Selected article for: "epidemic growth and model estimate"
Author: Chowell, Gerardo
Title: Fitting dynamic models to epidemic outbreaks with quantified uncertainty: A primer for parameter uncertainty, identifiability, and forecasts
  • Document date: 2017_8_12
    • ID: 3aa8wgr0_104
    Snippet: For simplicity, we only estimate one parameter from the time series data of the early epidemic growth phase: the transmission rate, b. We can fit the SEIR to the first 16e20 days of the influenza pandemic in San Francsico via least-square fitting using the Matlab built-in function lsqcurvefit.m. The initial number of cases I(0) is fixed according to the first observation day in the data (i.e., C(0) ¼ 4). For instance, fitting the model to the fi.....
    Document: For simplicity, we only estimate one parameter from the time series data of the early epidemic growth phase: the transmission rate, b. We can fit the SEIR to the first 16e20 days of the influenza pandemic in San Francsico via least-square fitting using the Matlab built-in function lsqcurvefit.m. The initial number of cases I(0) is fixed according to the first observation day in the data (i.e., C(0) ¼ 4). For instance, fitting the model to the first 16 epidemic days, we estimate the transmission rate at: b ¼ 1:1 ð95% CI:1:1; 1:2Þ

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