WITHDRAWN Automatic Calibration of Agent-Based Models using Recurrent Neural Networks

Music City Center 

This study presents a deep learning framework for calibrating Agent-Based Models (ABMs), focusing on the Susceptible-Infected-Recovered (SIR) model. By leveraging Convolutional Neural Networks (CNNs) for pattern extraction and Recurrent Neural Networks (RNNs) for temporal dependencies, the approach enhances parameter estimation accuracy and efficiency. A synthetic dataset generated using epiworldR enabled model training, with RNNs achieving lower Mean Absolute Errors (MAEs).

To support real-world applications, we developed epiworldRcalibrate, an R package for real-time SIR parameter estimation and epidemic visualization. Validated on 10,000 simulated datasets, the framework proved robust and adaptable. This method offers a scalable solution for real-time epidemiological modeling, improving decision-making in public health and beyond.

Parameter Calibration
Agent-Based Models (ABMs)
Convolutional Neural Networks (CNNs)
Recurrent Neural Networks (RNNs)
Susceptible-Infected-Recovered (SIR) Model


Parameter Calibration

Recurrent Neural Networks (RNNs) 

Section on Statistical Computing