JOURNAL ARTICLE
An age-structured tuberculosis model with information and immigration: Stability and simulation study.
Published In: International Journal of Biomathematics, 2023, v. 16, n. 2. P. 1 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Liu, Lili; Zhang, Jian; Li, Yazhi; Ren, Xinzhi 3 of 3
Abstract
This paper establishes an age-structured Tuberculosis (TB) model to investigate the joint impacts of information and immigration of population on the spread of TB disease. Mathematically, we show that the model is point dissipative, and the semi-flow generated by the model has the property of asymptotic smoothness, and then study the existence and global stability of positive steady state by the direct Lyapunov functional. Numerically, by using Matlab software, we verify the theoretical results, and further explore the influence of information (including information coverage and disease-related memory delay) and immigration on the final size of TB disease. The simulation results show that both information coverage and immigration are positive correlated with the final size of disease, and disease-related memory delay can affect the arrival time of positive steady state, which implies us that improving information coverage, enlarging disease-related memory, and reducing the immigration of population (especially latent and infected individuals) can effectively control the progression of TB disease. [ABSTRACT FROM AUTHOR]
Additional Information
- Source:International Journal of Biomathematics. 2023/02, Vol. 16, Issue 2, p1
- Document Type:Article
- Subject Area:Consumer Health
- Publication Date:2023
- ISSN:1793-5245
- DOI:10.1142/S1793524522500760
- Accession Number:162295949
- Copyright Statement:Copyright of International Journal of Biomathematics is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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