TY - GEN
T1 - Customizing Spatial-Temporal Graph Mamba Networks for Pandemic Forecasting
AU - Xu, Haowei
AU - Gao, Chao
AU - Li, Xianghua
AU - Wang, Zhen
AU - Jun, Tanimoto
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
PY - 2025
Y1 - 2025
N2 - The global spread of COVID-19 has emphasized the need for accurate pandemic prediction. While previous studies used spatiotemporal and graph-structured mobility data for outbreak forecasts, these models often suffer from long training times and high computational demands, limiting their effectiveness in dynamic scenarios. Additionally, varying regional mobility patterns add complexity, making manual model adjustments difficult. This paper presents AutoGMN, an automated architecture search framework utilizing bidirectional Graph Mamba Networks. We construct a graph where nodes represent regions, with historical COVID-19 data and human mobility as edge weights. The model forecasts future case numbers, integrating transmission control strategies. To reduce manual intervention, we employ differentiable neural architecture search. Our approach, validated against benchmarks in three European countries, shows superior performance in epidemiological forecasting.
AB - The global spread of COVID-19 has emphasized the need for accurate pandemic prediction. While previous studies used spatiotemporal and graph-structured mobility data for outbreak forecasts, these models often suffer from long training times and high computational demands, limiting their effectiveness in dynamic scenarios. Additionally, varying regional mobility patterns add complexity, making manual model adjustments difficult. This paper presents AutoGMN, an automated architecture search framework utilizing bidirectional Graph Mamba Networks. We construct a graph where nodes represent regions, with historical COVID-19 data and human mobility as edge weights. The model forecasts future case numbers, integrating transmission control strategies. To reduce manual intervention, we employ differentiable neural architecture search. Our approach, validated against benchmarks in three European countries, shows superior performance in epidemiological forecasting.
KW - Mamba
KW - Neural architecture search
KW - Pandemic forecasting
KW - Spatial-temporal graph representation learning
UR - http://www.scopus.com/inward/record.url?scp=85210158858&partnerID=8YFLogxK
U2 - 10.1007/978-981-96-0116-5_19
DO - 10.1007/978-981-96-0116-5_19
M3 - 会议稿件
AN - SCOPUS:85210158858
SN - 9789819601158
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 236
EP - 242
BT - PRICAI 2024
A2 - Hadfi, Rafik
A2 - Ito, Takayuki
A2 - Anthony, Patricia
A2 - Sharma, Alok
A2 - Bai, Quan
PB - Springer Science and Business Media Deutschland GmbH
T2 - 21st Pacific Rim International Conference on Artificial Intelligence, PRICAI 2024
Y2 - 18 November 2024 through 24 November 2024
ER -