JACIII Vol.28 No.3 pp. 739-745
doi: 10.20965/jaciii.2024.p0739

Research Paper:

Research on Early Warning of Transmission of Tuberculosis Infectious Diseases from the Perspective of Social Factors

Miao Zhu ORCID Icon, Xiyi Li, Xingyue Zhang, and Xiaoyu Dong

School of Statistics, Huaqiao University
No.668 Jimei Avenue, Jimei District, Xiamen, Fujian 361021, China

Corresponding author

January 14, 2024
March 10, 2024
May 20, 2024
bond percolation model, tuberculosis, epidemic network, SEIR model

In this study, the infiltration model was established to study the early warning of pulmonary tuberculosis data in Xiamen public hospitals. Based on the gender characteristics of residents in Xiamen, a percolation model was established to analyze the transmission rates of diseases under different contact types. In addition, the calculation method of the percolation threshold is discussed, and the model is verified by a simulation experiment. The results show that the model can predict the spread of epidemic situations well. The early warning value and relevant preventive measures were obtained by simulating the spread of tuberculosis under different exposure numbers. Bond percolation analysis was used to predict the proportion of the eventually infected population, this threshold of percolation was the basic regeneration number of tuberculosis, and the tuberculosis infection situation was effectively predicted.

Cite this article as:
M. Zhu, X. Li, X. Zhang, and X. Dong, “Research on Early Warning of Transmission of Tuberculosis Infectious Diseases from the Perspective of Social Factors,” J. Adv. Comput. Intell. Intell. Inform., Vol.28 No.3, pp. 739-745, 2024.
Data files:
  1. [1] “Global Tuberculosis Report 2021,” World Health Organization, 2021.
  2. [2] V. P. Bajiya et al., “Global dynamics of a multi-group SEIR epidemic model with infection age,” Chinese Annals of Mathematics, Series B, Vol.42, No.6, pp. 833-860, 2021.
  3. [3] B. Ji, “SIR model of COVID-19 epidemic spread between two regions,” Proc. of the 2nd Int. Conf. on Mathematical Statistics and Economic Analysis (MSEA 2023), 2023.
  4. [4] G. Cao and L. Shen, “Modelling and simulating medical crowdfunding with SEIR,” Data Analysis and Knowledge Discovery, Vol.6, No.1, pp. 80-90, 2022 (in Chinese).
  5. [5] J. R. Zelnick et al., “Health-care workers’ perspectives on workplace safety, infection control, and drug-resistant tuberculosis in a high-burden HIV setting,” J. of Public Health Policy, Vol.34, No.3, pp. 388-402, 2013.
  6. [6] S. Lin et al., “Analysis of tuberculosis epidemiological characteristics and application of incidence prediction model in Fujian Province from 2010 to 2019,” Chinese J. of Disease Control & Prevention, Vol.25, No.7, pp. 768-774, 2021 (in Chinese).
  7. [7] H. W. Hethcote, “The mathematics of infectious diseases,” SIAM Review, Vol.42, No.4, pp. 599-653, 2000.
  8. [8] M. Dickison, S. Havlin, and H. E. Stanley, “Epidemics on interconnected networks,” Physical Review E, Vol.85, No.6, Article No.066109, 2012.
  9. [9] S. Li and X. Zhao, “Network percolation of the disease transmission based on bipartite networks,” Int. J. of Modern Physics B, Vol.34, No.6, Article No.2050029, 2020.
  10. [10] S. Li and Y. Zhang, “Application of LSTM and Prophet models in predicting the number of tuberculosis cases,” Henan Science, Vol.38, No.2, pp. 173-178, 2020 (Chinese).
  11. [11] M. Asad, A. Mahmood, and M. Usman, “A machine learning-based framework for Predicting Treatment Failure in tuberculosis: A case study of six countries,” Tuberculosis, Vol.123, Article No.101944, 2020.
  12. [12] M. Newman, “Networks: An Introduction,” 1st Edition, Oxford University Press, 2010.
  13. [13] M. Opuszko and J. Ruhland, “Impact of the network structure on the SIR model spreading phenomena in online networks,” Proc. of the 8th Int. Multi-Conf. on Computing in the Global Information Technology (ICCGI 2013), pp. 22-28, 2013.
  14. [14] D.-S. Lee and M. Zhu, “Epidemic spreading in a social network with facial masks wearing individuals,” IEEE Trans. on Computational Social Systems, Vol.8, No.6, pp. 1393-1406, 2021.
  15. [15] R. Diel and A. Nienhaus, “Pathways of TB transmission in children—A systematic review of molecular epidemiological studies,” Int. J. of Environmental Research and Public Health, Vol.20, No.3, Article No.1737, 2023.
  16. [16] W. Guan et al., “Comorbidity and its impact on 1590 patients with COVID-19 in China: A nationwide analysis,” European Respiratory J., Vol.55, No.5, Article No.2000547, 2020.
  17. [17] R. Singh et al., “Mathematical modelling and analysis of COVID-19 and tuberculosis transmission dynamics,” Informatics in Medicine Unlocked, Vol.38, Article No.101235, 2023.
  18. [18] L. Xue et al., “Seasonal transmission dynamics and optimal control strategies for tuberculosis in Jiangsu Province, China,” Mathematical Methods in the Applied Sciences, Vol.46, No.2, pp. 2072-2092, 2023.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on Jun. 03, 2024