Spatial Model Selection and Uncertainty Quantification: Comparing Continuous and Discrete Wound Healing Models
All data-driven modeling tasks (e.g., parameter estimation, uncertainty quantification, and data forecasting) require the selection of a mathematical model. An overlooked aspect of model selection is modality; for example, there are no guidelines on when to use a partial differential equation (PDE) model or an agent-based model (ABM) for spatial processes. To address this, we created a model selection pipeline that uses approximate Bayesian computations to perform parameter estimation, uncertainty quantification, and model selection (using both information criteria and out-of-sample forecasting). Applying the pipeline to artificial datasets (generated from ABMs) reveals that while both modalities yield comparable parameter estimation performance, the ABM estimates exhibit higher uncertainty, and the PDE models compute more than 1,000$\times$ faster. Surprisingly, the mean-field PDE is often selected over the true generative ABM model using both information criteria and data forecasting. Applying the pipeline to public wound healing data indicates that a PDE model with cell pulling and a time delay is the most appropriate model for this data, however, this model has high levels of parametric uncertainty. This methodology establishes a preliminary framework for selecting the appropriate modeling modality for spatial biological data.