International journal of

ADVANCED AND APPLIED SCIENCES

EISSN: 2313-3724, Print ISSN:2313-626X

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 Volume 6, Issue 3 (March 2019), Pages: 79-85

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 Original Research Paper

 Title: Nonstandard finite difference scheme for control of measles epidemiology

 Author(s): Farah Ashraf *, M. O. Ahmad

 Affiliation(s):

 Department of Mathematics and Statistics, University of Lahore, Lahore, Pakistan

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 * Corresponding Author. 

  Corresponding author's ORCID profile: https://orcid.org/0000-0002-5700-3822

 Digital Object Identifier: 

 https://doi.org/10.21833/ijaas.2019.03.012

 Abstract:

This paper is based on the analysis of SEIR measles models, which are used to study the integrating vaccination as a control strategy and taking the two stages of infectiousness and transmission dynamics of infectious diseases in a population. Measles is a higher contagious that can spread in a community population depending on the number of people susceptible or infected and also depending on their movement in a community. We construct an unconditionally convergent nonstandard finite difference (NSFD) scheme for SEIR measles model. NSFD preserve the positivity of all values of h. This method proved to be a very efficient technique for solving epidemic models. We obtained disease-free equilibrium (DFE) point, Endemic equilibrium (EE), reproduction number for the model. Moreover, the analysis of the epidemic models using nonstandard finite difference scheme reveals that the method provides a rapidly convergent series solution by little iteration and avoids the massive computational work. Numerical simulations show that the rate of infection is decreased with the passage of time and disease will die out in the community. The results are compared to the Differential Transformation Method to show this scheme is efficient and better accuracy for epidemic models. 

 © 2019 The Authors. Published by IASE.

 This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

 Keywords: SEIR model, Qualitative analysis, DTM, NSFD, Stability analysis

 Article History: Received 26 October 2018, Received in revised form 13 January 2019, Accepted 26 January 2019

 Acknowledgement:

No Acknowledgement

 Compliance with ethical standards

 Conflict of interest:  The authors declare that they have no conflict of interest.

 Citation:

 Ashraf F and Ahmad MO (2019). Nonstandard finite difference scheme for control of measles epidemiology. International Journal of Advanced and Applied Sciences, 6(3): 79-85

 Permanent Link to this page

 Figures

 Fig. 1 Fig. 2 Fig. 3 Fig. 4 Fig. 5 Fig. 6 Fig. 7 Fig. 8 Fig. 9 Fig. 10 Fig. 11 Fig. 12 Fig. 13 

 Tables

 Table 1 Table 2 

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 References (23) 

  1. Akinboro FS, Alao S, and Akinpelu FO (2014). Numerical solution of SIR model using differential transformation method and variational iteration method. General Mathematics Notes, 22(2): 82-92.   [Google Scholar]
  2. Anguelov R and Lubuma JMS (2001). Contributions to the mathematics of the nonstandard finite difference method and applications. Numerical Methods for Partial Differential Equations: An International Journal, 17(5): 518-543. https://doi.org/10.1002/num.1025   [Google Scholar]
  3. Chen CJ and Wu WJ (1996). Application of the Taylor differential transformation method to viscous damped vibration of hard and soft spring system. Computers and Structures, 59(4): 631-639. https://doi.org/10.1016/0045-7949(95)00304-5   [Google Scholar]
  4. Chen CK and Ho SH (1996). Application of differential transformation to eigenvalue problems. Applied Mathematics and Computation, 79(2-3): 173-188. https://doi.org/10.1016/0096-3003(95)00253-7   [Google Scholar]
  5. Chen CL and Sy-Hong L (1996). Application of Taylor transformation to nonlinear predictive control problem. Applied Mathematical Modelling, 20(9): 699-710. https://doi.org/10.1016/0307-904X(96)00050-9   [Google Scholar]
  6. Dimitrov DT and Kojouharov HV (2005). Analysis and numerical simulation of phytoplankton–nutrient systems with nutrient loss. Mathematics and Computers in Simulation, 70(1): 33-43. https://doi.org/10.1016/j.matcom.2005.03.001   [Google Scholar]
  7. Grenfell BT (1992). Chance and chaos in measles dynamics. Journal of the Royal Statistical Society. Series B (Methodological), 54(2): 383-398. https://doi.org/10.1111/j.2517-6161.1992.tb01888.x   [Google Scholar]
  8. Gumel AB, Patidar KC, and Spiteri RJ (2005). Asymptotically consistent non-standard finite-difference methods for solving mathematical models arising in population biology. In: Mickens RE (Ed.), Advances in the applications of nonstandard finite difference schemes: 385-421. World Scientific, Singapore. https://doi.org/10.1142/9789812703316_0009   [Google Scholar]
  9. Hethcote HW (2000). The mathematics of infectious diseases. Society for Industrial and Applied Mathematics Review, 42(4): 599-653. https://doi.org/10.1137/S0036144500371907   [Google Scholar]
  10. Jang MJ and Chen CL (1997). Analysis of the response of a strongly nonlinear damped system using a differential transformation technique. Applied Mathematics and Computation, 88(2-3): 137-151. https://doi.org/10.1016/S0096-3003(96)00308-6   [Google Scholar]
  11. Jang SRJ (2005). Nonstandard finite difference methods and biological models. In: Mickens RE (Ed.), Advances in the applications of nonstandard finite difference schemes: 423-457. World Scientific, Singapore. https://doi.org/10.1142/9789812703316_0010   [Google Scholar]
  12. Mickens RE (2003). A nonstandard finite-difference scheme for the Lotka–Volterra system. Applied Numerical Mathematics, 45(2-3): 309-314. https://doi.org/10.1016/S0168-9274(02)00223-4   [Google Scholar]
  13. Moaddy K, Hashim I, and Momani S (2011). Non-standard finite difference schemes for solving fractional-order Rössler chaotic and hyperchaotic systems. Computers and Mathematics with Applications, 62(3): 1068-1074. https://doi.org/10.1016/j.camwa.2011.03.059   [Google Scholar]
  14. Momoh AA, Ibrahim MO, Uwanta IJ, and Manga SB (2013). Mathematical model for control of measles epidemiology. International Journal of Pure and Applied Mathematics, 87(5): 707-717. https://doi.org/10.12732/ijpam.v87i5.4   [Google Scholar]
  15. Mounim AS and de Dormale BM (2004). A note on Mickens' finite-difference scheme for the Lotka–Volterra system. Applied Numerical Mathematics, 51(2-3): 341-344. https://doi.org/10.1016/j.apnum.2004.06.014   [Google Scholar]
  16. Murray J (2003). Mathematical biology: I. An introduction. 3rd Edition, Springer, Berlin, Germany.   [Google Scholar]
  17. Ochoche JM and Gweryina RI (2014). A mathematical model of measles with vaccination and two phases of infectiousness. IOSR Journal of Mathematics, 10(1): 95-105. https://doi.org/10.9790/5728-101495105   [Google Scholar]
  18. Pukhov GE (1978). Computational structure for solving differential equations by Taylor transformations. Cybernetics, 14(3): 383-390. https://doi.org/10.1007/BF01074670   [Google Scholar]
  19. Pukhov GE (1981). Expanison formulas for differential transforms. Cybernetics, 17(4): 460-464. https://doi.org/10.1007/BF01082476   [Google Scholar]
  20. Pukhov GE (1982). Differential transforms and circuit theory. International Journal of Circuit Theory and Applications, 10(3): 265-276. https://doi.org/10.1002/cta.4490100307   [Google Scholar]
  21. Pukhov GE (1986). Differential transformations and mathematical modelling of physical processes. Naukova Dumka, Kiev, Ukraine.   [Google Scholar] PMid:2944207
  22. Taylor AH, Harris JRW, and Aiken J (1986). Distribution of phytoplankton under stratification. Marine Interfaces Ecohydrodynamics, 42: 313-330. https://doi.org/10.1016/S0422-9894(08)71052-3   [Google Scholar]
  23. Zhou JK (1986). Differential transformation and its applications for electrical circuits. Huarjungs University Press, Wuuhahn, China.   [Google Scholar] PMCid:PMC341339