Epi$^2$-Net: Advancing Epidemic Dynamics Forecasting with Physics-Inspired Neural Networks

• URL: http://arxiv.org/abs/2508.02049v1
• Date: Mon, 04 Aug 2025 04:32:18 GMT
• Title: Epi$^2$-Net: Advancing Epidemic Dynamics Forecasting with Physics-Inspired Neural Networks
• Authors: Rui Sun, Chenghua Gong, Tianjun Gu, Yuhao Zheng, Jie Ding, Juyuan Zhang, Liming Pan, Linyuan Lü,
• Abstract summary: Epi$2$-Net is an Epidemic Forecasting Framework built upon Physics-Inspired Neural Networks.<n>Epi$2$-Net outperforms state-of-the-art methods in epidemic forecasting.
• Score: 9.990138127942288
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Advancing epidemic dynamics forecasting is vital for targeted interventions and safeguarding public health. Current approaches mainly fall into two categories: mechanism-based and data-driven models. Mechanism-based models are constrained by predefined compartmental structures and oversimplified system assumptions, limiting their ability to model complex real-world dynamics, while data-driven models focus solely on intrinsic data dependencies without physical or epidemiological constraints, risking biased or misleading representations. Although recent studies have attempted to integrate epidemiological knowledge into neural architectures, most of them fail to reconcile explicit physical priors with neural representations. To overcome these obstacles, we introduce Epi$^2$-Net, a Epidemic Forecasting Framework built upon Physics-Inspired Neural Networks. Specifically, we propose reconceptualizing epidemic transmission from the physical transport perspective, introducing the concept of neural epidemic transport. Further, we present a physic-inspired deep learning framework, and integrate physical constraints with neural modules to model spatio-temporal patterns of epidemic dynamics. Experiments on real-world datasets have demonstrated that Epi$^2$-Net outperforms state-of-the-art methods in epidemic forecasting, providing a promising solution for future epidemic containment. The code is available at: https://anonymous.4open.science/r/Epi-2-Net-48CE.

Related papers

• Advancing Opinion Dynamics Modeling with Neural Diffusion-Convection-Reaction Equation [13.884804908187391]
  We present the OPINN, a physics-informed neural framework for opinion dynamics modeling.<n>Building upon the Neural ODEs, we define the neural opinion dynamics to coordinate neural networks with physical priors.<n> OPINN achieves state-of-the-art performance in opinion evolution forecasting.
  arXiv  Detail & Related papers  (2026-02-05T07:41:19Z)
• Langevin Flows for Modeling Neural Latent Dynamics [81.81271685018284]
  We introduce LangevinFlow, a sequential Variational Auto-Encoder where the time evolution of latent variables is governed by the underdamped Langevin equation.<n>Our approach incorporates physical priors -- such as inertia, damping, a learned potential function, and forces -- to represent both autonomous and non-autonomous processes in neural systems.<n>Our method outperforms state-of-the-art baselines on synthetic neural populations generated by a Lorenz attractor.
  arXiv  Detail & Related papers  (2025-07-15T17:57:48Z)
• Understanding Malware Propagation Dynamics through Scientific Machine Learning [1.393499936476792]
  Using data from the Code Red worm outbreak, we show that the UDE approach substantially reduces prediction error compared to both traditional and neural baselines by 44%.<n>We introduce a symbolic recovery method that transforms the learned neural feedback into explicit mathematical expressions.<n>Our results demonstrate that hybrid physics-informed models can outperform both purely analytical and purely neural approaches.
  arXiv  Detail & Related papers  (2025-07-09T04:49:23Z)
• NOBLE -- Neural Operator with Biologically-informed Latent Embeddings to Capture Experimental Variability in Biological Neuron Models [68.89389652724378]
  NOBLE is a neural operator framework that learns a mapping from a continuous frequency-modulated embedding of interpretable neuron features to the somatic voltage response induced by current injection.<n>It predicts distributions of neural dynamics accounting for the intrinsic experimental variability.<n>NOBLE is the first scaled-up deep learning framework validated on real experimental data.
  arXiv  Detail & Related papers  (2025-06-05T01:01:18Z)
• Interpretable Spatio-Temporal Embedding for Brain Structural-Effective Network with Ordinary Differential Equation [56.34634121544929]
  In this study, we first construct the brain-effective network via the dynamic causal model. We then introduce an interpretable graph learning framework termed Spatio-Temporal Embedding ODE (STE-ODE) This framework incorporates specifically designed directed node embedding layers, aiming at capturing the dynamic interplay between structural and effective networks.
  arXiv  Detail & Related papers  (2024-05-21T20:37:07Z)
• Metapopulation Graph Neural Networks: Deep Metapopulation Epidemic Modeling with Human Mobility [14.587916407752719]
  We propose a novel hybrid model called MepoGNN for multi-step multi-region epidemic forecasting. Our model can not only predict the number of confirmed cases but also explicitly learn the epidemiological parameters.
  arXiv  Detail & Related papers  (2023-06-26T17:09:43Z)
• Approaching epidemiological dynamics of COVID-19 with physics-informed neural networks [23.95944607153291]
  A physics-informed neural network (PINN) embedded with the susceptible-infected-removed (SIR) model is devised to understand the temporal evolution dynamics of infectious diseases. The method is applied to COVID-19 data reported for Germany and shows that it can accurately identify and predict virus spread trends.
  arXiv  Detail & Related papers  (2023-02-17T10:36:58Z)
• Data-Centric Epidemic Forecasting: A Survey [56.99209141838794]
  This survey delves into various data-driven methodological and practical advancements. We enumerate the large number of epidemiological datasets and novel data streams that are relevant to epidemic forecasting. We also discuss experiences and challenges that arise in real-world deployment of these forecasting systems.
  arXiv  Detail & Related papers  (2022-07-19T16:15:11Z)
• Bayesian Physics-Informed Neural Networks for real-world nonlinear dynamical systems [0.0]
  We integrate data, physics, and uncertainties by combining neural networks, physics-informed modeling, and Bayesian inference. Our study reveals the inherent advantages and disadvantages of Neural Networks, Bayesian Inference, and a combination of both. We anticipate that the underlying concepts and trends generalize to more complex disease conditions.
  arXiv  Detail & Related papers  (2022-05-12T19:04:31Z)
• EINNs: Epidemiologically-Informed Neural Networks [75.34199997857341]
  We introduce a new class of physics-informed neural networks-EINN-crafted for epidemic forecasting. We investigate how to leverage both the theoretical flexibility provided by mechanistic models as well as the data-driven expressability afforded by AI models.
  arXiv  Detail & Related papers  (2022-02-21T18:59:03Z)
• An Optimal Control Approach to Learning in SIDARTHE Epidemic model [67.22168759751541]
  We propose a general approach for learning time-variant parameters of dynamic compartmental models from epidemic data. We forecast the epidemic evolution in Italy and France.
  arXiv  Detail & Related papers  (2020-10-28T10:58:59Z)

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.