Fitting dynamic models to epidemic outbreaks with quantified uncertainty: A primer for parameter uncertainty, identifiability, and forecasts

Chowell, Gerardo

We rely on the general bootstrap method (Efron & Tibshirani, 1994) and describe a parametric bootstrapping approach which we have previously used in several publications to quantify parameter uncertainty and construct confidence intervals in mathematical modeling studies (e.g., (Chowell et al., 2006a (Chowell et al., , 2006b ). In this method, multiple observations are repeatedly sampled from the best-fit model in order to quantify parameter uncertainty by assuming that the time series follow a Poisson distribution centered on the mean at the time points t i . However, it is also possible to consider overdispersion in the data (see next section). This computational method requires generating simulated data from f ðt i ; b QÞ, which is the best fit of the model to the data. The step-by-step algorithm to quantify parameter uncertainty follows (Fig. 5 ):