# Differentiable Agent-based Epidemiology

arXiv: https://arxiv.org/abs/2207.09714 · HTML: https://mlsanity.com/paper/2207.09714/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 82 references that are in the map.
- First posted on arXiv 20 Jul 2022.

## Builds on (most cited first, 21 of 21)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Normalizing Flows for Probabilistic Modeling and Inference](https://mlsanity.com/paper/1912.02762/) (2019)
- [Geometric deep learning: going beyond Euclidean data](https://mlsanity.com/paper/1611.08097/) (2016)
- [A Review of the Gumbel-max Trick and its Extensions for Discrete Stochasticity in Machine Learning](https://mlsanity.com/paper/2110.01515/) (2021)
- [Simulation Intelligence: Towards a New Generation of Scientific Methods](https://mlsanity.com/paper/2112.03235/) (2021)
- [Coupling Vision and Proprioception for Navigation of Legged Robots](https://mlsanity.com/paper/2112.02094/) (2021)
- [Interpretable Sequence Learning for COVID-19 Forecasting](https://mlsanity.com/paper/2008.00646/) (2020)
- [EINNs: Epidemiologically-informed Neural Networks](https://mlsanity.com/paper/2202.10446/) (2022)
- [CAMul: Calibrated and Accurate Multi-view Time-Series Forecasting](https://mlsanity.com/paper/2109.07438/) (2021)
- [When and How to Lift the Lockdown? Global COVID-19 Scenario Analysis and Policy Assessment using Compartmental Gaussian Processes](https://mlsanity.com/paper/2005.08837/) (2020)
- [Steering a Historical Disease Forecasting Model Under a Pandemic: Case of Flu and COVID-19](https://mlsanity.com/paper/2009.11407/) (2020)
- [Segregation Dynamics with Reinforcement Learning and Agent Based Modeling](https://mlsanity.com/paper/1909.08711/) (2019)
- [A Framework for Evaluating Epidemic Forecasts](https://mlsanity.com/paper/1703.09828/) (2017)
- [Differentiable Agent-Based Simulation for Gradient-Guided Simulation-Based Optimization](https://mlsanity.com/paper/2103.12476/) (2021)
- [Data-Centric Epidemic Forecasting: A Survey](https://mlsanity.com/paper/2207.09370/) (2022)
- [Back2Future: Leveraging Backfill Dynamics for Improving Real-time Predictions in Future](https://mlsanity.com/paper/2106.04420/) (2021)
- [Google COVID-19 Search Trends Symptoms Dataset: Anonymization Process Description (version 1.0)](https://mlsanity.com/paper/2009.01265/) (2020)
- [Predicting Infectiousness for Proactive Contact Tracing](https://mlsanity.com/paper/2010.12536/) (2020)
- [Multi-Level Mesa](https://mlsanity.com/paper/1904.08315/) (2019)

## Built on by (most cited first, 21 of 21)

- [Automatic Differentiation of Programs with Discrete Randomness](https://mlsanity.com/paper/2210.08572/) (2022)
- [On the limits of agency in agent-based models](https://mlsanity.com/paper/2409.10568/) (2024)
- [Bayesian calibration of differentiable agent-based models](https://mlsanity.com/paper/2305.15340/) (2023)
- [Bayesian calibration of stochastic agent based model via random forest](https://mlsanity.com/paper/2406.19524/) (2024)
- [Toward World Models for Epidemiology](https://mlsanity.com/paper/2604.09519/) (2026)
- [Some challenges of calibrating differentiable agent-based models](https://mlsanity.com/paper/2307.01085/) (2023)
- [Improving Epidemic Analyses with Privacy-Preserving Integration of Sensitive Data](https://mlsanity.com/paper/2506.22342/) (2025)
- [Large Population Models](https://mlsanity.com/paper/2507.09901/) (2025)
- [CALYPSO: Forecasting and Analyzing MRSA Infection Patterns with Community and Healthcare Transmission Dynamics](https://mlsanity.com/paper/2508.13548/) (2025)
- [Agentic Framework for Epidemiological Modeling](https://mlsanity.com/paper/2602.00299/) (2026)
- [Towards Learning Stochastic Population Models by Gradient Descent](https://mlsanity.com/paper/2404.07049/) (2024)
- [Fully Data-driven but Interpretable Human Behavioural Modelling with Differentiable Discrete Choice Model](https://mlsanity.com/paper/2412.19403/) (2024)
- [Are LLMs Ready for Neural-integrated Mechanistic Modeling? A Benchmark and Agentic Framework](https://mlsanity.com/paper/2602.18008/) (2026)
- [Global Prediction of COVID-19 Variant Emergence Using Dynamics-Informed Graph Neural Networks](https://mlsanity.com/paper/2401.03390/) (2024)
- [Designing Dynamic Pricing for Bike-sharing Systems via Differentiable Agent-based Simulation](https://mlsanity.com/paper/2507.23344/) (2025)
- [Control of dynamical systems with neural networks](https://mlsanity.com/paper/2510.12810/) (2025)
- [A differentiable model of supply-chain shocks](https://mlsanity.com/paper/2511.05231/) (2025)
- [Ultra-fast Traffic Nowcasting and Control via Differentiable Agent-based Simulation](https://mlsanity.com/paper/2603.25068/) (2026)
- [Boundary Degree as a Node-level Feature for Epidemic Scenario Identification in Agent-based Cascade Simulations](https://mlsanity.com/paper/2606.29596/) (2026)
- [Poor Man's Agentic Modeling: Simulating Large LLM-Agent Societies on a Laptop](https://mlsanity.com/paper/2608.11215/) (2026)
- [Learning Collective Dynamics with Differentiable Gaussian Representations](https://mlsanity.com/paper/2609.28405/) (2026)

## Abstract (arXiv)

Mechanistic simulators are an indispensable tool for epidemiology to explore the behavior of complex, dynamic infections under varying conditions and navigate uncertain environments. Agent-based models (ABMs) are an increasingly popular simulation paradigm that can represent the heterogeneity of contact interactions with granular detail and agency of individual behavior. However, conventional ABM frameworks are not differentiable and present challenges in scalability; due to which it is non-trivial to connect them to auxiliary data sources. In this paper, we introduce GradABM: a scalable, differentiable design for agent-based modeling that is amenable to gradient-based learning with automatic differentiation. GradABM can quickly simulate million-size populations in few seconds on commodity hardware, integrate with deep neural networks and ingest heterogeneous data sources. This provides an array of practical benefits for calibration, forecasting, and evaluating policy interventions. We demonstrate the efficacy of GradABM via extensive experiments with real COVID-19 and influenza datasets.