A Color Mapping Automatic Tuning Framework for Complex Simulation Datasets

Wang, Xiaohua; Cao, Yi; Wang, Huawei; Ai, Zhiwei; Zhang, Zhe; Wu, Fukun

doi:10.3724/sp.j.1089.2022.18946

Cited by 1 publication

(2 citation statements)

References 14 publications

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“…Since COVID-19 has an incubation period, the SEIR model not only considers the incubation period, but also takes exposed people as one of the simulated populations, satisfying the transmission characteristics of COVID-19. Therefore, most COVID-19 epidemic models use the SEIR model [10]. According to the degree of infection and the possibility of transmission of infection, the SEIR model divides the population into four mutually exclusive sub-populations called compartments: susceptible compartment S, exposed compartment E, infected compartment I and removed compartment R [11].…”

Section: Seir Modelmentioning

confidence: 99%

“…However, this combination only treats the macro model and micro model as two independently evolving models, with no interaction between them, and cannot simultaneously satisfy the requirements of individual heterogeneity and computational efficiency. Wang et al [10] combined the SEIR model with the complex network model. They used macro information to correct micro predictions, thereby reducing the computational burden of the micro model.…”

Section: Introductionmentioning

confidence: 99%

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

Ma,

Xie,

Chen

2023

Third International Conference on Advanced Algorithms and Neural Networks (AANN 2023)

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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.

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Section: Seir Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%