#StayHome or #Marathon? Social Media Enhanced Pandemic Surveillance on Spatial-temporal Dynamic Graphs

TL;DR

This paper introduces SMART, a social media-enhanced framework that leverages social media data and dynamic graphs for improved long-term COVID-19 pandemic surveillance and prediction at the state level in the US.

Contribution

The paper presents a novel framework combining social media data with dynamic graph modeling for pandemic monitoring, surpassing existing statistical and epidemic models in accuracy.

Findings

Outperforms baselines by 7.3% in confirmed case prediction.

Outperforms baselines by 7.4% in fatality prediction.

Effectively captures social media events influencing pandemic trends.

Abstract

COVID-19 has caused lasting damage to almost every domain in public health, society, and economy. To monitor the pandemic trend, existing studies rely on the aggregation of traditional statistical models and epidemic spread theory. In other words, historical statistics of COVID-19, as well as the population mobility data, become the essential knowledge for monitoring the pandemic trend. However, these solutions can barely provide precise prediction and satisfactory explanations on the long-term disease surveillance while the ubiquitous social media resources can be the key enabler for solving this problem. For example, serious discussions may occur on social media before and after some breaking events take place. These events, such as marathon and parade, may impact the spread of the virus. To take advantage of the social media data, we propose a novel framework, Social Media enhAnced pandemic suRveillance Technique (SMART), which is composed of two modules: (i) information extraction module to construct heterogeneous knowledge graphs based on the extracted events and relationships among them; (ii) time series prediction module to provide both short-term and long-term forecasts of the confirmed cases and fatality at the state-level in the United States and to discover risk factors for COVID-19 interventions. Extensive experiments show that our method largely outperforms the state-of-the-art baselines by 7.3% and 7.4% in confirmed case/fatality prediction, respectively.