Near real time monitoring and forecasting for COVID-19 situational awareness

Robert Stewart; Samantha Erwin; Jesse Piburn; Nicholas Nagle; Jason Kaufman; Alina Peluso; J. Blair Christian; Joshua Grant; Alexandre Sorokine; Budhendra Bhaduri

doi:10.1016/j.apgeog.2022.102759

Near real time monitoring and forecasting for COVID-19 situational awareness

Robert Stewart, Samantha Erwin, Jesse Piburn, Nicholas Nagle, Jason Kaufman, Alina Peluso, J. Blair Christian, Joshua Grant, Alexandre Sorokine, Budhendra Bhaduri

Research output: Contribution to journal › Article › peer-review

4 Scopus citations

Abstract

In the opening months of the pandemic, the need for situational awareness was urgent. Forecasting models such as the Susceptible-Infectious-Recovered (SIR) model were hampered by limited testing data and key information on mobility, contact tracing, and local policy variations would not be consistently available for months. New case counts from sources like John Hopkins University and the NY Times were systematically reliable. Using these data, we developed the novel COVID County Situational Awareness Tool (CCSAT) for reliable monitoring and decision support. In CCSAT, we developed a retrospective seven-day moving window semantic map of county-level disease magnitude and acceleration that smoothed noisy daily variations. We also developed a novel Bayesian model that reliably forecasted county-level magnitude and acceleration for the upcoming week based on population and new case count data. Together these formed a robust operational update including county-level maps of new case rate changes, estimates of new cases in the upcoming week, and measures of model reliability. We found CCSAT provided stable, reliable estimates across the seven-day time window, with the greatest errors occurring in cases of anomalous, single day spikes. In this paper, we provide CCSAT details and apply it to a single week in June 2020.

Original language	English
Article number	102759
Journal	Applied Geography
Volume	146
DOIs	https://doi.org/10.1016/j.apgeog.2022.102759
State	Published - Sep 2022

Funding

We would like to thank our Compute and Data Environment for Science (CADES) colleagues at the Oak Ridge National Laboratory, for supplying the computational resources needed to carry out this work. CADES is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC05-00OR22725 . Research was supported by the DOE Office of Science through the National Virtual Biotechnology Laboratory, a consortium of DOE national laboratories focused on response to COVID-19, with funding provided by the Coronavirus CARES Act. We would like to thank our Compute and Data Environment for Science (CADES) colleagues at the Oak Ridge National Laboratory, for supplying the computational resources needed to carry out this work. CADES is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC05-00OR22725. Research was supported by the DOE Office of Science through the National Virtual Biotechnology Laboratory, a consortium of DOE national laboratories focused on response to COVID-19, with funding provided by the Coronavirus CARES Act.

Funders	Funder number
CADES
Data Environment for Science
National Virtual Biotechnology Laboratory
U.S. Department of Energy	DE-AC05-00OR22725
Office of Science

Keywords

Bayesian
COVID-19
Forecasting
Monitoring
Spatio-temporal

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1016/j.apgeog.2022.102759

Cite this

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abstract = "In the opening months of the pandemic, the need for situational awareness was urgent. Forecasting models such as the Susceptible-Infectious-Recovered (SIR) model were hampered by limited testing data and key information on mobility, contact tracing, and local policy variations would not be consistently available for months. New case counts from sources like John Hopkins University and the NY Times were systematically reliable. Using these data, we developed the novel COVID County Situational Awareness Tool (CCSAT) for reliable monitoring and decision support. In CCSAT, we developed a retrospective seven-day moving window semantic map of county-level disease magnitude and acceleration that smoothed noisy daily variations. We also developed a novel Bayesian model that reliably forecasted county-level magnitude and acceleration for the upcoming week based on population and new case count data. Together these formed a robust operational update including county-level maps of new case rate changes, estimates of new cases in the upcoming week, and measures of model reliability. We found CCSAT provided stable, reliable estimates across the seven-day time window, with the greatest errors occurring in cases of anomalous, single day spikes. In this paper, we provide CCSAT details and apply it to a single week in June 2020.",

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Near real time monitoring and forecasting for COVID-19 situational awareness

Abstract

Funding

Keywords

UN SDGs

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