Differentiable Agent-based Epidemiology
- URL: http://arxiv.org/abs/2207.09714v2
- Date: Sun, 21 May 2023 20:59:46 GMT
- Title: Differentiable Agent-based Epidemiology
- Authors: Ayush Chopra, Alexander Rodr\'iguez, Jayakumar Subramanian, Arnau
Quera-Bofarull, Balaji Krishnamurthy, B. Aditya Prakash, Ramesh Raskar
- Abstract summary: 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.
- Score: 71.81552021144589
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
Related papers
- MVKTrans: Multi-View Knowledge Transfer for Robust Multiomics Classification [14.533025681231294]
We propose the multi-view knowledge transfer learning framework, which transfers intra- and inter-omics knowledge in an adaptive manner.
Specifically, we design a graph contrastive module that is trained on unlabeled data to effectively learn and transfer the underlying intra-omics patterns to the supervised task.
In light of the varying discriminative capacities of modalities across different diseases and/or samples, we introduce an adaptive and bi-directional cross-omics distillation module.
arXiv Detail & Related papers (2024-11-13T15:45:46Z) - Seeing Unseen: Discover Novel Biomedical Concepts via
Geometry-Constrained Probabilistic Modeling [53.7117640028211]
We present a geometry-constrained probabilistic modeling treatment to resolve the identified issues.
We incorporate a suite of critical geometric properties to impose proper constraints on the layout of constructed embedding space.
A spectral graph-theoretic method is devised to estimate the number of potential novel classes.
arXiv Detail & Related papers (2024-03-02T00:56:05Z) - The effect of data augmentation and 3D-CNN depth on Alzheimer's Disease
detection [51.697248252191265]
This work summarizes and strictly observes best practices regarding data handling, experimental design, and model evaluation.
We focus on Alzheimer's Disease (AD) detection, which serves as a paradigmatic example of challenging problem in healthcare.
Within this framework, we train predictive 15 models, considering three different data augmentation strategies and five distinct 3D CNN architectures.
arXiv Detail & Related papers (2023-09-13T10:40:41Z) - INTAGS: Interactive Agent-Guided Simulation [4.04638613278729]
In many applications involving multi-agent system (MAS), it is imperative to test an experimental (Exp) autonomous agent in a high-fidelity simulator prior to its deployment to production.
We propose a metric to distinguish between real and synthetic multi-agent systems, which is evaluated through the live interaction between the Exp and BG agents.
We show that using INTAGS to calibrate the simulator can generate more realistic market data compared to the state-of-the-art conditional Wasserstein Generative Adversarial Network approach.
arXiv Detail & Related papers (2023-09-04T19:56:18Z) - Rethinking Mitosis Detection: Towards Diverse Data and Feature
Representation [30.882319057927052]
We propose a novel generalizable framework (MitDet) for mitosis detection.
Our proposed model outperforms all the SOTA approaches in several popular mitosis detection datasets.
arXiv Detail & Related papers (2023-07-12T03:33:11Z) - Heterogeneous Domain Adaptation and Equipment Matching: DANN-based
Alignment with Cyclic Supervision (DBACS) [3.4519649635864584]
This work introduces the Domain Adaptation Neural Network with Cyclic Supervision (DBACS) approach.
DBACS addresses the issue of model generalization through domain adaptation, specifically for heterogeneous data.
This work also includes subspace alignment and a multi-view learning that deals with heterogeneous representations.
arXiv Detail & Related papers (2023-01-03T10:56:25Z) - Mixed Effects Neural ODE: A Variational Approximation for Analyzing the
Dynamics of Panel Data [50.23363975709122]
We propose a probabilistic model called ME-NODE to incorporate (fixed + random) mixed effects for analyzing panel data.
We show that our model can be derived using smooth approximations of SDEs provided by the Wong-Zakai theorem.
We then derive Evidence Based Lower Bounds for ME-NODE, and develop (efficient) training algorithms.
arXiv Detail & Related papers (2022-02-18T22:41:51Z) - DeepABM: Scalable, efficient and differentiable agent-based simulations
via graph neural networks [18.50340156403979]
We introduce DeepABM, a framework for agent-based modeling that leverages geometric message passing of graph neural networks for simulating action and interactions over large agent populations.
Specifically, DeepABM-COVID can model 200 million interactions (over 100,000 agents across 180 time-steps) in 90 seconds.
We discuss results from one research study to evaluate the impact of delaying the second dose of the COVID-19 vaccine in collaboration with clinical and public health experts.
arXiv Detail & Related papers (2021-10-09T00:46:13Z) - Select-ProtoNet: Learning to Select for Few-Shot Disease Subtype
Prediction [55.94378672172967]
We focus on few-shot disease subtype prediction problem, identifying subgroups of similar patients.
We introduce meta learning techniques to develop a new model, which can extract the common experience or knowledge from interrelated clinical tasks.
Our new model is built upon a carefully designed meta-learner, called Prototypical Network, that is a simple yet effective meta learning machine for few-shot image classification.
arXiv Detail & Related papers (2020-09-02T02:50:30Z) - Modeling Shared Responses in Neuroimaging Studies through MultiView ICA [94.31804763196116]
Group studies involving large cohorts of subjects are important to draw general conclusions about brain functional organization.
We propose a novel MultiView Independent Component Analysis model for group studies, where data from each subject are modeled as a linear combination of shared independent sources plus noise.
We demonstrate the usefulness of our approach first on fMRI data, where our model demonstrates improved sensitivity in identifying common sources among subjects.
arXiv Detail & Related papers (2020-06-11T17:29:53Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.