Model: Discrete Dynamical Systems - DDS-NBDS

    Name: Negative Binomial Dynamical System
    Abbreviation: DDS-NBDS
    Owner: covid19hub
    Team name: Discrete Dynamical Systems
    Description: Jointly modeling daily deaths and cases using a negative binomial distribution based nonparametric Bayesian generalized linear dynamical system.
    Contributors: Rahi Kalantari <rkalantari@utexas.edu>, Mingyuan Zhou <mingyuan.zhou@mccombs.utexas.edu>
    License: MIT
    Notes: primary
    Citation: https://dds-covid19.github.io/
    Methods: We introduce negative binomial linear dynamical system (NBDS) to jointly model daily deaths and cases reported at all 50 US States and D.C. NBDS is a nonparametric Bayesian hierarchical model built on the graph gamma process (GGP). Generated from the GGP is an infinite dimensional directed sparse random graph, which is constructed by taking the OR operation of countably infinite binary adjacency matrices that share the same set of countably infinite nodes. Each of these adjacency matrices is associated with a weight to indicate its overall activation strength, and places a finite number of edges between a finite subset of nodes belonging to the same community. We use the generated random graph, whose number of nonzero-degree nodes is finite, to define both the sparsity patterns and dimensions of the latent state transition matrices of generalized linear dynamical systems. We show how the formed overlapping communities in the latent space can provide interpretable latent transition patterns of the COVID-19 time series, providing good predictive performance and uncertainty estimation.
    Home: https://dds-covid19.github.io/
    Auxiliary data: https://github.com/dds-covid19/dds-covid19.github.io

    Forecasts (53)

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