  A susceptible-exposed-infected-removed (SEIR) model assumes
spatial-/time-varying coefficients to model the effect of non-pharmaceutical
interventions (NPIs) on the regional and temporal distribution of COVID-19
disease epidemics. A significant challenge in using such model is their fast
and accurate calibration to observed data from geo-referenced hospitalized
data, i.e., efficient estimation of the spatial-/time-varying parameters. In
this work, a new calibration framework is proposed towards optimizing the
spatial-/time-varying parameters of the SEIR model. We also devise a method for
combing the overlapping sliding window technique (OSW) with a genetic algorithm
(GA) calibration routine to automatically search the segmented parameter space.
Parallelized GA is used to reduce the computational burden. Our framework
abstracts the implementation complexity of the method away from the user. It
provides high-level APIs for setting up a customized calibration system and
consuming the optimized values of parameters. We evaluated the application of
our method on the calibration of a spatial age-structured microsimulation model
using a single objective function that comprises observed COVID-19-related ICU
demand. The results reflect the effectiveness of the proposed method towards
estimating the parameters in a changing environment.
