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McDonald DJ, Bien J, Green A, Hu AJ, DeFries N, Hyun S, Oliveira NL, Sharpnack J, Tang J, Tibshirani R, Ventura V, Wasserman L, Tibshirani RJ. Can auxiliary indicators improve COVID-19 forecasting and hotspot prediction? Proc Natl Acad Sci U S A 2021; 118:e2111453118. [PMID: 34903655 PMCID: PMC8713796 DOI: 10.1073/pnas.2111453118] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/02/2021] [Indexed: 02/07/2023] Open
Abstract
Short-term forecasts of traditional streams from public health reporting (such as cases, hospitalizations, and deaths) are a key input to public health decision-making during a pandemic. Since early 2020, our research group has worked with data partners to collect, curate, and make publicly available numerous real-time COVID-19 indicators, providing multiple views of pandemic activity in the United States. This paper studies the utility of five such indicators-derived from deidentified medical insurance claims, self-reported symptoms from online surveys, and COVID-related Google search activity-from a forecasting perspective. For each indicator, we ask whether its inclusion in an autoregressive (AR) model leads to improved predictive accuracy relative to the same model excluding it. Such an AR model, without external features, is already competitive with many top COVID-19 forecasting models in use today. Our analysis reveals that 1) inclusion of each of these five indicators improves on the overall predictive accuracy of the AR model; 2) predictive gains are in general most pronounced during times in which COVID cases are trending in "flat" or "down" directions; and 3) one indicator, based on Google searches, seems to be particularly helpful during "up" trends.
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Affiliation(s)
- Daniel J McDonald
- Department of Statistics, University of British Columbia, Vancouver, BC, Canada V6T 1Z4;
| | - Jacob Bien
- Department of Data Sciences and Operations, University of Southern California, Los Angeles, CA 90089
| | - Alden Green
- Department of Statistics & Data Science, Carnegie Mellon University, Pittsburgh, PA 15213
| | - Addison J Hu
- Department of Statistics & Data Science, Carnegie Mellon University, Pittsburgh, PA 15213
- Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA 15213
| | - Nat DeFries
- Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA 15213
| | - Sangwon Hyun
- Department of Data Sciences and Operations, University of Southern California, Los Angeles, CA 90089
| | - Natalia L Oliveira
- Department of Statistics & Data Science, Carnegie Mellon University, Pittsburgh, PA 15213
- Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA 15213
| | - James Sharpnack
- Department of Statistics, University of California, Davis, CA 95616
| | - Jingjing Tang
- Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA 15213
| | - Robert Tibshirani
- Department of Statistics, Stanford University, Stanford, CA 94305
- Department of Biomedical Data Science, Stanford University, Stanford, CA 94305
| | - Valérie Ventura
- Department of Statistics & Data Science, Carnegie Mellon University, Pittsburgh, PA 15213
| | - Larry Wasserman
- Department of Statistics & Data Science, Carnegie Mellon University, Pittsburgh, PA 15213
- Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA 15213
| | - Ryan J Tibshirani
- Department of Statistics & Data Science, Carnegie Mellon University, Pittsburgh, PA 15213
- Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA 15213
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