Elsevier

Applied Geography

Volume 146, September 2022, 102759
Applied Geography

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

https://doi.org/10.1016/j.apgeog.2022.102759Get rights and content
Under a Creative Commons license
open access

Highlights

  • National county-level framework for monitoring the spread of COVID-19.

  • Robust to the limited data environment prevailing in the opening months of the pandemic.

  • Bivariate mapping strategy simultaneously conveys magnitude and acceleration.

  • Novel Bayesian nowcast model provides robust new case estimates a week in advance.

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.

Keywords

COVID-19
Spatio-temporal
Monitoring
Forecasting
Bayesian

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