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COVID-19 has caused lasting damage to almost every domain in public health,society, and economy. To monitor the pandemic trend, existing studies rely onthe aggregation of traditional statistical models and epidemic spread theory.In other words, historical statistics of COVID-19, as well as the populationmobility data, become the essential knowledge for monitoring the pandemictrend. However, these solutions can barely provide precise prediction andsatisfactory explanations on the long-term disease surveillance while theubiquitous social media resources can be the key enabler for solving thisproblem. For example, serious discussions may occur on social media before andafter some breaking events take place. These events, such as marathon andparade, may impact the spread of the virus. To take advantage of the socialmedia data, we propose a novel framework, Social Media enhAnced pandemicsuRveillance Technique (SMART), which is composed of two modules: (i)information extraction module to construct heterogeneous knowledge graphs basedon the extracted events and relationships among them; (ii) time seriesprediction module to provide both short-term and long-term forecasts of theconfirmed cases and fatality at the state-level in the United States and todiscover risk factors for COVID-19 interventions. Extensive experiments showthat our method largely outperforms the state-of-the-art baselines by 7.3% and7.4% in confirmed case/fatality prediction, respectively.

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