Model: UCLA Statistical Machine Learning Lab - UCLA-SuEIR

    Name: SuEIR
    Abbreviation: UCLA-SuEIR
    Owner: covid19hub
    Team name: UCLA Statistical Machine Learning Lab
    Description: The SuEIR model is a variant of the SEIR model considering both untested and unreported cases. The model considers reopening and assumes susceptible population will increase after the reopen.
    Contributors: Quanquan Gu <qgu@cs.ucla.edu>, Pan Xu, Jinghui Chen, Lingxiao Wang, Difan Zou, Weitong Zhang
    License: Creative Commons Attribution 4.0
    Notes: primary
    Citation: https://www.medrxiv.org/content/10.1101/2020.05.24.20111989v1
    Methods: We update our model by considering the effects of reopenings. In particular, for the states that have reopened or partially reopened, we assume there is a transition from a virtual “Quarantined/Isolated” group to the “Susceptible” group at a certain rate after the reopen date. This rate along with other model parameters are jointly estimated based on the reported data. The key feature of the SuEIR model is that it can infer the untested cases as well as unreported cases. More specifically, we treat the “Exposed” group in the SEIR model as the individuals that have already been infected and have not been tested, which are also capable of infecting the “Susceptible” group. Moreover, some of the people in the “Exposed” group can get tested and transferred to the “Infectious” group (which are reported to the public), while the rest of them will recover/die and transit to the so-called “Unreported Recovered” group (which are not reported to the public). More detailed model descriptions and projections can be found at https://covid19.uclaml.org. Reference: Difan Zou, Lingxiao Wang, Pan Xu, Jinghui Chen, Weitong Zhang and Quanquan Gu, Epidemic Model Guided Machine Learning for COVID-19 Forecasts in the United States, medRxiv:2020.05.24.20111989, 2020.
    Home: https://covid19.uclaml.org/
    Auxiliary data: Missing

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