Chapter 6 Cloud-based data pipeline orchestration platform for COVID-19 evidence-based analytics

Identifying high-quality publications remains a critical challenge for health-care data consumers (e.g., immunologists, clinical researchers) who seek to make timely decisions related to the COVID-19 pandemic response. Currently, researchers perform a manual literature review process to compile and analyze publications from disparate medical journal databases. Such a process is cumbersome, inefficient, and increases the time to complete research tasks. In this book chapter, we describe a cloud-based, intelligent data pipeline orchestration platform, viz., “OnTimeEvidence” that provides health-care consumers with easy access to publication archives and analytics tools for rapid pandemic-related knowledge discovery tasks. This platform aims to reduce the burden and expensive time to find, sort, and analyze publications in terms of their level of evidence. We also present a case study of how OnTimeEvidence platform can be configured to help health-care data consumers to combine and analyze multiple data sources using interactive interfaces featuring workspaces equipped with analytics tools.
Date: 31 Dec 2022
DOI: 10.1016/b978-0-323-90054-6.00003-9
Journal: Novel AI and Data Science Advancements for Sustainability in the Era of COVID-19
CORD UID: 202h3g20
ML/Curated Information
Viruses: SARS-CoV-2
Article Type(s): Book chapter
Topics: Surveillance