Model: Los Alamos National Lab and Northern Arizona University - LosAlamos_NAU-CModel_SDVaxVar

    Name: Compartmental Model with Bayesian Inference
    Abbreviation: LosAlamos_NAU-CModel_SDVaxVar
    Owner: covid19hub
    Team name: Los Alamos National Lab and Northern Arizona University
    Description: We used the compartmental model analyze case data available for each State. The model consists of ordinary differential equations describing the dynamics of 40 populations (state variables).
    Contributors: Ye Chen (Northern Arizona University)<yechent@gmail.com>, Yen Ting Lin (Los Alamos National Laboratory)<yentingl@lanl.gov>, Ely F. Miller (Northern Arizona University), Jacob Neumann (Northern Arizona University), Abhishek Mallela (University of California, Davis), Richard G. Posner (Northern Arizona University)<Richard.Posner@nau.edu>, William S. Hlavacek (Los Alamos National Laboratory)<wish@lanl.gov>
    License: Creative Commons Attribution 4.0
    Notes: primary
    Citation: https://www.medrxiv.org/content/10.1101/2021.10.19.21265223v1
    Methods: We used a compartmental model consists of ordinary differential equations (ODEs) describing the dynamics of 40 populations (state variables). The model quantified the impact of non-pharmaceutical interventions (social distancing for example) on COVID-19 transmission dynamics, and captures vaccination of susceptible and recovered persons and infected non-quarantined persons without symptoms at a time-varying per capita rate. This vaccination rate is updated daily for consistency with MSA-specific daily reports of completed vaccinations. The model also captures immune responses to vaccination yielding varying degrees of protection and the consequences of breakthrough infection of vaccinated persons. Vaccine protection against transmissible infection was taken to be variant-dependent. The parameterized models provide estimates of the relative transmissibility of Alpha and Delta, as well as their takeover times in each State (i.e., the times at which the variants became dominant). Using Bayesian inference, we calibrate region-specific models daily for consistency with new reports of confirmed cases from the 50 States in US and quantify uncertainty in parameter estimates and predictions of future case reports. This online learning approach allows for early identification of new trends despite considerable variability in case reporting. Details of the model can be found in 'citation'.
    Home: https://github.com/lanl/COVID-19-Predictions
    Auxiliary data: Missing

    Forecasts (4)

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