Modeling and Transmission Dynamics of Leprosy Disease: Via Numerical Methods

被引:3
|
作者
Raza, Ali [1 ,2 ]
Rafiq, Muhammad [3 ]
机构
[1] Govt Maulana Zafar Ali Khan Grad Coll Wazirabad, Dept Math, Punjab Higher Educ Dept PHED Lahore, Lahore 54000, Pakistan
[2] Natl Coll Business Adm & Econ Lahore, Dept Math, Lahore 54660, Pakistan
[3] Univ Cent Punjab, Fac Sci, Dept Math, Lahore 54500, Pakistan
来源
IRANIAN JOURNAL OF SCIENCE AND TECHNOLOGY TRANSACTION A-SCIENCE | 2022年 / 46卷 / 01期
关键词
Leprosy disease; Mathematical model; Reproduction number; Numerical methods; Stability analysis;
D O I
10.1007/s40995-021-01242-w
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
Leprosy is a chronic infectious disease caused by Mycobacterium leprae, an acid-fast, rod-shaped bacillus. Leprosy is curable, and treatment in the early stages can prevent disability. In 2019, a total of 153 countries reported on leprosy to World Health Organization (WHO): 38 from the African region, 31 from the region of the Americas, 11 from the South-East Asia region, 19 from the Eastern Mediterranean region, 30 from the European region, and 24 from the Western Pacific region. In 2019, 0.2 million new cases of leprosy were detected, and the registered prevalence was 0.7 million cases. The population is categorized into four compartments such as susceptible (x), infected (y), paucibacillary leprosy (w), and multibacillary leprosy (z). The dynamics of disease are analyzed dynamically and numerically. The model predicts positivity, boundedness, equilibria, and local stability rigorously with the support of the reproduction number of the model. In numerical analysis, we develop some explicit and implicit models like Euler and Runge-Kutta methods are time-dependent and violate the physical relevance of the disease. Then, the proposed implicit way for the said model is independent of the time step size, dynamically consistent, positive, and bounded.
引用
收藏
页码:279 / 290
页数:12
相关论文
共 50 条
  • [31] Numerical methods for plasma dynamics
    Stone, JM
    SPECTROSCOPIC CHALLENGES OF PHOTOIONIZED PLASMAS, 2001, 247 : 261 - 280
  • [32] On the dynamics of some numerical methods
    Ahmed, E
    Hegazi, AS
    INTERNATIONAL JOURNAL OF MODERN PHYSICS C, 2000, 11 (07): : 1481 - 1487
  • [33] Numerical Methods in Fluid Dynamics
    Wirz, H.J.
    Smolderen, J.J.
    White, Frank M.
    Journal of Fluids Engineering, Transactions of the ASME, 1980, 102 (01):
  • [34] Numerical Methods in Modeling the Ionosphere
    Huba, J. D.
    Joyce, G.
    MODELING THE IONOSPHERE-THERMOSPHERE SYSTEM, 2013, 201 : 49 - 55
  • [35] NUMERICAL MODELING FOR ELECTRICAL METHODS
    HOHMANN, GW
    GEOPHYSICS, 1983, 48 (04) : 485 - 485
  • [36] NUMERICAL MODELING OF INTERSARCOMERE DYNAMICS
    MORGAN, DL
    JULIAN, FJ
    FEDERATION PROCEEDINGS, 1980, 39 (06) : 1731 - 1731
  • [37] Modeling Zika Virus Transmission Dynamics: Parameter Estimates, Disease Characteristics, and Prevention
    Munsur Rahman
    Kidist Bekele-Maxwell
    LeAnna L. Cates
    H. T. Banks
    Naveen K. Vaidya
    Scientific Reports, 9
  • [38] Modeling Disease Agents Transmission Dynamics in Dementia on Heterogeneous Spatially Embedded Networks
    Tahmassebi, Amirhessam
    Karbaschi, Gelareh
    Meyer-Baese, Uwe
    Meyer-Baese, Anke
    PATTERN RECOGNITION AND TRACKING XXXII, 2021, 11735
  • [39] Modeling the potential influence of economic migration on Ebola virus disease transmission dynamics
    Njankou, Sylvie Diane Djiomba
    Nyabadza, Farai
    FRONTIERS IN APPLIED MATHEMATICS AND STATISTICS, 2023, 9
  • [40] Modeling the dynamics of the consequences of demographic disparities in the transmission of Lassa fever disease in Nigeria
    Oluwatayo Michael Ogunmiloro
    Modeling Earth Systems and Environment, 2023, 9 : 865 - 880