Metapopulation Graph Neural Networks: Deep Metapopulation Epidemic Modeling with Human Mobility

• URL: http://arxiv.org/abs/2306.14857v2
• Date: Tue, 27 Jun 2023 13:44:36 GMT
• Title: Metapopulation Graph Neural Networks: Deep Metapopulation Epidemic Modeling with Human Mobility
• Authors: Qi Cao, Renhe Jiang, Chuang Yang, Zipei Fan, Xuan Song, Ryosuke Shibasaki
• Abstract summary: 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.
• Score: 14.587916407752719
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Epidemic prediction is a fundamental task for epidemic control and prevention. Many mechanistic models and deep learning models are built for this task. However, most mechanistic models have difficulty estimating the time/region-varying epidemiological parameters, while most deep learning models lack the guidance of epidemiological domain knowledge and interpretability of prediction results. In this study, we propose a novel hybrid model called MepoGNN for multi-step multi-region epidemic forecasting by incorporating Graph Neural Networks (GNNs) and graph learning mechanisms into Metapopulation SIR model. Our model can not only predict the number of confirmed cases but also explicitly learn the epidemiological parameters and the underlying epidemic propagation graph from heterogeneous data in an end-to-end manner. The multi-source epidemic-related data and mobility data of Japan are collected and processed to form the dataset for experiments. The experimental results demonstrate our model outperforms the existing mechanistic models and deep learning models by a large margin. Furthermore, the analysis on the learned parameters illustrate the high reliability and interpretability of our model and helps better understanding of epidemic spread. In addition, a mobility generation method is presented to address the issue of unavailable mobility data, and the experimental results demonstrate effectiveness of the generated mobility data as an input to our model.

Related papers

• A Review of Graph Neural Networks in Epidemic Modeling [14.28921518883576]
  Since the onset of the COVID-19 pandemic, there has been a growing interest in studying epidemiological models. Graph Neural Networks have emerged as a progressively popular tool in epidemic research.
  arXiv  Detail & Related papers  (2024-03-28T21:54:48Z)
• MedDiffusion: Boosting Health Risk Prediction via Diffusion-based Data Augmentation [58.93221876843639]
  This paper introduces a novel, end-to-end diffusion-based risk prediction model, named MedDiffusion. It enhances risk prediction performance by creating synthetic patient data during training to enlarge sample space. It discerns hidden relationships between patient visits using a step-wise attention mechanism, enabling the model to automatically retain the most vital information for generating high-quality data.
  arXiv  Detail & Related papers  (2023-10-04T01:36:30Z)
• 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)
• 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)
• 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)
• Unifying Epidemic Models with Mixtures [28.771032745045428]
  The COVID-19 pandemic has emphasized the need for a robust understanding of epidemic models. Here, we introduce a simple mixture-based model which bridges the two approaches. Although the model is non-mechanistic, we show that it arises as the natural outcome of a process based on a networked SIR framework.
  arXiv  Detail & Related papers  (2022-01-07T19:42:05Z)
• Discrepancies in Epidemiological Modeling of Aggregated Heterogeneous Data [1.433758865948252]
  We show that state-of-the-art models for estimating epidemiological parameters, e.g.transmission rates, can be inappropriate when faced with complex systems. We generate three complex outbreak scenarios by combining incidence curves from multiple epidemics. We evaluate two data-generating models within this Bayesian inference framework.
  arXiv  Detail & Related papers  (2021-06-20T03:41:19Z)
• 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)
• OutbreakFlow: Model-based Bayesian inference of disease outbreak dynamics with invertible neural networks and its application to the COVID-19 pandemics in Germany [0.19791587637442667]
  We present a novel combination of epidemiological modeling with specialized neural networks. We are able to obtain reliable probabilistic estimates for important disease characteristics, such as generation time, fraction of undetected infections, likelihood of transmission before symptom onset, and reporting delays using a very moderate amount of real-world observations.
  arXiv  Detail & Related papers  (2020-10-01T11:01:49Z)
• 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)

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.