Near real time monitoring and forecasting for Covid-19 situational awareness
Bibliographic Data
| ID | 21449827 |
|---|---|
| Authors | Robert Stewart (0000-0002-6326-915X, Oak Ridge National Laboratory, corresponding author), Samantha Erwin (0000-0002-5162-4193, Battelle), Jesse Piburn (0000-0003-4967-7912, Oak Ridge National Laboratory), Nicholas N Nagle (0000-0002-3721-8148, University of Tennessee at Knoxville), Nicholas Nagle, Jason Kaufman, Justin Epting (0000-0001-5482-6914, Oak Ridge National Laboratory), Alina Peluso (0000-0003-2895-0406, Oak Ridge National Laboratory), J Blair Christian (0000-0002-4658-1635, Oak Ridge National Laboratory), Joshua Grant (0000-0001-6347-5060, Oak Ridge National Laboratory), Alexandre Sorokine (0000-0001-7993-1534, Oak Ridge National Laboratory), Budhendra Bhaduri (0000-0003-1555-1377, Oak Ridge National Laboratory) |
| Year | 2022 |
| Volume | 146 |
| Pages | 102759 |
| Publication date | 2022-09-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Applied Geography (JOURNAL) |
| Journal identifiers | ISSN: 0143-6228 • E-ISSN: 1873-7730 |
| Publisher | Elsevier BV (PUBLISHER) |
| DOI | 10.1016/j.apgeog.2022.102759 |
| PMID | 35945952 |
| OpenAlex | W4289886861 |
| Language | EN |
| References cited | 30 |
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
Bayesian probability · Cartography · Coronavirus disease 2019 (COVID-19) · Count data · Disease · Geography · Infectious disease (medical specialty) · Operations research · Pandemic · Population · Reliability (semiconductor) · Situation awareness · Statistics · Anomaly Detection Techniques and Applications · Artificial Intelligence · Computer Science · COVID-19 epidemiological studies · Data-Driven Disease Surveillance · Demography · Engineering · Mathematics · Medicine
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| Highly cited | No |
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