×

Mathematical modeling and projections of a vector-borne disease with optimal control strategies: a case study of the Chikungunya in Chad. (English) Zbl 1498.92187

Summary: In this work, we extend existing models of vector-borne diseases by including density-dependent rates and some existing control mechanisms to decrease the disease burden in the human population. We begin by analyzing the vector model dynamics and by determining the offspring reproductive number denoted by \(\mathcal{N}\) as well as the trivial and nontrivial equilibria. Using theory of cooperative systems and the general theory of Lyapunov, we prove that, although there is a possibility that the trivial equilibrium coexists with a positive equilibrium, it remains globally asymptotically stable whenever \(\mathcal{N}\leq 1\). The fact that the non-trivial equilibrium is globally asymptotically stable permits us to reduce the study of the full model to the study of a reduced model whenever \(\mathcal{N}>1\). Thus, we analyze the reduced model by computing the basic reproduction number \(\mathcal{R}_0\), equilibrium points as well as asymptotic stability of each equilibrium point. We also explore the nature of the bifurcation for the disease-free equilibrium from \(\mathcal{R}_0=1\). By the application of the centre manifold theory, we prove that the backward bifurcation phenomenon can occur in our model, which means that the necessary condition \(\mathcal{R}_0<1\) is not sufficient to guarantee the final extinction of the disease in human populations. To calibrate our model, we estimate model parameters on clinical data from the last Chikungunya epidemic which occurred in Chad, using the non-linear least-square method. We find out that \(\mathcal{R}_0=1.8519\), which means that we are in an endemic state since \(\mathcal{R}_0>1\). To determine model parameters that are responsible for disease spread in the human community, we perform sensitivity analysis (SA) using a global method. It follows that the density-dependent death rate of mosquitoes and the average number of mosquito bites are key parameters in the disease dynamics. Following this, we thus formulate an optimal control model by including in the autonomous model, four time-dependent control functions to fight the disease spread. Pontryagin’s maximum principle is used to characterize our optimal controls. Numerical simulations, using parameter values of Chikungunya transmission dynamics, and efficiency analysis, are conducted to determine the better control strategy which guaranteed the final extinction of the disease in human populations.

MSC:

92D30 Epidemiology
34C60 Qualitative investigation and simulation of ordinary differential equation models
34H20 Bifurcation control of ordinary differential equations
37N25 Dynamical systems in biology
49K15 Optimality conditions for problems involving ordinary differential equations
49N90 Applications of optimal control and differential games

Software:

EnKF
Full Text: DOI

References:

[1] World Health Organization. A global brief on vector-borne diseases; 2014. http://apps.who.int/iris/bitstream/10665/111008/1/WHO_DCO_WHD_2014.1_eng.pdf.
[2] Agrawal, V.; Sashindran, V., Lymphatic filariasis in india: problems, challenges and new initiatives, Medical Journal Armed Forces India, 62, 4, 359-362 (2006)
[3] Mani, T.; Rajendran, R.; Sunish, I.; Munirathinam, A.; Arunachalam, N.; Satyanarayana, K., Effectiveness of two annual, single-dose mass drug administrations of diethylcarbamazine alone or in combination with albendazole on soil-transmitted helminthiasis in filariasis elimination programme, Tropical Medicine & International Health, 9, 9, 1030-1035 (2004)
[4] Morel, C. M.; Lauer, J. A.; Evans, D. B., Cost effectiveness analysis of strategies to combat malaria in developing countries, BMJ, 331, 7528, 1299 (2005)
[5] Richard-Lenoble, D.; Chandenier, J.; Gaxotte, P., Ivermectin and filariasis, Fundamental & clinical pharmacology, 17, 2, 199-203 (2003)
[6] Souares, A.; Lalou, R.; Sene, I.; Sow, D.; Le Hesran, J.-Y., Adherence and effectiveness of drug combination in curative treatment among children suffering uncomplicated malaria in rural senegal, Trans R Soc Trop Med Hyg, 102, 8, 751-758 (2008)
[7] Weiss, W. R.; Oloo, A. J.; Johnson, A.; Koech, D.; Hoffman, S. L., Daily primaquine is effective for prophylaxis against falciparum malaria in kenya: comparison with mefloquine, doxycycline, and chloroquine plus proguanil, Journal of Infectious Diseases, 171, 6, 1569-1575 (1995)
[8] Lacour, G.; Chanaud, L.; L’Ambert, G.; Hance, T., Seasonal synchronization of diapause phases in aedes albopictus (diptera: culicidae), PLoS ONE, 10, 12, e0145311 (2015)
[9] Lacour, G.; Vernichon, F.; Cadilhac, N.; Boyer, S.; Lagneau, C.; Hance, T., When mothers anticipate: effects of the prediapause stage on embryo development time and of maternal photoperiod on eggs of a temperate and a tropical strains of aedes albopictus (diptera: culicidae), J Insect Physiol, 71, 87-96 (2014)
[10] Moulay, D., Modélisation et analyse mathématique de systèmes dynamiques en épidémiologie. application au cas du chikungunya. mathématiques [math] (2011), PhD Thesis Université du Havre, 2011. Français.< tel-00633827
[11] Zeller, M.; Koella, J. C., Effects of food variability on growth and reproduction of a edes aegypti, Ecol Evol, 6, 2, 552-559 (2016)
[12] Mastrantonio, V.; Crasta, G.; Puggioli, A.; Bellini, R.; Urbanelli, S.; Porretta, D., Cannibalism in temporary waters: simulations and laboratory experiments revealed the role of spatial shape in the mosquito aedes albopictus, PLoS ONE, 13, 5, e0198194 (2018)
[13] Aslan, O.; Altan, A.; Hacıoğlu, R., The control of blast furnace top gas pressure by using fuzzy pid, Proceedings of the fifth international conference on advances in mechanical and robotics engineering-AMRE, 22-26 (2017)
[14] Aydogan, M. S.; Baleanu, D.; Mousalou, A.; Rezapour, S., On high order fractional integro-differential equations including the caputo-fabrizio derivative, Boundary Value Problems, 2018, 1, 1-15 (2018) · Zbl 1499.34400
[15] Baleanu, D.; Etemad, S.; Rezapour, S., A hybrid caputo fractional modeling for thermostat with hybrid boundary value conditions, Boundary Value Problems, 2020, 1, 1-16 (2020) · Zbl 1495.34006
[16] Baleanu, D.; Mousalou, A.; Rezapour, S., On the existence of solutions for some infinite coefficient-symmetric caputo-fabrizio fractional integro-differential equations, Boundary Value Problems, 2017, 1, 1-9 (2017) · Zbl 1377.45004
[17] Abboubakar, H.; Kumar, P.; Erturk, V. S.; Kumar, A., A mathematical study of a tuberculosis model with fractional derivatives, International Journal of Modeling, Simulation, and Scientific Computing, 2150037 (2021)
[18] Anderson, R. M., The population dynamics of infectious diseases: theory and applications (2013), Springer
[19] Baleanu, D.; Jajarmi, A.; Mohammadi, H.; Rezapour, S., A new study on the mathematical modelling of human liver with caputo-fabrizio fractional derivative, Chaos, Solitons & Fractals, 134, 109705 (2020) · Zbl 1483.92041
[20] Baleanu, D.; Mohammadi, H.; Rezapour, S., Analysis of the model of HIV-1 infection of CD4+ T-cell with a new approach of fractional derivative, Advances in Difference Equations, 2020, 1, 1-17 (2020) · Zbl 1482.37090
[21] Ma, Z.; Zhou, Y.; Wu, J., Modeling and dynamics of infectious diseases, 11 (2009), World Scientific · Zbl 1180.92081
[22] Mohammadi, H.; Kumar, S.; Rezapour, S.; Etemad, S., A theoretical study of the caputo-fabrizio fractional modeling for hearing loss due to mumps virus with optimal control, Chaos, Solitons & Fractals, 144, 110668 (2021)
[23] Taylor, R. A.; Mordecai, E. A.; Gilligan, C. A.; Rohr, J. R.; Johnson, L. R., Mathematical models are a powerful method to understand and control the spread of huanglongbing, PeerJ, 4, e2642 (2016)
[24] Ross, R., Some quantitative studies in epidemiology, Nature, 87, 2188, 466-467 (1911) · JFM 42.0336.01
[25] Blayneh, K.; Cao, Y.; Kwon, H.-D., Optimal control of vector-borne diseases: treatment and prevention, Discrete & Continuous Dynamical Systems-B, 11, 3, 587 (2009) · Zbl 1162.92034
[26] Blayneh, K. W.; Gumel, A. B.; Lenhart, S.; Clayton, T., Backward bifurcation and optimal control in transmission dynamics of west nile virus, Bull Math Biol, 72, 4, 1006-1028 (2010) · Zbl 1191.92024
[27] Chitnis, N.; Cushing, J. M.; Hyman, J., Bifurcation analysis of a mathematical model for malaria transmission, SIAM J Appl Math, 67, 1, 24-45 (2006) · Zbl 1107.92047
[28] Bellan, S. E., The importance of age dependent mortality and the extrinsic incubation period in models of mosquito-borne disease transmission and control, PLoS ONE, 5, 4 (2010)
[29] Gurtin, M. E.; MacCamy, R. C., Some simple models for nonlinear age-dependent population dynamics, Math Biosci, 43, 3-4, 199-211 (1979) · Zbl 0397.92025
[30] Gurtin, M. E.; MacCamy, R. C., Product solutions and asymptotic behavior for age-dependent, dispersing populations, Math Biosci, 62, 2, 157-167 (1982) · Zbl 0505.92019
[31] Stukalin, E. B.; Aifuwa, I.; Kim, J. S.; Wirtz, D.; Sun, S. X., Age-dependent stochastic models for understanding population fluctuations in continuously cultured cells, Journal of the royal society interface, 10, 85, 20130325 (2013)
[32] Bano-Zaidi, M.; Aguayo-Romero, M.; Campos, F. D.; Colome-Ruiz, J.; Gonzalez, M. E.; Piste, I. M., Typhoid fever outbreak with severe complications in Yucatan, Mexico, The Lancet Global Health, 6, 10, e1062-e1063 (2018)
[33] Brainard, J.; D’hondt, R.; Ali, E.; Van den Bergh, R.; De Weggheleire, A.; Baudot, Y., Typhoid fever outbreak in the democratic republic of congo: case control and ecological study, PLoS Negl Trop Dis, 12, 10, e0006795 (2018)
[34] Kim, S.; Lee, K. S.; Pak, G. D.; Excler, J.-L.; Sahastrabuddhe, S.; Marks, F., Spatial and temporal patterns of typhoid and paratyphoid fever outbreaks: a worldwide review, 1990-2018, Clinical Infectious Diseases, 69, Supplement_6, S499-S509 (2019)
[35] Gumel, A. B.; Ruan, S.; Day, T.; Watmough, J.; Brauer, F.; Van den Driessche, P., Modelling strategies for controlling sars outbreaks, Proceedings of the Royal Society of London Series B: Biological Sciences, 271, 1554, 2223-2232 (2004)
[36] Maunder, R.; Hunter, J.; Vincent, L.; Bennett, J.; Peladeau, N.; Leszcz, M., The immediate psychological and occupational impact of the 2003 sars outbreak in a teaching hospital, CMAJ, 168, 10, 1245-1251 (2003)
[37] Wang, W.; Ruan, S., Simulating the sars outbreak in beijing with limited data, J Theor Biol, 227, 3, 369-379 (2004) · Zbl 1439.92185
[38] Haagmans, B. L.; van den Brand, J. M.; Raj, V. S.; Volz, A.; Wohlsein, P.; Smits, S. L., An orthopoxvirus-based vaccine reduces virus excretion after MERS-CoV infection in dromedary camels, Science, 351, 6268, 77-81 (2016)
[39] Tang, X.-C.; Agnihothram, S. S.; Jiao, Y.; Stanhope, J.; Graham, R. L.; Peterson, E. C., Identification of human neutralizing antibodies against mers-cov and their role in virus adaptive evolution, Proceedings of the National Academy of Sciences, 111, 19, E2018-E2026 (2014)
[40] World Health Organization and others, Middle east respiratory syndrome coronavirus (MERS-COV): current situation 3 years after the virus was first identified, Weekly Epidemiological Record= Relevé épidémiologique hebdomadaire, 90, 20, 245-250 (2015)
[41] Yong, B.; Owen, L., Dynamical transmission model of MERS-COV in two areas, AIP Conference Proceedings, 1716, 020010 (2016), AIP Publishing LLC
[42] Djaoue, S.; Kolaye, G. G.; Abboubakar, H.; Ari, A. A.A.; Damakoa, I., Mathematical modeling, analysis and numerical simulation of the covid-19 transmission with mitigation of control strategies used in cameroon, Chaos, Solitons & Fractals, 110281 (2020)
[43] Liu, Z.; Magal, P.; Seydi, O.; Webb, G., Understanding unreported cases in the covid-19 epidemic outbreak in wuhan, china, and the importance of major public health interventions, Biology (Basel), 9, 3, 50 (2020)
[44] Nabi, K. N.; Abboubakar, H.; Kumar, P., Forecasting of covid-19 pandemic: from integer derivatives to fractional derivatives, Chaos, Solitons & Fractals, 110283 (2020) · Zbl 1496.92122
[45] Peng, L.; Yang, W.; Zhang, D.; Zhuge, C.; Hong, L., Epidemic analysis of covid-19 in china by dynamical modeling (2020), arXiv:200206563
[46] Ai, S.; Li, J.; Lu, J., Mosquito-stage-structured malaria models and their global dynamics, SIAM J Appl Math, 72, 4, 1213-1237 (2012) · Zbl 1267.34076
[47] Anguelov, R.; Dumont, Y.; Yatat Djeumen, I. V., Sustainable vector/pest control using the permanent sterile insect technique, Math Methods Appl Sci (2020) · Zbl 1472.34083
[48] Moulay, D.; Aziz-Alaoui, M.; Cadivel, M., The chikungunya disease: modeling, vector and transmission global dynamics, Math Biosci, 229, 1, 50-63 (2011) · Zbl 1208.92044
[49] Traoré, B.; Koutou, O.; Sangaré, B., A global mathematical model of malaria transmission dynamics with structured mosquito population and temperature variations, Nonlinear Anal Real World Appl, 53, 103081 (2020) · Zbl 1430.92112
[50] Moulay, D.; Aziz-Alaoui, M.; Kwon, H.-D., Optimal control of chikungunya disease: larvae reduction, treatment and prevention, Mathematical Biosciences & Engineering, 9, 2, 369-392 (2012) · Zbl 1260.92068
[51] Altan, A.; Hacıoğlu, R., Model predictive control of three-axis gimbal system mounted on UAV for real-time target tracking under external disturbances, Mech Syst Signal Process, 138, 106548 (2020)
[52] Altan, A.; Karasu, S.; Zio, E., A new hybrid model for wind speed forecasting combining long short-term memory neural network, decomposition methods and grey wolf optimizer, Appl Soft Comput, 100, 106996 (2021)
[53] Awad, M.; Khanna, R., Support vector regression, Efficient learning machines, 67-80 (2015), Springer
[54] Evensen, G., The ensemble kalman filter: theoretical formulation and practical implementation, Ocean Dyn, 53, 4, 343-367 (2003)
[55] Han, T.; Gois, F. N.B.; Oliveira, R.; Prates, L. R.; de Almeida Porto, M. M., Modeling the progression of covid-19 deaths using Kalman filter and automl, Soft comput, 1-16 (2021)
[56] Hoteit, I.; Pham, D.-T.; Blum, J., A simplified reduced order Kalman filtering and application to altimetric data assimilation in tropical pacific, J Mar Syst, 36, 1-2, 101-127 (2002)
[57] Kalman, R. E., A new approach to linear filtering and prediction problems [j], Journal of basic Engineering, 82, 1, 35-45 (1960)
[58] Karasu, S.; Altan, A.; Bekiros, S.; Ahmad, W., A new forecasting model with wrapper-based feature selection approach using multi-objective optimization technique for chaotic crude oil time series, Energy, 212, 118750 (2020)
[59] Sciences et Techniques; 2021. http://www.ferdinandpiette.com/blog/2011/04/le-filtre-de-kalman-interets-et-limites/ [internet] [accessed 05/04/2021].
[60] Johnson, M. L.; Frasier, S. G., [16] nonlinear least-squares analysis, Meth Enzymol, 117, 301-342 (1985)
[61] Ibeas, A.; De La Sen, M.; Alonso-Quesada, S.; Nistal, R., Parameter estimation of multi-staged SI (n) RS epidemic models, 2018 UKACC 12th International Conference on Control (CONTROL), 456-461 (2018), IEEE
[62] Memon, Z.; Qureshi, S.; Memon, B. R., Assessing the role of quarantine and isolation as control strategies for covid-19 outbreak: a case study, Chaos, Solitons & Fractals, 144, 110655 (2021)
[63] Samsuzzoha, M.; Singh, M.; Lucy, D., Parameter estimation of influenza epidemic model, Appl Math Comput, 220, 616-629 (2013)
[64] Pontryagin, L.; Boltyanskii, V.; Gamkrelidze, R.; Mishchenko, C., The mathematical theory of optimal control process, 4 (1986)
[65] Abboubakar, H.; Buonomo, B.; Chitnis, N., Modelling the effects of malaria infection on mosquito biting behaviour and attractiveness of humans, Ricerche di matematica, 65, 1, 329-346 (2016) · Zbl 1354.92078
[66] Abboubakar, H.; Kamgang, J. C.; Nkamba, N. L.; Tieudjo, D.; Emini, L., Modeling the dynamics of arboviral diseases with vaccination perspective, Biomath, 4, 1, 1507241 (2015) · Zbl 1373.92116
[67] World Health Organization. Dengue control; 2019. https://www.who.int/denguecontrol/human/en/.
[68] Smith, H. L., Monotone dynamical systems: an introduction to the theory of competitive and cooperative systems (2008), American Mathematical Soc.
[69] Abboubakar, H.; Kamgang, J. C.; Nkamba, L. N.; Tieudjo, D., Bifurcation thresholds and optimal control in transmission dynamics of arboviral diseases, J Math Biol, 76, 1-2, 379-427 (2018) · Zbl 1393.37098
[70] Cushing, J. M., An introduction to structured population dynamics, 71 (1998), SIAM · Zbl 0939.92026
[71] Cushing, J. M.; Yicang, Z., The net reproductive value and stability in matrix population models, Natural Resources Modeling, 8, 4, 297-333 (1994)
[72] Diekmann, O.; Heesterbeek, J., Mathematical epidemiology of infectious diseases: model building, analysis and interpretation John Wiley & Sons, Chichester, UK (2000)
[73] Van den Driessche, P.; Watmough, J., Reproduction numbers and sub-threshold endemic equilibria for compartmental models of disease transmission, Math Biosci, 180, 1-2, 29-48 (2002) · Zbl 1015.92036
[74] Abboubakar, H.; Kamgang, J. C.; Tieudjo, D., Backward bifurcation and control in transmission dynamics of arboviral diseases, Math Biosci, 278, 100-129 (2016) · Zbl 1347.34073
[75] Lord, C.; Woolhouse, M.; Heesterbeek, J.; Mellor, P., Vector-borne diseases and the basic reproduction number: a case study of african horse sickness, Med Vet Entomol, 10, 1, 19-28 (1996)
[76] Castillo-Chavez, C.; Song, B., Dynamical models of tuberculosis and their applications, Mathematical Biosciences & Engineering, 1, 2, 361 (2004) · Zbl 1060.92041
[77] Garba, S. M.; Gumel, A. B.; Bakar, M. A., Backward bifurcations in dengue transmission dynamics, Math Biosci, 215, 1, 11-25 (2008) · Zbl 1156.92036
[78] République du Tchad, Ministère de la Santé Publique. Rapport de la Situation Épidémiologique CHIKUNGUNYA; 2020. https://www.humanitarianresponse.info/en/operations/chad/health/documents.
[79] Strugarek, M.; Bossin, H.; Dumont, Y., On the use of the sterile insect release technique to reduce or eliminate mosquito populations, Appl Math Model (2018) · Zbl 1481.92117
[80] Chitnis, N.; Hyman, J. M.; Cushing, J. M., Determining important parameters in the spread of malaria through the sensitivity analysis of a mathematical model, Bull Math Biol, 70, 5, 1272 (2008) · Zbl 1142.92025
[81] http://www.mathworks.com
[82] Marino, S.; Hogue, I. B.; Ray, C. J.; Kirschner, D. E., A methodology for performing global uncertainty and sensitivity analysis in systems biology, J Theor Biol, 254, 1, 178-196 (2008) · Zbl 1400.92013
[83] Wu, J.; Dhingra, R.; Gambhir, M.; Remais, J. V., Sensitivity analysis of infectious disease models: methods, advances and their application, Journal of The Royal Society Interface, 10, 86, 20121018 (2013)
[84] Stein, M., Large sample properties of simulations using latin hypercube sampling, Technometrics, 29, 2, 143-151 (1987) · Zbl 0627.62010
[85] Abboubakar, H.; Racke, R., Mathematical modelling and optimal control of typhoid fever, Tech. Rep. (2019), University of Konstanz
[86] Abboubakar, H.; Racke, R., Mathematical modeling, forecasting, and optimal control of typhoid fever transmission dynamics, Chaos, Solitons & Fractals, 149, 111074 (2021) · Zbl 1485.93465
[87] Lukes, D. L., Differential equations: classical to controlled (1982), Elsevier · Zbl 0509.34003
[88] Joshi, H.; Lenhart, S.; Hota, S.; Agusto, F., Optimal control of an sir model with changing behavior through an education campaign, Electronic Journal of Differential Equations, 2015, 50, 1-14 (2015) · Zbl 1516.92109
[89] Di Liddo, A., Optimal control and treatment of infectious diseases. the case of huge treatment costs, Mathematics, 4, 2, 21 (2016) · Zbl 1358.92087
[90] Neilan, R. L.M.; Schaefer, E.; Gaff, H.; Fister, K. R.; Lenhart, S., Modeling optimal intervention strategies for cholera, Bull Math Biol, 72, 8, 2004-2018 (2010) · Zbl 1201.92045
[91] Posny, D.; Wang, J.; Mukandavire, Z.; Modnak, C., Analyzing transmission dynamics of cholera with public health interventions, Math Biosci, 264, 38-53 (2015) · Zbl 1371.92126
[92] Abboubakar, H.; Kamgang, J. C., Optimal control of arboviral diseases, Proceedings of CARI, 445 (2016)
[93] Lenhart, S.; Workman, J. T., Optimal control applied to biological models (2007), CRC press · Zbl 1291.92010
[94] Fleming, W.; Rishel, R.; Marchuk, G.; Balakrishnan, A.; Borovkov, A.; Makarov, V., Applications of mathematics, Deterministic and Stochastic Optimal Control (1975)
[95] Helikumi, M.; Kgosimore, M.; Kuznetsov, D.; Mushayabasa, S., Backward bifurcation and optimal control analysis of a trypanosoma brucei rhodesiense model, Mathematics, 7, 10, 971 (2019)
[96] Saleem, R.; Habib, M.; Manaf, A., Review of forward backward sweep method for bounded and unbounded control problem with payoff term., Science International, 27, 1 (2015)
[97] Elfahham, Y., Estimation and prediction of construction cost index using neural networks, time series, and regression, Alexandria Engineering Journal, 58, 2, 499-506 (2019)
[98] Walter, W., Ordinary differential equations, Springer-Verlag (1998) · Zbl 0991.34001
[99] LaSalle, J., The stability of dynamical systems, society for industrial and applied mathematics, philadelphia, pa., 1976, With an appendix: Limiting equations and stability of nonautonomous ordinary differential equations by Z. Artstein, Regional Conference Series in Applied Mathematics (1976) · Zbl 0364.93002
[100] Birkhoff, G.; Rota, G.-C., Ordinary differential equations, 3rd edition (1978), John Wiley and Sons: John Wiley and Sons New York · Zbl 0183.35601
[101] Guckenheimer, J.; Holmes, P., Nonlinear oscillations, dynamical systems, and bifurcations of vector fields, Applied Mathematical Sciences, 42 (2002), Springer-Verlag: Springer-Verlag New York
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. In some cases that data have been complemented/enhanced by data from zbMATH Open. This attempts to reflect the references listed in the original paper as accurately as possible without claiming completeness or a perfect matching.