Dynamic spatiotemporal graph attention networks for cross-regional multi-disease forecasting and intervention optimization

The framework provides an integrated, interpretable, and generalizable solution for real-time epidemic prediction and equitable public-health decision-making.

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Publication Details

Journal
Frontiers in Public Health
Published
2026-02-04
DOI
https://doi.org/10.3389/fpubh.2026.1720620
Primary Topic
COVID-19 epidemiological studies
Type
article
Field-Weighted Citation Impact
0.00
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article

Dynamic spatiotemporal graph attention networks for cross-regional multi-disease forecasting and intervention optimization

Siyan Liu, Lixing Cao
Frontiers in Public Health
COVID-19 epidemiological studies
article

Dynamic spatiotemporal graph attention networks for cross-regional multi-disease forecasting and intervention optimization

Siyan Liu, Lixing Cao
article en

Abstract

The framework provides an integrated, interpretable, and generalizable solution for real-time epidemic prediction and equitable public-health decision-making.

Frontiers in Public HealthVol. 14
Xuzhou Medical College (CN), Beijing University of Chinese Medicine (CN), Second People’s Hospital of Huai’an (CN)
Good health and well-being
Openalex Percentile: Top 68%
COVID-19 epidemiological studies
DOIOpen Access PDFOpenAlex
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