Model: IDSS COVID-19 Collaboration (Isolat) at MIT - MIT_ISOLAT-Mixtures

    Name: Mixtures of Exponentials
    Abbreviation: MIT_ISOLAT-Mixtures
    Owner: covid19hub
    Team name: IDSS COVID-19 Collaboration (Isolat) at MIT
    Description: Non-mechanistic, non-parametric model based on representing time series as a sum of bell curves.
    Contributors: Arnab Sarker (MIT) <arnabs@mit.edu>, Ali Jadbabaie (MIT) <jadbabai@mit.edu>, Devavrat Shah (MIT) <devavrat@mit.edu>
    License: Creative Commons Attribution 4.0
    Notes: primary
    Citation: https://idss.mit.edu/vignette/real-time-mixture-based-predictions/
    Methods: A non-mechanistic, non-parametric model for prediction which forecasts time series as a sum of bell curves. Confidence intervals are provided by assuming a multiplicative log-Gaussian perturbation on the observed time series. While the curve-fitting approach is based on a parsimonious function class, we also note that it arises as a simple outcome of an SIR-like process in a network setting, allowing the parameters of the fitted curve to contain structural meaning.
    Home: https://covidpredictions.mit.edu
    Auxiliary data: Missing

    Forecasts (6)

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    2021-02-24 (No data)
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    2021-02-22 2021-02-22-MIT_ISOLAT-Mixtures.csv 2021-02-22 18:21:55 UTC 2021-02-22
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    2021-02-15 2021-02-15-MIT_ISOLAT-Mixtures.csv 2021-02-15 18:38:21 UTC 2021-02-15
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    2021-02-08 2021-02-08-MIT_ISOLAT-Mixtures.csv 2021-02-08 18:33:38 UTC 2021-02-08
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    2021-02-01 2021-02-01-MIT_ISOLAT-Mixtures.csv 2021-02-02 00:50:09 UTC 2021-02-01
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    2021-01-25 2021-01-25-MIT_ISOLAT-Mixtures.csv 2021-01-25 19:02:11 UTC 2021-01-25
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    2021-01-17 2021-01-17-MIT_ISOLAT-Mixtures.csv 2021-01-18 06:48:54 UTC 2021-01-18
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