Effective disease outbreak response requires actionable, region-specific guidance, but most modeling tools rely on detailed surveillance or strong assumptions, such as random mixing. Agent-based models (ABMs) allow us to capture key heterogeneity in contact patterns and intervention mechanisms, but linking these models with data is often computationally intractable, particularly at the larger...
Agent-based models (ABMs) have become valuable tools for understanding complex systems in biology and medicine. In order to evaluate the robustness and accuracy of the model predictions, uncertainty quantification using global sensitivity analysis should be performed. Unfortunately, most global sensitivity analyses are computational prohibitive for complex ABMs. By leveraging explicitly...
Sensitivity analysis characterizes input–output relationships for mathematical models and has been widely applied to deterministic models across many applications in the life sciences. In contrast, sensitivity analysis for stochastic models has received less attention, with most previous work focusing on well-mixed, non-spatial problems. For explicit spatiotemporal stochastic models, such as...
Agent-based models (ABMs) are increasingly used to study complex systems in biology, enabled by advances in computing and the growing availability of high-resolution data tracking individual agents. However, fitting ABMs to data remains challenging because their likelihood functions are typically intractable, making standard statistical methods such as maximum likelihood estimation or Markov...
Agent-based models (ABMs) are a natural platform to capture the complexity inherent in multiscale biological systems. However, the impact of mathematical modeling with ABMs remains limited by persistent challenges in model calibration, sensitivity analysis, and uncertainty quantification. Difficulties in integrating experimental data with models and the computational cost of simulating ABMs...
