MaMiH: a new data assimilation framework based on macro-micro hierarchical simulation model

Yuqing Ma; Xu Xie; Hailiang Chen

doi:10.1117/12.3004827

9 October 2023 MaMiH: a new data assimilation framework based on macro-micro hierarchical simulation model

Yuqing Ma, Xu Xie, Hailiang Chen

Proceedings Volume 12791, Third International Conference on Advanced Algorithms and Neural Networks (AANN 2023); 127912L (2023) https://doi.org/10.1117/12.3004827
Event: Third International Conference on Advanced Algorithms and Neural Networks (AANN 2023), 2023, Qingdao, SD, China

Abstract

The widespread transmission of infectious diseases is a significant threat to human safety and development, and predicting epidemic trends is a pressing concern for scholars. Data assimilation methods can improve prediction accuracy by incorporating real observation data into system models. However, current data assimilation in epidemic models is limited to macro models, which fail to reflect individual heterogeneity realistically. Conversely, micro models have a high-dimensional parameter space, and the computational complexity of using data assimilation to adjust the state of each individual is high. To address this issue, this paper proposes a new data assimilation framework based on a macro-micro hierarchical simulation model called MaMiH. The MaMiH framework integrates the SEIR model as the macro model, reducing the computational complexity of the data assimilation process, and a complex network model as the micro model to simulate the infection status of each node within the network, thus reflecting individual heterogeneity. This paper verifies the effectiveness of the framework using the early stages of the COVID-19 pandemic as a research background.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yuqing Ma, Xu Xie, and Hailiang Chen "MaMiH: a new data assimilation framework based on macro-micro hierarchical simulation model", Proc. SPIE 12791, Third International Conference on Advanced Algorithms and Neural Networks (AANN 2023), 127912L (9 October 2023); https://doi.org/10.1117/12.3004827

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