| Name: |
COVID-19 Chimeric Forecast |
| Abbreviation: |
Chimeric
|
| Owner: |
allicodi
|
| Team name: |
Computational Uncertainty Laboratory |
| Description: |
This chimeric forecast is a combination of Metaculus and GJO expert consensus forecasts and the COVIDhub-ensemble- an ensemble of computational models hosted by the COVID-19 Forecast Hub (https://covid19forecasthub.org/). Experts are defined as people with several years of experience in the study or modeling of infectious disease and/or public health decision making. |
| Contributors: |
Thomas McAndrew (Lehigh University) <mcandrew@lehigh.edu>, Juan Cambeiro (Metaculus), David Braun (Lehigh University), Tamay Besiroglu (Metaculus), Eva Chen (Good Judgement Open) <chen@goodjudgment.com>, Luis Enrique Urtubey (Good Judgement Open) <decesaris@goodjudgment.com>, Damon Luk (Lehigh University), Allison Codi (Lehigh University) |
| License: |
Creative Commons Attribution 4.0 |
| Notes: |
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| Citation: |
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| Methods: |
A chimeric forecast is built by combining an ensemble of computational models and a consensus of predictions from humans- in our case, predictions from trained forecasters and experts. The COVID-19 forecast hub ensemble cumulative predictive density is divided into 23 quantiles for incident deaths and 7 quantiles for incident cases. We can extract the same number of quantiles from our consensus forecast and compute quantiles for our chimeric forecast as:
Qchimericforecast = 0.5(QCOVID-19 ensemble) + 0.5(QConsensus forecast) where Qx is a quantile from distribution x. |
| Home: |
https://github.com/computationalUncertaintyLab/aggStatModelsAndHumanJudgment_PUBL
|
| Auxiliary data: |
(No URL)
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