Open Access
ARTICLE
Numerical Control Measures of Stochastic Malaria Epidemic Model
Muhammad Rafiq1, Ali Ahmadian2, *, Ali Raza3, Dumitru Baleanu4, Muhammad Sarwar Ahsan1, Mohammad Hasan Abdul Sathar5
1 Faculty of Engineering, University of Central Punjab, Lahore, Pakistan.
2 Institute of Visual Informatics, National University of Malaysia, Bangi, 43600 UKM, Malaysia.
3 Faculty of Computing, National College of Business Administration and Economics, Lahore, Pakistan.
4 Department of Mathematics, Cankaya University, Ankara, 06530, Turkey.
5 Centre of Foundation Studies for Agricultural Science, Universiti Putra Malaysia, Serdang, 43400,
Malaysia.
* Corresponding Author: Ali Ahmadian. Email: .
Computers, Materials & Continua 2020, 65(1), 33-51. https://doi.org/10.32604/cmc.2020.010893
Received 04 April 2020; Accepted 21 April 2020; Issue published 23 July 2020
Abstract
Nonlinear stochastic modeling has significant role in the all discipline of
sciences. The essential control measuring features of modeling are positivity,
boundedness and dynamical consistency. Unfortunately, the existing stochastic methods
in literature do not restore aforesaid control measuring features, particularly for the
stochastic models. Therefore, these gaps should be occupied up in literature, by
constructing the control measuring features numerical method. We shall present a
numerical control measures for stochastic malaria model in this manuscript. The results
of the stochastic model are discussed in contrast of its equivalent deterministic model. If
the basic reproduction number is less than one, then the disease will be in control while
its value greater than one shows the perseverance of disease in the population. The
standard numerical procedures are conditionally convergent. The propose method is
competitive and preserve all the control measuring features unconditionally. It has also
been concluded that the prevalence of malaria in the human population may be controlled
by reducing the contact rate between mosquitoes and humans. The awareness programs
run by world health organization in developing countries may overcome the spread of
malaria disease.
Keywords
Cite This Article
M. Rafiq, A. Ahmadian, A. Raza, D. Baleanu, M. Sarwar Ahsan
et al., "Numerical control measures of stochastic malaria epidemic model,"
Computers, Materials & Continua, vol. 65, no.1, pp. 33–51, 2020.
Citations