An integrated framework for building trustworthy data-driven epidemiological models: Application to the COVID-19 outbreak in New York City
Fig 1
A general framework for building a trustworthy data-driven epidemiological model—An overview of the main contribution.
In this work, we propose a general framework for building a trustworthy data-driven epidemiological model, which constructs a workflow to integrate data acquisition and event timeline, model development, identifiability analysis, sensitivity analysis, model calibration, model robustness analysis, and projection with uncertainties and scenarios. We first introduce a modified SEIR model that accommodates the pandemic data in New York City. Secondly, we study the structural identifiability, practical identifiability, and sensitivity to examine the relationship between the model’s data and parameters. We then calibrate the identifiable model parameters using simulated annealing and MCMC simulation. Model robustness is then checked to study how the model behaves under random perturbations. In addition, we demonstrate the model’s projective capabilities with uncertainties. Finally, reopening scenarios are investigated as a reference for policymakers.