Real-time Epidemic Surveillance Management for Supporting COVID-19 Response Workflows

Author: ORCID icon orcid.org/0000-0001-8983-5941
Peddireddy, Akhil Sai, Computer Science - School of Engineering and Applied Science, University of Virginia
Advisors:
Marathe, Madhav, PV-Biocomplexity Initiative, University of Virginia
Venkatramanan, Srini, PV-Biocomplexity Initiative, University of Virginia
Abstract:

The COVID-19 outbreak caused by SARS-CoV-2 has disrupted the lives of people globally. It has had a huge impact on health, economies, and society in general, undoubtedly making it the pandemic of the century. As of April 1, 2021, the cumulative number of confirmed COVID-19 cases exceeded 130 million worldwide, with almost 2.85 million deaths. Global Surveillance is one of the important tools of epidemiological response to allow the public to be informed of the pandemic and to provide insights to the policymakers.

With the experience from assessing the US influenza surveillance prior to COVID-19, we identified 6 key metrics, called 6Cs, which we propose as a standard for the design and evaluation of real-time epidemic science data portals. We describe our work building the COVID-19 Surveillance Dashboard, visited by over a million users, its underlying architecture, the multitude of the data present and the data pipelines that conform to the 6Cs standard. As fluid as the pandemic remains, the biggest challenge is to adapt to the real world changes and continuously maintain the dashboard by updating the data sources, be consistent with changes in reporting and adding new data as it becomes available. We also demonstrate a number of utilities of surveillance data that we built such as Q&A based Analytics, PatchViz tool to seed, run and visualize mechanistic simulations and Ensemble Kalman Filter for high-resolution forecasting of COVID-19 cases as part of a Bayesian ensemble that is integrated each week into CDC initiated COVID-19 Forecast Hub.

We hope this work presents a framework for maintaining epidemic surveillance in real-time, discusses the use-cases and lessons learnt, and equips us with the tools & insights needed in the unfortunate event of a future pandemic.

Degree:
MS (Master of Science)
Keywords:
Surveillance, COVID-19, Forecasting, Epidemic
Language:
English
Issued Date:
2021/04/28