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

Chowell, Gerardo

where t i are the time points at which the time series data are observed, and n is the number of data points available for inference. Hence, the model solution f ðt i ; b QÞ yields the best fit to the time series data y ti . However, it is important to keep in mind the underlying assumption in least squares fitting: the standard deviation of the errors (deviation of model to data) is invariant across the time series. In Matlab (The Mathworks, Inc.), two numerical optimization methods are available to solve the nonlinear least squares problem: The trust-region reflective algorithm and the Levenberg-Marquardt algorithm.