# Accurate Calibration of Agent-based Epidemiological Models with Neural Network Surrogates

arXiv: https://arxiv.org/abs/2010.06558 · HTML: https://mlsanity.com/paper/2010.06558/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Oct 2020.

## Built on by (most cited first, 9 of 9)

- [Differentiable Agent-Based Simulation for Gradient-Guided Simulation-Based Optimization](https://mlsanity.com/paper/2103.12476/) (2021)
- [Conditional diffusions for amortized neural posterior estimation](https://mlsanity.com/paper/2410.19105/) (2024)
- [Interventionally Consistent Surrogates for Agent-based Simulators](https://mlsanity.com/paper/2312.11158/) (2023)
- [Bayesian calibration of stochastic agent based model via random forest](https://mlsanity.com/paper/2406.19524/) (2024)
- [Generating Origin-Destination Matrices in Neural Spatial Interaction Models](https://mlsanity.com/paper/2410.07352/) (2024)
- [Deep Learning-based Spatially Explicit Emulation of an Agent-Based Simulator for Pandemic in a City](https://mlsanity.com/paper/2205.14396/) (2022)
- [Epidemiological Model Calibration via Graybox Bayesian Optimization](https://mlsanity.com/paper/2412.07193/) (2024)
- [Advancing calibration for stochastic agent-based models in epidemiology with Stein variational inference and Gaussian process surrogates](https://mlsanity.com/paper/2502.19550/) (2025)
- [Designing Dynamic Pricing for Bike-sharing Systems via Differentiable Agent-based Simulation](https://mlsanity.com/paper/2507.23344/) (2025)

## Abstract (arXiv)

Calibrating complex epidemiological models to observed data is a crucial step to provide both insights into the current disease dynamics, i.e. by estimating a reproductive number, as well as to provide reliable forecasts and scenario explorations. Here we present a new approach to calibrate an agent-based model -- EpiCast -- using a large set of simulation ensembles for different major metropolitan areas of the United States. In particular, we propose: a new neural network based surrogate model able to simultaneously emulate all different locations; and a novel posterior estimation that provides not only more accurate posterior estimates of all parameters but enables the joint fitting of global parameters across regions.