Please wait a minute...
Chin. Phys. B, 2025, Vol. 34(1): 018904    DOI: 10.1088/1674-1056/ad92ff
Special Issue: SPECIAL TOPIC — Computational programs in complex systems
SPECIAL TOPIC — Computational programs in complex systems Prev  

Dynamic modeling and analysis of brucellosis on metapopulation network: Heilongjiang as cases

Xin Pei(裴鑫)1, Xuan-Li Wu(武绚丽)1, Pei Pei(裴沛)2, Ming-Tao Li(李明涛)1, Juan Zhang(张娟)3, and Xiu-Xiu Zhan(詹秀秀)4,†
1 School of Mathematics, Taiyuan University of Technology, Taiyuan 030024, China;
2 Department of Information Technology, Shanxi Professional College of Finance, Taiyuan 030008, China;
3 Complex System Research Center, ShanXi University, Taiyuan 030006, China;
4 Alibaba Complex Science Research Center, Hangzhou Normal University, Hangzhou 311121, China
Abstract  Livestock transportation is a key factor that contributes to the spatial spread of brucellosis. To analyze the impact of sheep transportation on brucellosis transmission, we develop a human-sheep coupled brucellosis model within a metapopulation network framework. Theoretically, we examine the positively invariant set, the basic reproduction number, the existence, uniqueness, and stability of disease-free equilibrium and the existence of the endemic equilibrium of the model. For practical application, using Heilongjiang province as a case study, we simulate brucellosis transmission across 12 cities based on data using three network types: the BA network, the ER network, and homogeneous mixing network. The simulation results indicate that the network's average degree plays a role in the spread of brucellosis. For BA and ER networks, the basic reproduction number and cumulative incidence of brucellosis stabilize when the network's average degree reaches 4 or 5. In contrast, sheep transport in a homogeneous mixing network accelerates the cross-regional spread of brucellosis, whereas transportation in a BA network helps to control it effectively. Furthermore, the findings suggest that the movement of sheep is not always detrimental to controlling the spread of brucellosis. For cities with smaller sheep populations, such as Shuangyashan and Qitaihe, increasing the transport of sheep outward amplifies the spatial spread of the disease. In contrast, in cities with larger sheep populations, such as Qiqihar, Daqing, and Suihua, moderate sheep outflow can help reduce the spread. In addition, cities with large livestock populations play a dominant role in the overall transmission dynamics, underscoring the need for stricter supervision in these areas.
Keywords:  brucellosis      metapopulation network      basic reproduction number      sheep transport  
Received:  29 September 2024      Revised:  04 November 2024      Accepted manuscript online:  15 November 2024
PACS:  89.75.Hc (Networks and genealogical trees)  
  05.90.+m (Other topics in statistical physics, thermodynamics, and nonlinear dynamical systems)  
  92.20.jp (Ecosysystems, structure, dynamics and modeling)  
  02.70.-c (Computational techniques; simulations)  
Fund: Project supported by the National Natural Science Foundation of China (Grant No. 12101443,12371493) and the Natural Science Foundation of Shanxi Province (Grant Nos. 20210302124260 and 202303021221024).
Corresponding Authors:  Xiu-Xiu Zhan     E-mail:  zhanxiuxiu@hznu.edu.cn

Cite this article: 

Xin Pei(裴鑫), Xuan-Li Wu(武绚丽), Pei Pei(裴沛), Ming-Tao Li(李明涛), Juan Zhang(张娟), and Xiu-Xiu Zhan(詹秀秀) Dynamic modeling and analysis of brucellosis on metapopulation network: Heilongjiang as cases 2025 Chin. Phys. B 34 018904

[1] Robertson A 1976 Handbook on Animal Diseases in the Tropics, 3rd Edn. (London: Veterinary) p. 96
[2] Pappas G and Papadimitriou P 2007 Int. J. Antimicrob. Agents 30 29
[3] Sun G Q, Li M T, Zhang J, Zhang W, Pei X and Jin Z 2020 Comput. Struct. Biotechnol. J. 18 3843
[4] Zhang J, Ruan S G, Sun G Q, Sun X D and Jin Z 2014 Journal of Shanghai Normal University 43 441
[5] Zeng H,Wang Y M, Sun X D, Liu P, Xu Q G, Huang D, Gao L, You S B and Huang B X 2019 PLOS Neglected Tropical Diseases 13 e0007285
[6] Ge J X, Ying S H, Tang L, Yuan S and Xing Z F 2023 Chin. J. Public Health Eng. 22 6
[7] Suo B, Liu M, Wu C Y, Zhou K, Liu C M and Z X 2022 J. Med. Pest Control 38 2
[8] Yin S H, Tang L, Yu Y L, Xing Z F Yuan S and Ge J X 2021 Disease Surveil. 36 12
[9] Yang C L, Lolika P O, Mushayabasa S and Wang J 2017 Nonlinear Anal. Real World Appl. 38 49
[10] Guo J M, Luo X F, Zhang J and Li M T 2022 Mathematics 10 3436
[11] Qin Y Y, Pei X, Li M T and Chai Y Z 2022 Math. Biosci. Eng. 19 6396
[12] Li M, Song Y R, Song B, Li R Q, Jiang G P and Zhang H 2024 Chin. Phys. B 33 088902
[13] Yang P, Fan R G, Wang Y B and Zhang Y Q 2024 Chin. Phys. B 33 070206
[14] Zhang Y Q, Wang X W, Wang Y N and Zheng W 2024 Chin. Phys. B 33 070702
[15] Colizza V, Pastor-Satorras R and Vespignani A 2007 Nat. Phys. 3 276
[16] Richard L 1969 Bulletin of the Entomological Society of America 15 237
[17] Levins R 1970 Extinction. In: Gesternhaber M (ed) Some mathematical problems in biology (Providence, Rhode Island: American Mathematical Society) pp. 77-107
[18] Gallos L K and Argyrakis P 2004 Phys. Rev. Lett. 92 138301
[19] Lentz H H K, Selhorst T and Sokolov IM2012 Phys. Rev. E 85 066111
[20] Dreessche P and Watmough J 2002 Math. Bio 180 29
[21] Diekmann O, Heesterbeek J A and Roberts M G 2010 J. Royal Soc. Interf. 7 873
[22] Waltman P E and Smith H L 1995 The Theory of the Chemostat: Dynamics of Microbial Competition (London: Cambridge University Press) pp. 99-102
[23] Thieme H R 1992 J. Math. Biol. 30 755
[24] Thieme H R 2006 Siam J. Math. Anal. 24 407
[25] Zhao X Q 1995 Canad. Appl. Math. Quart. 3 473
[26] Hou Q, Sun X D, Zhang J, Liu Y J, Wang Y M and Jin Z 2013 Math. Biosci. 242 51
[27] Zhou L, Fan M, Hou Q, Jin Z and Sun X D 2018 Math. Biosci. Eng. 38 435
[28] Shi Y J, Lai S J, Chen Q L, Mu D, Li Y, Li X X, Yin W W and Yu H J 2017 Chinese Center for Disease Control and Prevention 242 51
[29] The Heilongjiang Provincial Bureau of Statistics
[30] Wang L S, Li M T, Pei X, Zhang J, Sun G Q and Jin Z 2023 Commun. Nonl. Sci. Numer. Simul. 124 107310
[31] Li M T, Sun G Q, Zhang J, Jin Z, Sun X D,Wang Y M, Huang B X and Zheng Y H 2014 Math. Biosci. Eng. 11 1115
[32] Wang X F, Li X and Chen G R 2012 Network Science: An Introduction 1st ed (Beijing: Higher Education Press) pp. 193-226, 272-276
[1] Analysis of radiation diffusion of COVID-19 driven by social attributes
Fuzhong Nian(年福忠), Xiaochen Yang(杨晓晨), and Yayong Shi(师亚勇). Chin. Phys. B, 2024, 33(1): 018904.
[2] Direct immune-SCIR public-opinion propagation model based on real-time online users
Yun-Ming Wang(王运明), Tian-Yi Guo(郭天一)†, Wei-Dong Li(李卫东)‡, and Bo Chen(陈波). Chin. Phys. B, 2020, 29(10): 100204.
[3] Dynamical analysis of a sexually transmitted disease model on complex networks
Yuan Xin-Peng (原新鹏), Xue Ya-Kui (薛亚奎), Liu Mao-Xing (刘茂省). Chin. Phys. B, 2013, 22(3): 030207.
No Suggested Reading articles found!