataset that contains 66 dynamic mobility graphs, with each graph having over 3k nodes and an average of 540k edges. One drawback with existing Graph Neural Networks (GNNs) for pandemic forecasting is that they generally perform information propagation in a flat way and thus ignore the inherent community structure in a mobility graph. To bridge this gap, we propose a Hierarchical Spatio-Temporal Graph Neural Network (HiSTGNN) to perform pandemic forecasting, which learns both spatial and temporal information from a sequence of dynamic mobility graphs. HiSTGNN consists of two network architectures. One is a hierarchical graph neural network (HiGNN) that constructs a two-level neural architecture: county-level and region-level, and performs information propagation in a hierarchical way. The other network architecture is a Transformer-based model that captures the temporal dynamics among the sequence of learned node representations from HiGNN. Additionally, we introduce a joint learning objective to further optimize HiSTGNN. Extensive experiments have demonstrated HiSTGNN's superior predictive power of COVID-19 new case/death counts compared with state-of-the-art baselines. © 2022 Owner/Author.

Pandemic Pulse: Unraveling and Modeling Social Signals during the COVID-19 Pandemic

COVID-19 has presented society with a unique set of challenges, including seeking a scientific understanding of the novel coronavirus, modeling its epidemiology, and inferring appropriate societal response. In this article, we posit that fighting a pandemic is as much a social endeavor as a medicinal and scientific one and focus on developing a platform for understand the social pulse of the United States during the COVID-19 crisis. We collected a multitude of data that includes longitudinal trends of news topics, social distancing behaviors, community mobility changes, web searches, and other descriptors of the COVID-19 pandemic's effects on the United States. Our preliminary results show that the number of COVID-19-related news articles published immediately after the World Health Organization declared the pandemic on March 11 have steadily decreased - regardless of changes in the number of cases or public policies. Additionally, we found that politically moderate and scientifically grounded sources have, relative to baselines measured before the beginning of the pandemic, published a lower proportion of COVID-19 news articles than more politically extreme sources - a fact that has implications for the spread and consequences of misinformation during the pandemic. We suggest that further analysis of these multi-modal signals could produce meaningful social insights and present an interactive dashboard to aid further exploration.1 © 2020 ACM.