RAPID: AI- and Data-driven Integrated Framework for Hierarchical Community-level Risk Assessment

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The fast evolving and deadly outbreak of coronavirus disease (COVID-19) has created one of the most challenging issues facing global public health. According to the Centers for Disease Control and Prevention (CDC), before a vaccine or drug becomes widely available, community mitigation, which is a set of actions that persons and communities can take to help slow the spread of respiratory virus infections, is the most readily available interventions to help slow transmission of the virus in communities. A growing number of areas are reporting community transmission of the virus, which would represent a significant turn for the worse in the battle against the novel coronavirus; this points to an urgent need for expanded surveillance so we can better understand the spread of COVID-19 and better respond with actionable strategies for community mitigation. By advancing capabilities of artificial intelligence (AI) and leveraging the large-scale and real-time data generated from heterogeneous sources, the goal of this project is to design and develop an AI- and data-driven integrated framework to provide real-time hierarchical community-level risk assessment to help combat the COVID-19 pandemic.The research will have three main parts. First, the research team will construct a novel heterogeneous graph architecture to comprehensively model the large-scale and real-time pandemic related data collected from multiple sources. Second, the team will develop conditional generative adversarial nets for graph enrichment to address the challenge of limited data that might be available for learning. Third, the team will develop algorithms to model potential community transmission routes and design an innovative heterogeneous graph auto-encoder model for hierarchical community-level risk assessment. Through the potential community transmission route modeling, the developed framework will facilitate a predictive understanding of the spread of the virus; by providing the dynamic and real-time COVID-19 risk assessment, the planned work will enable the general public to select appropriate actions for protection while minimizing disruptions to daily life to the extent possible (i.e., mitigate the negative effects of COVID-19 on public health, society, and the economy). The planned research will benefit intelligent information management where multiple data sources are involved and secure and trustworthy cyberspace with applications such as malware detection and mitigation. The project integrates research with education through curriculum development, the participation of underrepresented groups, and student mentoring activities.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.