Epidemiology-informed Graph Neural Network for Heterogeneity-aware Epidemic Forecasting Article Swipe
Yufan Zheng
,
Wei Jiang
,
Alexander Zhou
,
Quoc Viet Hung Nguyen
,
Choujun Zhan
,
Tong Chen
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2411.17372
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2411.17372
Among various spatio-temporal prediction tasks, epidemic forecasting plays a critical role in public health management. Recent studies have demonstrated the strong potential of spatio-temporal graph neural networks (STGNNs) in extracting heterogeneous spatio-temporal patterns for epidemic forecasting. However, most of these methods bear an over-simplified assumption that two locations (e.g., cities) with similar observed features in previous time steps will develop similar infection numbers in the future. In fact, for any epidemic disease, there exists strong heterogeneity of its intrinsic evolution mechanisms across geolocation and time, which can eventually lead to diverged infection numbers in two ``similar'' locations. However, such mechanistic heterogeneity is non-trivial to be captured due to the existence of numerous influencing factors like medical resource accessibility, virus mutations, mobility patterns, etc., most of which are spatio-temporal yet unreachable or even unobservable. To address this challenge, we propose a Heterogeneous Epidemic-Aware Transmission Graph Neural Network (HeatGNN), a novel epidemic forecasting framework. By binding the epidemiology mechanistic model into a GNN, HeatGNN learns epidemiology-informed location embeddings of different locations that reflect their own transmission mechanisms over time. With the time-varying mechanistic affinity graphs computed with the epidemiology-informed location embeddings, a heterogeneous transmission graph network is designed to encode the mechanistic heterogeneity among locations, providing additional predictive signals to facilitate accurate forecasting. Experiments on three benchmark datasets have revealed that HeatGNN outperforms various strong baselines. Moreover, our efficiency analysis verifies the real-world practicality of HeatGNN on datasets of different sizes.
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- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2411.17372
- https://arxiv.org/pdf/2411.17372
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
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https://openalex.org/W4405026121Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2411.17372Digital Object Identifier
- Title
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Epidemiology-informed Graph Neural Network for Heterogeneity-aware Epidemic ForecastingWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
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2024Year of publication
- Publication date
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2024-11-26Full publication date if available
- Authors
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Yufan Zheng, Wei Jiang, Alexander Zhou, Quoc Viet Hung Nguyen, Choujun Zhan, Tong ChenList of authors in order
- Landing page
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https://arxiv.org/abs/2411.17372Publisher landing page
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https://arxiv.org/pdf/2411.17372Direct link to full text PDF
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YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2411.17372Direct OA link when available
- Concepts
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Epidemiology, Computer science, Graph, Artificial neural network, Data science, Artificial intelligence, Medicine, Theoretical computer science, Internal medicineTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.epidemiology | 155 |
| abstract_inverted_index.forecasting. | 35, 210 |
| abstract_inverted_index.practicality | 231 |
| abstract_inverted_index.time-varying | 179 |
| abstract_inverted_index.transmission | 173, 191 |
| abstract_inverted_index.Heterogeneous | 140 |
| abstract_inverted_index.heterogeneity | 75, 100, 200 |
| abstract_inverted_index.heterogeneous | 30, 190 |
| abstract_inverted_index.unobservable. | 132 |
| abstract_inverted_index.Epidemic-Aware | 141 |
| abstract_inverted_index.accessibility, | 117 |
| abstract_inverted_index.over-simplified | 43 |
| abstract_inverted_index.spatio-temporal | 2, 23, 31, 127 |
| abstract_inverted_index.epidemiology-informed | 163, 186 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 6 |
| citation_normalized_percentile |