Quantifying the effectiveness of brucellosis control strategies in northern China using a mechanistic and data-driven model

被引:0
|
作者
Zhang, Zhenzhen [1 ,2 ,3 ]
Zhang, Juan [4 ,5 ]
Li, Li [6 ]
Guo, Zunguang [7 ]
Zhang, Zi-Ke [8 ,9 ]
Sun, Gui-Quan [1 ,2 ,4 ]
机构
[1] North Univ China, Sch Data Sci & Technol, Taiyuan 030051, Shanxi, Peoples R China
[2] North Univ China, Sch Math, Taiyuan 030051, Shanxi, Peoples R China
[3] Taiyuan Univ, Dept Comp Sci & Technol, Taiyuan 030032, Peoples R China
[4] Shanxi Univ, Complex Syst Res Ctr, Taiyuan 030006, Shanxi, Peoples R China
[5] Shanxi Key Lab Math Tech & Big Data Anal Dis Contr, Taiyuan 030006, Shanxi, Peoples R China
[6] Shanxi Univ, Sch Comp & Informat Technol, Taiyuan 030006, Shanxi, Peoples R China
[7] Taiyuan Inst Technol, Dept Sci, Taiyuan 030008, Shanxi, Peoples R China
[8] Zhejiang Univ, Ctr Digital Commun Studies, Hangzhou 310058, Zhejiang, Peoples R China
[9] Qinghai Normal Univ, Sch Comp, Xining 810008, Qinghai, Peoples R China
关键词
Dynamical modeling; Real-time reproduction number; Control measures; Time-varying parameters; Deep neural network; TRANSMISSION DYNAMICS; JILIN PROVINCE;
D O I
10.1016/j.chaos.2024.115121
中图分类号
O1 [数学];
学科分类号
0701 ; 070101 ;
摘要
In recent years, with the frequent movement of livestock and related products on the Chinese mainland that have resulted in a rise in brucellosis cases in certain regions, the Chinese government has implemented "vaccination + culling"strategy to control the spread of brucellosis, but the epidemiological situation and the intensity of prevention and control measures vary from province to province, and much of the situation has not yet been reversed. Thus, it is crucial to understand and assess the combined effects of vaccination and culling on brucellosis transmission behind the data from multiple provinces and cities. In this paper, we combine an ordinary differential equation model and a deep neural network model to develop a hybrid model that not only characterizes the propagation mechanism of brucellosis, but also describes control strategies as time -varying parameters to track the dynamic evolution of brucellosis transmission under control measures with varying intensity. In addition, the real-time reproduction number is calculated and taken as an evaluation index. We then train and test the hybrid model using actual data from four provincial administrative regions in Northern China from 2010 to 2020. It is found that in Inner Mongolia, the sheep vaccination rate (theta(t)) and the culling rate of infected sheep ( c ( t ) ) have a weak effect on reducing the brucellosis transmission. In Gansu, the incomplete culling of infected sheep was the main reason for the increase in human brucellosis cases after the introduction of whole -flock vaccination in 2016. Culling and vaccination contributed to the decrease in human cases in both Xinjiang and Jilin. Prevention and control measures for human brucellosis are inadequate in Xinjiang. However, prevention and control measures for human brucellosis are inadequate, while indirect transmission of Brucella from the environment to humans and sheep poses a greater risk than direct contact with infected sheep in Jilin. The above analysis can provide theoretical foundation for decision -making on public health policies to control the pandemic.
引用
收藏
页数:14
相关论文
共 50 条
  • [1] Quantifying Viscous Damping and Stiffness in Parkinsonism Using Data-Driven Model Estimation and Admittance Control
    Werning, Alec
    Umbarila, Daniel
    Fite, Maxwell
    Fergus, Sinta
    Zhang, Jianyu
    Molnar, Gregory F.
    Johnson, Luke A.
    Wang, Jing
    Vitek, Jerrold L.
    Sanabria, David Escobar
    JOURNAL OF MEDICAL DEVICES-TRANSACTIONS OF THE ASME, 2022, 16 (04):
  • [2] Model-Based Evaluation of Strategies to Control Brucellosis in China
    Li, Ming-Tao
    Sun, Gui-Quan
    Zhang, Wen-Yi
    Jin, Zhen
    INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH, 2017, 14 (03)
  • [3] Data-Driven Model Predictive Control using Interpolated Koopman Generators
    Peitz, Sebastian
    Otto, Samuel E.
    Rowley, Clarence W.
    SIAM JOURNAL ON APPLIED DYNAMICAL SYSTEMS, 2020, 19 (03): : 2162 - 2193
  • [4] Amidst Data-Driven Model Reduction and Control
    Monshizadeh, Nima
    IEEE CONTROL SYSTEMS LETTERS, 2020, 4 (04): : 833 - 838
  • [5] Data-Driven Dynamic Internal Model Control
    Chi, Ronghu
    Zhang, Huimin
    Li, Huaying
    Huang, Biao
    Hou, Zhongsheng
    IEEE TRANSACTIONS ON CYBERNETICS, 2024, 54 (09) : 5347 - 5359
  • [6] Stochastic data-driven model predictive control using gaussian processes
    Bradford, Eric
    Imsland, Lars
    Zhang, Dongda
    Chanona, Ehecatl Antonio del Rio
    COMPUTERS & CHEMICAL ENGINEERING, 2020, 139
  • [7] Data-Driven Model for Traffic Signal Control
    Zhang, Chen
    Xi, Yugeng
    Li, Dewei
    Xu, Yunwen
    2018 37TH CHINESE CONTROL CONFERENCE (CCC), 2018, : 7880 - 7885
  • [8] Model-Based and Data-Driven HVAC Control Strategies for Residential Demand Response
    Kou, Xiao
    Du, Yan
    Li, Fangxing
    Pulgar-Painemal, Hector
    Zandi, Helia
    Dong, Jin
    Olama, Mohammed M.
    IEEE OPEN ACCESS JOURNAL OF POWER AND ENERGY, 2021, 8 : 186 - 197
  • [9] Fusion of data-driven model and mechanistic model for kiwifruit flesh firmness prediction
    Xiao, Xun
    Li, Mo
    COMPUTERS AND ELECTRONICS IN AGRICULTURE, 2022, 193
  • [10] Quantifying postprandial glucose responses using a hybrid modeling approach: Combining mechanistic and data-driven models in The Maastricht Study
    Erdos, Balazs
    van Sloun, Bart
    Goossens, Gijs H.
    O'Donovan, Shauna D.
    de Galan, Bastiaan E.
    van Greevenbroek, Marleen M. J.
    Stehouwer, Coen D. A.
    Schram, Miranda T.
    Blaak, Ellen E.
    Adriaens, Michiel E.
    van Riel, Natal A. W.
    Arts, Ilja C. W.
    PLOS ONE, 2023, 18 (07):