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A time-series graph is a line graph with repeated measurements taken at successive intervals of time. It is also called a time series chart. To construct a time-series graph, one must look at both pieces of a paired data set. The horizontal axis is used to plot the time increments, and the vertical axis is used to plot the values of the variable that one is measuring. By using the axes in this way, each point on the graph will correspond to time and a measured quantity. The points on the graph...
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  3. Spatio-temporal Epidemic Forecasting With Graph-based Transformer.
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  3. Spatio-temporal Epidemic Forecasting With Graph-based Transformer.

Related Experiment Videos

Spatio-temporal epidemic forecasting with graph-based transformer.

Mahmoud Ezzat1, Youssef Mohamed Malek2, Tamer AbdelKader3,4

  • 1Department of Information Systems, Computer and Information Sciences, Ain Shams University, Abbasya, Cairo, 11566, Egypt. mahmoud.abdelmobdy@cis.asu.edu.eg.

International Journal of Health Geographics
|June 2, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

New spatio-temporal Graph Neural Network (GNN) models improve epidemic forecasting by integrating human mobility data. These models better capture complex temporal dynamics and spatial dependencies for more accurate public health predictions.

Keywords:
Attention mechanismCOVID-19Graph convolutional networkSpatio-temporalTransformer

Related Experiment Videos

Area of Science:

  • Epidemiology and Public Health
  • Artificial Intelligence
  • Network Science

Background:

  • Accurate epidemic forecasting is crucial for public health, as highlighted by the COVID-19 pandemic.
  • Existing spatio-temporal Graph Neural Network (GNN) models struggle with complex temporal dynamics and long-range spatial dependencies in human mobility networks.

Purpose of the Study:

  • To develop novel spatio-temporal architectures combining GNNs with Transformer-based temporal modeling.
  • To address limitations in capturing non-linear temporal dynamics and long-range spatial dependencies in epidemic forecasting.

Main Methods:

  • Introduced two architectures: a local-attention-model and a global-attention-model, integrating GNNs with Transformer temporal modeling.
  • Benchmarked against Persistence, Graph Convolutional Recurrent Network (GCRN), and Graph WaveNet using real-world datasets from Spain and Brazil.

Main Results:

  • The Linear Temporal Graph Convolutional Network (LinearTGCN) variant achieved superior performance on the Spain dataset (SMAPE 24.74%, MDA 72.52%).
  • The local-attention-model performed competitively on the Brazil dataset (RMSE 3873.63, SMAPE 83.47%).
  • Simple linear models matched or exceeded Transformer performance on structured data, while Transformers excelled on noisy/unstructured data.

Conclusions:

  • Novel spatio-temporal GNNs with Transformer-based temporal modeling offer improved epidemic forecasting.
  • Linear models can be highly effective for structured time-series data in this domain.
  • Transformer-based models demonstrate strong performance on noisy or unstructured datasets, highlighting their adaptability.