TrendGNN: Towards Understanding of Epidemics, Beliefs, and Behaviors

• URL: http://arxiv.org/abs/2512.00421v1
• Date: Sat, 29 Nov 2025 09:53:59 GMT
• Title: TrendGNN: Towards Understanding of Epidemics, Beliefs, and Behaviors
• Authors: Mulin Tian, Ajitesh Srivastava,
• Abstract summary: Epidemic outcomes have a complex interplay with human behavior and beliefs.<n>To better understand the mechanisms and predict the impact of interventions, we need the ability to forecast signals associated with beliefs and behaviors in an interpretable manner.<n>We propose a graph-based forecasting framework that first constructs a graph of interrelated signals based on trend similarity, and then applies graph neural networks (GNNs) for prediction.
• Score: 9.74230427221556
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Epidemic outcomes have a complex interplay with human behavior and beliefs. Most of the forecasting literature has focused on the task of predicting epidemic signals using simple mechanistic models or black-box models, such as deep transformers, that ingest all available signals without offering interpretability. However, to better understand the mechanisms and predict the impact of interventions, we need the ability to forecast signals associated with beliefs and behaviors in an interpretable manner. In this work, we propose a graph-based forecasting framework that first constructs a graph of interrelated signals based on trend similarity, and then applies graph neural networks (GNNs) for prediction. This approach enables interpretable analysis by revealing which signals are more predictable and which relationships contribute most to forecasting accuracy. We believe our method provides early steps towards a framework for interpretable modeling in domains with multiple potentially interdependent signals, with implications for building future simulation models that integrate behavior, beliefs, and observations.

Related papers

• Beyond Curve Fitting: Neuro-Symbolic Agents for Context-Aware Epidemic Forecasting [6.67449278650973]
  We propose a two-agent framework to predict hand, foot and mouth disease.<n>A "event interpreter" processes signals including school schedules, meteorological summaries, and reports.<n>A neuro-symbolic core then combines this with historical case counts to produce calibrated probabilistic forecasts.
  arXiv  Detail & Related papers  (2025-11-28T15:29:26Z)
• Towards Generalizable and Interpretable Motion Prediction: A Deep Variational Bayes Approach [54.429396802848224]
  This paper proposes an interpretable generative model for motion prediction with robust generalizability to out-of-distribution cases. For interpretability, the model achieves the target-driven motion prediction by estimating the spatial distribution of long-term destinations. Experiments on motion prediction datasets validate that the fitted model can be interpretable and generalizable.
  arXiv  Detail & Related papers  (2024-03-10T04:16:04Z)
• Bayesian Networks for the robust and unbiased prediction of depression and its symptoms utilizing speech and multimodal data [65.28160163774274]
  We apply a Bayesian framework to capture the relationships between depression, depression symptoms, and features derived from speech, facial expression and cognitive game data collected at thymia.
  arXiv  Detail & Related papers  (2022-11-09T14:48:13Z)
• Conditioned Human Trajectory Prediction using Iterative Attention Blocks [70.36888514074022]
  We present a simple yet effective pedestrian trajectory prediction model aimed at pedestrians positions prediction in urban-like environments. Our model is a neural-based architecture that can run several layers of attention blocks and transformers in an iterative sequential fashion. We show that without explicit introduction of social masks, dynamical models, social pooling layers, or complicated graph-like structures, it is possible to produce on par results with SoTA models.
  arXiv  Detail & Related papers  (2022-06-29T07:49:48Z)
• Why Did This Model Forecast This Future? Closed-Form Temporal Saliency Towards Causal Explanations of Probabilistic Forecasts [20.442850522575213]
  We build upon a general definition of information-theoretic saliency grounded in human perception. We propose to express the saliency of an observed window in terms of the differential entropy of the resulting predicted future distribution. We empirically demonstrate how our framework can recover salient observed windows from head pose features for the sample task of speaking-turn forecasting.
  arXiv  Detail & Related papers  (2022-06-01T18:00:04Z)
• GroupNet: Multiscale Hypergraph Neural Networks for Trajectory Prediction with Relational Reasoning [37.64048110462638]
  GroupNet is a multiscale hypergraph neural network that captures both pair-wise and group-wise interactions. We apply GroupNet into both CVAE-based prediction system and previous state-of-the-art prediction systems. We show that with GroupNet, the CVAE-based prediction system outperforms state-of-the-art methods.
  arXiv  Detail & Related papers  (2022-04-19T09:36:20Z)
• RAIN: Reinforced Hybrid Attention Inference Network for Motion Forecasting [34.54878390622877]
  We propose a generic motion forecasting framework with dynamic key information selection and ranking based on a hybrid attention mechanism. The framework is instantiated to handle multi-agent trajectory prediction and human motion forecasting tasks. We validate the framework on both synthetic simulations and motion forecasting benchmarks in different domains.
  arXiv  Detail & Related papers  (2021-08-03T06:30:30Z)
• Deep Co-Attention Network for Multi-View Subspace Learning [73.3450258002607]
  We propose a deep co-attention network for multi-view subspace learning. It aims to extract both the common information and the complementary information in an adversarial setting. In particular, it uses a novel cross reconstruction loss and leverages the label information to guide the construction of the latent representation.
  arXiv  Detail & Related papers  (2021-02-15T18:46:44Z)
• Causal Discovery in Physical Systems from Videos [123.79211190669821]
  Causal discovery is at the core of human cognition. We consider the task of causal discovery from videos in an end-to-end fashion without supervision on the ground-truth graph structure.
  arXiv  Detail & Related papers  (2020-07-01T17:29:57Z)
• Learning to Extrapolate Knowledge: Transductive Few-shot Out-of-Graph Link Prediction [69.1473775184952]
  We introduce a realistic problem of few-shot out-of-graph link prediction. We tackle this problem with a novel transductive meta-learning framework. We validate our model on multiple benchmark datasets for knowledge graph completion and drug-drug interaction prediction.
  arXiv  Detail & Related papers  (2020-06-11T17:42:46Z)
• EvolveGraph: Multi-Agent Trajectory Prediction with Dynamic Relational Reasoning [41.42230144157259]
  We propose a generic trajectory forecasting framework with explicit relational structure recognition and prediction via latent interaction graphs. Considering the uncertainty of future behaviors, the model is designed to provide multi-modal prediction hypotheses. We introduce a double-stage training pipeline which not only improves training efficiency and accelerates convergence, but also enhances model performance.
  arXiv  Detail & Related papers  (2020-03-31T02:49:23Z)

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.