| Name: | GLEAM_AI Flu Forecasting Model |
|---|---|
| Abbreviation: | NU_UCSD-GLEAM_AI_FLUH |
| Owner: | lshandross |
| Team name: | Northeastern University & University of California San Diego |
| Description: | This a deep surrogate model trained to mimic MOBS-GLEAM_FLUH model |
| Contributors: | Mohammadmehdi Zahedi (Network Science Institute, Northeastern University, Boston, MA, USA; The Roux Institute, Northeastern University, Portland, ME, USA) <zahedi.m@northeastern.edu> [orcid: 0009-0002-7064-3258], Dongxia (Allen) Wu (University of California, San Diego, La Jolla, CA, USA) <dowu@ucsd.edu> [orcid: 0000-0003-2412-6049], Jessica T. Davis (Network Science Institute, Northeastern University, Boston, MA, USA) <email: jes.davis@northeastern.edu> [orcid: 0000-0003-0726-1855], Yi-An Ma (University of California, San Diego, La Jolla, CA, USA) <yianma@ucsd.edu> [orcid: 0000-0001-6074-6638], Rose Yu (University of California, San Diego, La Jolla, CA, USA) <roseyu@ucsd.edu> [orcid: 0000-0002-8491-7937], Alessandro Vespignani (Network Science Institute, Northeastern University, Boston, MA, USA) <a.vespignani@northeastern.edu> [orcid: 0000-0003-3419-4205], Matteo Chinazzi (Network Science Institute, Northeastern University, Boston, MA, USA; The Roux Institute, Northeastern University, Portland, ME, USA) <m.chinazzi@northeastern.edu> [orcid: 0000-0002-5955-1929] |
| License: | Creative Commons Attribution Share Alike 4.0 |
| Notes: | designated model // data inputs: COVID-19 Reported Patient Impact and Hospital Capacity by State Timeseries (https://healthdata.gov/Hospital/COVID-19-Reported-Patient-Impact-and-Hospital-Capa/g62h-syeh). Data Provided by U.S. Department of Health & Human Services. // model version: '0.01' // team funding: JTD, AV, and MC acknowledge support from grant HHS/CDC 6U01IP001137, HHS/CDC 5U01IP0001137; DW, YM, RY acknowledge support from U. S. Army Research Office under Army-ECASE award W911NF-07-R-0003-03, the U.S. Department Of Energy, Office of Science, IARPA HAYSTAC Program, NSF Grants \#2205093, \#2146343, and \#2134274. |
| Citation: | |
| Methods: | This a deep surrogate model trained to mimic MOBS-GLEAM_FLUH model. GLEAM_FLUH is based on a metapopulation approach in which the world is divided into geographical subpopulations. \\ Human mobility between subpopulations is represented on a network. This mobility data layer identifies the numbers of individuals traveling from one sub-population to another. \\ The mobility network is made up of different kinds of mobility processes, from short-range commuting between nearby subpopulations to intercontinental flights. \\ To model short-range mobility such as commuting or car travel, we rely on databases collected from the Offices of Statistics of 30 countries on five continents. \\ Superimposed on the worldwide population and mobility layers is an agent-based epidemic model that defines the infection and population dynamics. |
| Home: | http://www.gleamproject.org/ |
| Auxiliary data: | (No URL) |
| Timezero | Data Source | Upload Time | Issued at | Version |
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| 2024-04-27 | forecast-73815c29983e.json | 2024-04-25 11:22:31 UTC | 2024-04-25 11:22:31 UTC | |
| 2024-04-20 | forecast-734e5b43a2a7.json | 2024-04-18 11:22:21 UTC | 2024-04-18 11:22:21 UTC | |
| 2024-04-13 | forecast-73f01b09d85a.json | 2024-04-13 11:18:16 UTC | 2024-04-13 11:18:16 UTC | |
| 2024-04-06 | forecast-73a77956bbab.json | 2024-04-04 11:22:20 UTC | 2024-04-04 11:22:20 UTC | |
| 2024-03-30 | forecast-758275625c38.json | 2024-03-28 11:26:09 UTC | 2024-03-28 11:26:09 UTC | |
| 2024-03-23 | forecast-75552421bf13.json | 2024-03-21 11:26:08 UTC | 2024-03-21 11:26:08 UTC | |
| 2024-03-16 | forecast-75a45ffb9afc.json | 2024-03-14 11:25:01 UTC | 2024-03-14 11:25:01 UTC | |
| 2024-03-09 | forecast-75e24e0b5e7f.json | 2024-03-07 11:25:19 UTC | 2024-03-07 11:25:19 UTC | |
| 2024-03-02 | forecast-752041cc3b03.json | 2024-03-01 15:33:11 UTC | 2024-03-01 15:33:11 UTC | |
| 2024-06-01 | (No data) | |||
| 2024-05-25 | (No data) | |||
| 2024-05-18 | (No data) | |||
| 2024-05-11 | (No data) | |||
| 2024-05-04 | forecast-73ae5be8501b.json | 2024-05-02 11:22:45 UTC | 2024-05-02 11:22:45 UTC | |
| 2024-02-24 | forecast-74e72176797f.json | 2024-02-22 11:24:55 UTC | 2024-02-22 11:24:55 UTC | |
| 2024-02-17 | forecast-754761f29fda.json | 2024-02-15 11:24:40 UTC | 2024-02-15 11:24:40 UTC | |
| 2024-02-10 | forecast-74e85c9cde77.json | 2024-02-08 11:25:10 UTC | 2024-02-08 11:25:10 UTC | |
| 2024-02-03 | forecast-710076d24ec.json | 2024-02-02 15:41:59 UTC | 2024-02-02 15:41:59 UTC | |
| 2024-01-27 | forecast-710039eb6279.json | 2024-02-02 15:41:58 UTC | 2024-02-02 15:41:58 UTC | |
| 2024-01-20 | forecast-710025409f5.json | 2024-02-02 15:41:57 UTC | 2024-02-02 15:41:57 UTC | |
| 2024-01-13 | forecast-7100417c5b72.json | 2024-02-02 15:41:55 UTC | 2024-02-02 15:41:55 UTC | |
| 2024-01-06 | forecast-710041051b55.json | 2024-02-02 15:41:54 UTC | 2024-02-02 15:41:54 UTC | |
| 2023-12-30 | forecast-7100170e186b.json | 2024-02-02 15:41:53 UTC | 2024-02-02 15:41:53 UTC | |
| 2023-12-23 | forecast-710016a57c43.json | 2024-02-02 15:41:52 UTC | 2024-02-02 15:41:52 UTC | |
| 2023-12-16 | forecast-710028ea49fc.json | 2024-02-02 15:41:51 UTC | 2024-02-02 15:41:51 UTC | |
| 2023-12-09 | forecast-71005a552c0a.json | 2024-02-02 15:41:49 UTC | 2024-02-02 15:41:49 UTC | |
| 2023-12-02 | forecast-710060c1ba2.json | 2024-02-02 15:41:48 UTC | 2024-02-02 15:41:48 UTC | |
| 2023-11-25 | (No data) | |||
| 2023-11-18 | (No data) | |||
| 2023-11-11 | (No data) | |||
| 2023-11-04 | (No data) | |||
| 2023-10-28 | (No data) | |||
| 2023-10-21 | (No data) | |||
| 2023-10-14 | (No data) | |||
| 2023-10-07 | (No data) |