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Prediction method of infectious disease transmission based on graph signal processing |
Wen-juan LI1( ),Hong-gao DENG1,*( ),Mou MA1,Jun-zheng JIANG1,2 |
1. School of Information and Communication, Guilin University of Electronic Technology, Guilin 541004, China 2. State and Local Joint Engineering Research Center for Satellite Navigation and Location Service, Guilin University of Electronic Technology, Guilin 541004, China |
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Abstract The existing spread predicting models of infectious diseases have not sufficiently considered the intrinsic correlation of the data. To solve this problem, graph polynomial-vector autoregressive (GP-VAR) model was used to predict the spread of infectious diseases, and a new optimization method for estimating model parameters was proposed. The regions where infectious diseases occur were modeled as nodes on the graph, and edges and weights between nodes were determined by the distance information of the regions and the flow of people, so as to reflect the spatial relevance in the transmission process of infectious diseases. The number of cases at different times was modeled as the time-varying graph signal, the GP-VAR model was used to predict the evolution process of the time-varying graph signal on the graph, and a least squares (LS) optimization method was designed to estimate the parameters of the GP-VAR model. Experimental results show that the proposed method can better consider the correlation of data in spatial dimension and the evolution of data in time dimension, and characterize the transmission characteristics of infectious diseases more accurately, which has universality and better prediction effect compared with the existing prediction methods.
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Received: 13 September 2021
Published: 31 May 2022
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Fund: 国家自然科学基金资助项目(62171146);广西创新驱动发展专项(桂科AA21077008);广西科技基地和人才专项(桂科AD21220112);广西自然科学杰出青年基金资助项目(2021GXNSFFA220004) |
Corresponding Authors:
Hong-gao DENG
E-mail: 20022201022@mails.guet.edu.cn;dhg@guet.edu.cn
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基于图信号处理的传染病传播预测方法
针对现有传染病传播预测模型存在未充分考虑数据的内在关联性的问题,采用图多项式-向量自回归(GP-VAR)模型对传染病的传播进行预测, 并提出新的用于模型参数估计的优化方法. 将传染病发病地区建模为图节点, 并根据地区间的距离信息和人群流动情况确定节点间的边及其权重, 以反映传染病传播过程中的空间关联性. 将不同时刻的感染疾病人数建模为时变图信号, 使用GP-VAR模型对时变图信号在图上的演变过程进行预测, 并设计一种最小二乘(LS)优化方法对GP-VAR模型的参数进行估计. 仿真实验结果表明, 与现有的预测方法相比,所提方法能够更好地考虑到数据在空间维的相关性和时间维的演变特性, 更加准确地刻画传染病的传播特性, 且具有普适性, 预测效果更好.
关键词:
传染病预测,
图信号处理,
时间序列,
时变图信号,
最小二乘
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[1] |
KHADDAJ S, CHRIEF H. Prevention and control of emerging infectious diseases in human populations [C]// Proceedings of 2020 19th International Symposium on Distributed Computing and Applications for Business Engineering and Science. Xuzhou: [s.n.], 2020: 341-344.
|
|
|
[2] |
庞维庆, 何宁, 罗燕华, 等 基于数据融合的ABC-SVM社区疾病预测方法[J]. 浙江大学学报: 工学版, 2021, 55 (7): 1253- 1260+1326 PANG Wei-qing, HE Ning, LUO Yan-hua, et al ABC-SVM disease prediction method based on data fusion in community health care[J]. Journal of Zhejiang University: Engineering Science, 2021, 55 (7): 1253- 1260+1326
|
|
|
[3] |
KERMACK W O, MCKENDRICK A G A A Contribution to the mathematical theory of epidemics[J]. Proceedings of the Royal Society A: Mathematical Physical and Engineering Sciences, 1927, 115 (772): 700- 721
|
|
|
[4] |
GAO S, CHEN L, NIETO J J, et al Analysis of a delayed epidemic model with pulse vaccination and saturation incidence[J]. Vaccine, 2006, 24 (35/36): 6037- 6045
|
|
|
[5] |
GOMEZ S, ARENAS A, BORGE-HOLTHOEFER J, et al Discrete-time Markov chain approach to contact-based disease spreading in complex networks[J]. Europhysics Letters, 2010, 89 (3): 38009
doi: 10.1209/0295-5075/89/38009
|
|
|
[6] |
BENVENUTO D, GIOVANETTI M, VASSALLO L, et al Application of the ARIMA model on the COVID-2019 epidemic dataset[J]. Data in Brief, 2020, 29: 105340
doi: 10.1016/j.dib.2020.105340
|
|
|
[7] |
余艳妮, 聂绍发, 廖青, 等 传染病预测及模型选择研究进展[J]. 公共卫生与预防医学, 2018, 29 (5): 89- 92 YU Yan-ni, NIE Shao-fa, LIAO Qing, et al Research progress on prediction and model selection of infectious diseases[J]. Journal of Public Health and Preventive Medicine, 2018, 29 (5): 89- 92
|
|
|
[8] |
KIESHA P, YANG L, TIMOTHY W R, et al The effect of control strategies to reduce social mixing on outcomes of the COVID-19 epidemic in Wuhan, China: a modelling study[J]. The Lancet Public Health, 2020, 5 (5): 261- 270
doi: 10.1016/S2468-2667(20)30073-6
|
|
|
[9] |
SANDRYHAILA A, MOURA J M Discrete signal processing on graphs[J]. IEEE Transactions on Signal Processing, 2013, 61 (7): 1644- 1656
doi: 10.1109/TSP.2013.2238935
|
|
|
[10] |
杨杰, 蒋俊正 利用联合图模型的传感器网络数据修复方法[J]. 西安电子科技大学学报, 2020, 47 (1): 44- 51 YANG Jie, JIANG Jun-zheng Method for data recovery in the sensor network based on the joint graph model[J]. Journal of Xidian University, 2020, 47 (1): 44- 51
|
|
|
[11] |
XUE X The contact process with semi-infected state on the complete graph[J]. Stochastic Analysis and Applications, 2018, 36 (2): 324- 340
doi: 10.1080/07362994.2017.1399802
|
|
|
[12] |
BUCUR D, HOLME P Beyond ranking nodes: predicting epidemic outbreak sizes by network centralities[J]. PLOS Computational Biology, 2020, 16 (7): e1008052
doi: 10.1371/journal.pcbi.1008052
|
|
|
[13] |
ISUFI E, LOUKAS A, PERRAUDIN N, et al Forecasting time series with VARMA recursions on graphs[J]. IEEE Transactions on Signal Processing, 2019, 67 (18): 4870- 4885
doi: 10.1109/TSP.2019.2929930
|
|
|
[14] |
MEI J, MOUEA J Signal processing on graphs: causal modeling of unstructured data[J]. IEEE Transactions on Signal Processing, 2017, 65 (8): 2077- 2092
doi: 10.1109/TSP.2016.2634543
|
|
|
[15] |
NOWZARI C, PRECIADO V M, PAPPAS G J Analysis and control of epidemics: a survey of spreading processes on complex networks[J]. IEEE Control Systems, 2016, 36 (1): 26- 46
doi: 10.1109/MCS.2015.2495000
|
|
|
[16] |
PUSCHEL M, MOURA J Algebraic signal processing theory: foundation and 1-D time[J]. IEEE Transactions on Signal Processing, 2008, 56 (8): 3572- 3585
doi: 10.1109/TSP.2008.925261
|
|
|
[17] |
SANDRYHAILA A, MOURA J Discrete signal processing on graphs: frequency analysis[J]. IEEE Transactions on Signal Processing, 2014, 62 (12): 3042- 3054
doi: 10.1109/TSP.2014.2321121
|
|
|
[18] |
JIANG J, CHENG C, SUN Q Nonsubsampled graph filter banks: theory and distributed algorithms[J]. IEEE Transactions on Signal Processing, 2019, 67 (15): 3938- 3953
doi: 10.1109/TSP.2019.2922160
|
|
|
[19] |
TAY D B, JIANG J Time-varying graph signal denoising via median filters[J]. IEEE Transactions on Circuits and Systems II: Express Briefs, 2021, 68 (3): 1053- 1057
doi: 10.1109/TCSII.2020.3017800
|
|
|
[20] |
JIANG J, FENG H, TAY D B, et al Theory and design of joint time-vertex nonsubsampled filter banks[J]. IEEE Transactions on Signal Processing, 2021, 69: 1968- 1982
doi: 10.1109/TSP.2021.3064984
|
|
|
[21] |
GRASSI F, LOUKAS A, PERRAUDIN N, et al A time-vertex signal processing framework: scalable processing and meaningful representations for time-series on graphs[J]. IEEE Transactions on Signal Processing, 2018, 66 (3): 817- 829
doi: 10.1109/TSP.2017.2775589
|
|
|
[22] |
JIANG J, TAY D B, SUN Q, et al Recovery of time-varying graph signals via distributed algorithms on regularized problems[J]. IEEE Transactions on Signal and Information Processing over Networks, 2020, 6: 540- 555
doi: 10.1109/TSIPN.2020.3010613
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