| Name: | TimeSeries |
|---|---|
| Abbreviation: | CMU-TimeSeries |
| Owner: | covid19hub |
| Team name: | Carnegie Mellon Delphi Group |
| Description: | (State) A basic AR-type time series model fit using case counts and deaths as features. (County) Quantile regr. on county pop., case counts, self-reported symptom data, and doctor's visits. |
| Contributors: | Alden Green (Lead modeler - state, Carnegie Mellon University), <ajgreen@andrew.cmu.edu>, Addison Hu (Co-lead modeler - county, Carnegie Mellon University), Maria Jahja (Co-lead modeler - county, Carnegie Mellon University), Valerie Ventura (Carnegie Mellon University), Larry Wasserman (Carnegie Mellon University), Rob Tibshirani (Stanford University), Vishnu Shankar (Stanford University), Jacob Bien (University of Southern California), Logan Brooks (Carnegie Mellon University), Balasubramanian Narasimhan (Stanford University), Samyak Rajanala (Stanford University), Aaron Rumack (Carnegie Mellon University), Noah Simon (University of Washington), James Sharpnack (UC Davis), Ryan Tibshirani (Senior author, Carnegie Mellon University), and the Delphi COVID-19 Response Team |
| License: | Creative Commons Attribution 4.0 |
| Notes: | primary |
| Citation: | None |
| Methods: | State level - A basic AR-type time series model fit using lagged values of case counts and deaths as features. No assumptions are made regarding reopening or governmental interventions. The model is jointly fit across all 50 US states, after some time-alignment is performed as a preprocessing step to facilitate comparison between states. Heavier weight is placed on more recent training data, to account for nonstationarity in the underlying process. A lasso penalty is added to induce variable selection and prevent overfitting. Quantiles are computed using a residual (Gaussian) bootstrap, separately for each location. County level - A quantile regression model fit jointly across selected counties using (1) case counts, (2) self-reported COVID symptom rates, (3) doctor's visits rates related to COVID symptoms, and (4) county population. Features (1-3) are taken at various lags. Furthermore, the slopes of features (1-3) are computed over the preceding month and included in the model to provide a sense of the virus's momentum. Signals (2-3) are available as indicators through Delphi COVIDcast (http://covidcast.cmu.edu). Signal (2) is administered with the support of Facebook. No assumptions are made regarding reopening or governmental interventions. |
| Home: | https://delphi.cmu.edu |
| Auxiliary data: | Missing |
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| 2020-07-20 | 2020-07-20-CMU-TimeSeries.csv | 2020-07-20 22:59:16 EDT |
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| 2020-07-27 | 2020-07-27-CMU-TimeSeries.csv | 2020-07-27 20:59:12 EDT |
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| 2020-08-02 | 2020-08-02-CMU-TimeSeries.csv | 2020-08-03 21:21:31 EDT |
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| 2020-08-10 | 2020-08-10-CMU-TimeSeries.csv | 2020-08-10 20:53:39 EDT |
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| 2020-08-24 | 2020-08-24-CMU-TimeSeries.csv | 2020-08-24 20:54:29 EDT |
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| 2020-09-07 | 2020-09-07-CMU-TimeSeries.csv | 2020-09-07 21:12:07 EDT |
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| 2020-09-14 | 2020-09-14-CMU-TimeSeries.csv | 2020-09-14 20:53:37 EDT |
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| 2020-09-21 | 2020-09-21-CMU-TimeSeries.csv | 2020-09-21 20:55:25 EDT |
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| 2020-09-28 | 2020-09-28-CMU-TimeSeries.csv | 2020-09-29 02:25:45 EDT |
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| 2020-10-05 | 2020-10-05-CMU-TimeSeries.csv | 2020-10-06 08:37:38 EDT |
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| 2020-10-12 | 2020-10-12-CMU-TimeSeries.csv | 2020-10-12 22:00:12 EDT |
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| 2020-10-26 | 2020-10-26-CMU-TimeSeries.csv | 2020-10-26 21:44:01 EDT |
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