|
| |
 |
|
| ORIGINAL ARTICLE |
|
| Year : 2021 | Volume
: 5
| Issue : 1 | Page : 7-13 |
|
Analysis for malaria transmission dynamic between human and mosquito population, part II: Effective infection rate using new technique
S Saravana Kumar1, L Maragatham1, A Eswari2
1 Department of Mathematics, Sri Ramakrishna Institute of Technology, Coimbatore, Tamil Nadu, India
2 Department of Physical Sciences and IT, AEC and RI, TNAU, Coimbatore, Tamil Nadu, India
| Date of Submission | 27-Nov-2020 |
| Date of Acceptance | 07-Dec-2020 |
| Date of Web Publication | 15-Sep-2021 |
Correspondence Address:
Prof. S Saravana Kumar
Department of Mathematics, Sri Ramakrishna Institute of Technology, Coimbatore, Tamil Nadu
India
 Source of Support: None, Conflict of Interest: None
DOI: 10.4103/MTSP.MTSP_15_20
Objective : This article presents the seven equation SEIR-SIR model for the dynamics of malaria parasite transmission in both mosquito and human. It defines the presence of area in which the model is epidemiologically feasible. Material and Methods: This paper is to find the approximate solution of the above models using q-homotopy analysis method. It is a flexible method that is used to solve a variety of differential equations. Results: Numerical simulations are carried out to confirm the analytic results and explore the possible behavior of the formulated model. Conclusions: The results of our study are that, Malaria can be controlled by reducing the rate of contact between humans and mosquitoes, the use of active malaria drugs, insecticides and mosquito nets treated with mosquitoes can also help reduce mosquito populations and malaria transmission respectively.
Keywords: Infection rate, malaria transmission, nonlinear model, q-homotopy analysis method, simulation
How to cite this article:
Kumar S S, Maragatham L, Eswari A. Analysis for malaria transmission dynamic between human and mosquito population, part II: Effective infection rate using new technique. Matrix Sci Pharma 2021;5:7-13 |
How to cite this URL:
Kumar S S, Maragatham L, Eswari A. Analysis for malaria transmission dynamic between human and mosquito population, part II: Effective infection rate using new technique. Matrix Sci Pharma [serial online] 2021 [cited 2023 Feb 3];5:7-13. Available from: https://www.matrixscipharma.org/text.asp?2021/5/1/7/326041 |
| Introduction | |  |
Malaria, an infectious disease caused by a virus contracted by the bite of a female mosquito called Anopheles mosquito, is one of the most prevalent vector transmitted diseases in the world. Given the decades of global efforts to eliminate the disease and to monitor reappear in areas where preventive measures have been successful and appears in epidemic-free zone.[1],[2],[3],[4],[5] Mathematical models for malaria transmission dynamics are useful to provide a clearer view of the epidemic, to plan for the future, and to suggest effective control steps. Models played a major role in the growth of the disease epidemiology. The study on malaria using mathematical modeling originated from the works of a recent study.[6] According to Ross, if the mosquito population can be reduced to below a certain threshold, then malaria can be eradicated. MacDonald did some modifications to the model and included superinfection.[7],[8] He has shown that the number of mosquitoes has no impact on the malaria epidemiology in areas with severe transmission.
Based on previous studies add two classes of men in their mathematical models, namely those with low recovery rates (more infections, vulnerability is greater) and high recovery rate (less infection, less susceptibility).[8],[9] Compartmental models of malaria and differential equations are designed to shape the disease.[8],[10],[11],[12],[13],[14] Studies on malaria vectors with bifurcation. Forms of malaria transmission that integrate immunity in humans have been studied.[11],[12],[13],[14],[15] Only epidemiological models were developed for the dissemination of antimalarial resistance.[15] Anderson and May proposed a malaria model assuming that the immunity obtained in malaria is independent of the period of exposure.[16] They also examined the different control measures and the role of transmission rates in the prevalence of disease. Ross and Macdonald's works have been expanded further by a research with the common generalized SEIR malaria model, which involves human and mosquito interactions.[10] Recently, Al-Rahman et al. analyzed the mathematical model for malaria transmission using stability analysis.[17] Currently, previous research has solved a nonlinear model in different mathematical areas using asymptotic method.[18],[19],[20],[21]
Our goal in this work is to investigate and understand the effect of logistic growth model due to the transmission of malaria in a fully homogeneous population and to inspect the influence of the embedded parameters in the dynamic of the malaria disease model. In this paper, we present a model formulation of malaria transmission in part 2, where the general mathematical framework, notation and equations SEIR model-SEI analyzed. In section 3, Application of q-HAM. In Part 4, we perform numerical simulation model with a graphic illustration. Part 5 consists of the results of the discussion and gives a closing speech in Part 6.
| Mathematical Analysis of Seir-Sei Model | | |
The SEIR-SEI Model for the transmission of malaria between humans and mosquitoes is relevant to this work,[17] using a seven-dimensional (ODE'S) Ordinary Differential Equations method to model the transmission of plasmodium falciparum malaria between humans and mosquitoes with nonlinear infection rate in the form of saturated incidence rates. This model is formulated for both human population and mosquito population at time t. We divide the human population into (4) four classes: Susceptible SH, Exposed EH, Infectious IH, and Recovery Human RH, and that of the population the mosquitoes is divided into three classes they are Susceptible SV, Exposed EV, and Infectious IV, respectively. The compartmental model which shows the mode of transmission of malaria between the two interacting populations is depicted in the [Figure 1]. The above model equations are given by: | Figure 1: Schematic diagram of malaria transmission between humans and mosquitoes
Click here to view |
with
Where ∧H denotes recruitment rate for humans, ∧V is recruitment rate for mosquitoes, α1H is developing rate of exposed (humans) becoming infectious, α2H is recovery rate of humans (removal rate), μH is natural death rate for humans, δ is inducing death rate for humans, α1V is developing rate of exposed (mosquitoes) becoming infectious, μV is natural death rate for mosquito, and βH and βV are infection rate qH × ηV of humans qV × ηV and mosquitoes, respectively, where qH and qV are probability of transmission of infection from an infectious mosquito to a susceptible human and an infectious human to a susceptible mosquito, respectively. For q-ham solution, we choose the linear operator:[22],[23]
Which satisfies the following conditions:
Where c1, c2, c3, c4, c5, c6 and c7 are integral constants. According to the zeroth-order deformation equations and the mth-order deformation equations with the initial conditions,
Where
Now, the solution of system of equations (4)–(10) for m ≥ 1. We now successively obtain
These solutions (5)–(11) are used to construct the analytical solutions given by
| Numerical Solution | | |
Here, we analyze the behavior of the system numerically using the surveillance estimate of susceptible, exposed, infectious, and recovered mosquito population, as shown in [Table 1], and by considering initial conditions, such as.[16],[17] | Table 1: Parameters descriptions for SEIR-SEI model (Al-Rahman et al., 2017)
Click here to view |
The numerical simulations are performed using MATLAB, and the results are shown in [Figure 2] to illustrate the behavior of the system with respect to the different values of the model parameters. It can be easily verified that the condition of nonlinear differential equation (1) could be solved using analytical method and compared with the help of function pdex in MATLAB. Satisfactory results are observed. | Figure 2(a,b,c,d): Comparison between analytical and Simulation result of the model (1), showing malaria transmission population against time for values of embedded parameters indicated in (Al- Rahman et al., 2017)
Click here to view |
| Discussion | | |
The analytical results show that in epidemic situation of model (1), both the human and mosquito populations will exist and get infected. These results are helpful in predicting malaria transmission and how to find an effective way of malaria prevention and control in the model. In [Figure 2], it is inferred that the susceptible human and mosquito population increases as time increases and its carrying capacity remains stable, while the exposed and infected human and mosquito population decreases over time. In reality, the plot reveals that the susceptible human populations continue to grow, which means that the disease will remain endemic throughout the population.
| Conclusions | | |
In this paper, we have formulated and analyzed a compartmental malaria transmission model for nonlinear infection rates in human and mosquito populations. The nonlinear model is analyzed using the asymptotic tool q-HAM. The human population has been divided into four compartments: susceptible, exposed, infectious, and recovered, while the mosquito population has been divided into three compartments: susceptible, exposed, and infectious, because the mosquitoes remain life-infectious and have not recovered.The conditions for the system of analytical solution have been determined using q-homotopy analysis method, and these conditions are further justified numerically by considering a given set of parameter values, see Appendix 1: Basic idea of the q-homotopy analysis of the problem. Infection rates are not linear in the model formulated shows the incidence rate is not saturated like ordinary mass action and the common occurrence. We established a region where the model is epidemiologically feasible and mathematically well-posed.
Financial support and sponsorship
Nil.
Conflicts of interest
There are no conflicts of interest.
Appendix 1: Basic idea of the q-homotopy analysis of the problem
Considering the following system of differential equation of the form
Where Ni = (i = 1, 2, 3, ...n) are nonlinear operators, t is independent variable, fi (t) (i = 1, 2, 3, ...n) are
Known functions and xi (t) (i = 1, 2, 3, ...n) are an unknown function. Note that, not necessary in the nonlinear operator Ni contains nonlinear term. Let us construct the so-called zeroth-order deformation equation
Where  denotes the so-called embedding operator, L is an auxiliary linear operator with the property L[f] = 0 when f = 0, h ≠ 0 is an auxiliary parameter, xi,0 (t) are initial guesses of xi (t) and Hi (t) denotes a non-zero auxiliary function. It is obvious that when q = 0 and  equation (A2) becomes
respectively. Thus, as q increases from 0  to the solution  varies from the initial guess xi,0 (t) to the solution xi (t). Having the freedom to choose xi,0 (t), L, h, H (t), we can assume that all of them can be properly chosen so that the solution of equation (A2) exists for 
Expanding in Taylor series with respect to q,one has
Where
Assume that h, H(t),ui,0(t),L are properly chosen such that the series (A4) converges at  and:
Define the vector 
Differentiating equation (A2)m times with respect to the embedding operator q and then setting q = 0 and finally dividing them by m!, we have the so-called mth-order deformation equation
Where
and
It should be emphasized that is xi,m (t) m ≥ 1 governed by the linear equation (A7) with the linear boundary conditions that come from the original problem. Due to the existence of the factor  ,more chances for convergence may occur or even much faster convergence can be obtained better than standard HAM. It should be noted that in the case of n = 1 in equation (A2), standard HAM can be reached.
| References | | |
| 1. | Wilson ME. Infectious diseases: An ecological perspective. Br Med J 1998;311:1681-4.  |
| 2. | Martens P, Hall L. Malaria on the move: Human population movement and malaria transmission. Emerg Infect Dis 2000;6:103-9. |
| 3. | Singh K, Wester WC, Gordon M, Trenholme GM. Problems in the therapy for imported malaria in the United States. Arch Intern Med 2003;163:2027-30. |
| 4. | Lopez-velez R, Huerga H, Turrientes MC. Infectious diseases in immi-grants from the perspective of a tropical medicine referral unit. Am J Trop Med Hyg 2003;69:115-21. |
| 5. | Tumwiine J, Mugisha JY, Luboobi LS. A host-vector model for malaria with infective immigrants. J Math Anal Appl 2010;361:139-49. |
| 6. | Ross R. The Prevention of Malaria. London: John Murray; 1911. p. 651-86. |
| 7. | Macdonald G. The Epidemiology and Control of Malaria. London: Oxford University Press; 1957. |
| 8. | Labadin J, Kon C, Juan SF. Deterministic Malaria Transmission model with Acquired Immunity, Proceedings of the World Congress on Engineering and Computer Science. Vol II WCECS. USA: San Francisco; 2009. |
| 9. | Dietz K, Molineaux L, Thomas A. A malaria model tested in Africa Savannah, Bull. World Health Organ 1974;50:347-57. |
| 10. | Ngwa GA, Shu WS. A mathematical model for the endemic malaria with variable human and mosquito populations. Math Comput Model 2000;32:747-63. |
| 11. | Yang HM. Malaria transmission model for different levels of acquired immunity and temperature-dependent parameters (vector). Rev Saude Publica 2000;34:223-31. |
| 12. | Nakul C, Cushing JM, Hyman JM. Bifurcation analysis of a mathematical model for malaria transmission. Siam J Appl Math 2006;67:24-45. |
| 13. | Chiyaka C, Garira W, Dube S. Transmission model of endemic human malaria in a partially immune population. Math Comp Model 2007;46:806-22. |
| 14. | Tumwiine J, Mugisha JY, Luboobi LS. A mathematical model for the dynamics of malaria in a human host and mosquito vector with temporary immunity. Appl Math Comp 2007;189:1953-65. |
| 15. | Koella JC, Antia R. Epidemiological models for the spread of anti-malarial resistance. Malaria J 2003;2:3. |
| 16. | Anderson RM, May RM. Infectious Diseases of Humans: Dynamics and Control. Oxford: Oxford University Press; 1991. |
| 17. | Al-Rahman M, El-Nor O, Adu IK. Simple mathematical model for malaria transmission. J Adv Math Comp Sci 2017;25:1-24. |
| 18. | Eswari A, Rajendran L, Saravana Kumar S. Computational and mathematical analysis of fuzzy quota harvesting model in fuzzy environment using homotopy perturbation method. Appl Math Inf Sci 2019a; 13:239-45. |
| 19. | Eswari A, Saravana Kumar S. Mathematical analysis of Dengue fever infection in India. Adv Math Sci J 2019b; 8:641-50. |
| 20. | Eswari A, Saravanakumar S, Varadha Raj S, Sabari Priya V. Analysis of mathematical modelling the depletion of forestry resource: Effects of population and industrialization. Matrix Sci Math 2019c; 3:22-6. |
| 21. | Eswari A. Mathematical contribution of analytical solution in biological control using asymptotic approach. Discont Nonlinearity Compl 2020;9:287-95. |
| 22. | El-Tawil MA, Huseen SN. The q-homotopy analysis method (q-HAM). Int J Appl Math Mech 2012;8:51-75. |
| 23. | Bataineh AS, Noorani MS, Hashim I. Direct solution of nth-order IVPs by q-Homotopy Analysis method. J Diff Eq Nonlinear Mech 2009;12:1-15. |
[Figure 1], [Figure 2]
[Table 1]
|
Search Pubmed for