UPHO: Leveraging an Explainable Multimodal Big Data Analytics Framework for COVID-19 Surveillance and Research

ABSTRACT

The coronavirus disease 2019 (COVID-19) is an infectious disease with high transmissibility and acquired through the severe acute respiratory syndrome coronavirus 2 (SARS-COV-2). Scientists, physicians, and health officials are seeking innovative approaches to understand the complex COVID-19 pandemic pathway and decrease its morbidity and mortality. Incorporating artificial intelligence and data science techniques across the health science domain could improve disease surveillance, intervention planning, and policymaking. In this paper, we report our effort on the deployment of multimodal big data analytics to improve pandemic surveillance and preparedness. A common challenge for conducting multimodal big data analytics in clinical and public health settings is the issue of the integration of multidimensional heterogeneous data sources. Additional challenges for developers are explaining decisions and actions made by intelligent systems to human users, maintaining interpretability between different data sources, and privacy of health information. We present Urban Population Health Observatory (UPHO), an explainable knowledge-based multimodal data analytics platform to facilitate CoVID-19 surveillance by integrating a large volume of multimodal multidimensional, heterogenous data including social determinants of health indicators, clinical and population health data. © 2021 IEEE.