Model: RPI and UW - RPI_UW-Mob_Collision

    Name: Mobility-informed Collision Model
    Abbreviation: RPI_UW-Mob_Collision
    Owner: covid19hub
    Team name: RPI and UW
    Description: A mobility-informed simplified SIR model motivated by collision theory.
    Contributors: Yunfeng Shi (RPI) shiy2@rpi.edu, Tanooj Shah (RPI) shaht2@rpi.edu, Xuegang Ban (Univ. of Washington) banx@uw.edu
    License: Creative Commons Attribution 4.0
    Notes: primary
    Citation: https://www.medrxiv.org/content/10.1101/2020.07.25.20162016v1
    Methods: We incorporate mobility information into a simplified SIR model which has an analytical solution with only two fitting parameters. Without any social- distancing policy or geological/demographic inputs, the model works very well for the twenty hardest-hit counties in the United States. This model is quite different from other models yet provide parameters with clear physical meaning. We have shown the cumulative mobility plays the role of time in controling exponential growth of outbreak. Furthermore, there exists a critical relative mobility above which outbreak will occur. For NYC, this critical relative mobility is 30%. The critical relative mobility is ~60% for all other counties. In this forecast, we chose the ~30 heaviest hit states, while combine the rest of the states together into one superstate. This practice is necessary to obtain meaningful model parameters. The forecast is based on Eq. 3 of the citation above, fatality-reference, which is the average of the 11-day-period prior to prediction-date. The cumulative mobility is the average of three Google mobility categories: retail and recreation, transit station and work place. We have shown in our paper those three categorites behave similarly. We conducted standard error analysis on our forecast in which the uncertainty comes from both the fitting error and the variation of the reference fatality. Quantile forecast is calculated based on standard error assuming normal distribution. State-level forecast has been included for 32 states. Other states and county-level forecast will be included later. Since Sep 6, 2020, the infection rate and recovery rate were fitted up to Aug 9, 2020, while assuming the infection rate drops 15% afterwards. This is motivated by the consistent over-prediction from our model solely based on mobility. This is also reasonable considering the mutation of the virus over more than half year. Google mobility data may have been affected by a recent Sep update.
    Home: http://rpi-uw-covid-mobility.herokuapp.com/
    Auxiliary data: Missing

    Forecasts (13)

    Timezero Data Source Upload Time
    2019-12-30 (No data)
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    2020-08-03 2020-08-03-RPI_UW-Mob_Collision.csv 2020-08-21 14:20:29 EDT
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