[1] Ahmedin J, Freddie B, Melissa M C, et al. Global cancer statistics. CA:A Cancer Journal for Clinicians, 2011, 61(2):69-90
[2] Freddie B, Jacques F, Isabelle S, et al. Global cancer statistics 2018:globocan estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA:A Cancer Journal for Clinicians, 2018, 68(6):394-424
[3] World Health Organization. World health statistics 2016:mMonitoring hHealth for the SDGs sustainable development goals. Geneva, Switzerland:World Health Organization, 2016
[4] Lindsey A T, Freddie B, Rebecca L S, et al. Global cancer statistics, 2012. CA:A Cancer Journal for Clinicians, 2015, 65(2):87-108
[5] Marek B, Monika J P. Stability analysis of the family of tumour angiogenesis models with distributed time delays. Commun Nonlinear Sci Numer Simul, 2016, 31(1/8):124-142
[6] Liu X D, Li Q Z, Pan J X. A deterministic and stochastic model for the system dynamics of tumor-immune responses to chemotherapy. Physica A:Statistical Mechanics and its Applications, 2018, 500:162-176
[7] Bellomo N, Preziosi L. Modelling and mathematical problems related to tumor evolution and its interaction with the immune system. Math Comput Modell, 2000, 32(3/8):413-452
[8] Pillis L G D, Radunskaya A E, Wiseman C L. A validated mathematical model of cell-mediated immune response to tumor growth. Cancer Res, 2005, 65:235-252
[9] Radouane Y. Hopf bifurcation in differential equations with delay for tumor-immune system competition model. SIAM J Appl Math, 2007, 67(6):1693-1703
[10] Saleem M, Tanuja A. Chaos in a tumor growth model with delayed responses of the immune system. J Appl Math, 2012, 2012:1-16
[11] Wang S L, Wang S L, Song X Y. Hopf bifurcation analysis in a delayed oncolytic virus dynamics with continuous control. Nonlinear Dynam, 2012, 67(1):629-640
[12] Dong Y P, Rinko M, Yasuhiro T. Mathematical modeling on helper t cells in a tumor immune system. Discrete Contin Dyn Syst -Ser B, 2014, 19(1):55-72
[13] Fuat G, Senol K, Ilhan O, Fatma B. Stability and bifurcation analysis of a mathematical model for tumor cimmune interaction with piecewise constant arguments of delay. Chaos Solitons Fractals, 2014, 68:169-179
[14] Subhas K, Sandip B. Stability and bifurcation analysis of delay induced tumor immune interaction model. Appl Math Comput, 2014, 248:652-671
[15] Rihan F A, Rahman D H A, Lakshmanan S, Alkhajeh A S. A time delay model of tumour-immune system interactions:global dynamics, parameter estimation, sensitivity analysis. Appl Math Comput, 2014, 232(1):606-623
[16] Subhas K. Bifurcation analysis of a delayed mathematical model for tumor growth. Chaos Solitons Fractals, 2015, 77:264-276
[17] Dong Y P, Huang G, Rinko M, Yasuhiro T. Dynamics in a tumor immune system with time delays. Appl Math Comput, 2015, 252:99-113
[18] Pang L Y, Shen L, Zhao Z. Mathematical modelling and analysis of the tumor treatment regimens with pulsed immunotherapy and chemotherapy. Comput Math Methods Med, 2016 (2016)
[19] López A G, Seoane J M, Sanjuán M A F. Bifurcation analysis and nonlinear decay of a tumor in the presence of an immune response. Int J Bifurcat Chaos, 2017, 27(14):1750223
[20] Ansarizadeh F, Singh M, Richards D. Modelling of tumor cells regression in response to chemotherapeutic treatment. Applied Mathematical Modelling, 2017, 48:96-112
[21] López A G, Iarosz K C, Batista A M, et al. Nonlinear cancer chemotherapy:modelling the NortonSimon hypothesis. Commun Nonlinear Sci Numer Simulat, 2019, 70:307-317
[22] Lisette G D P, Radunskaya A. The dynamics of an optimally controlled tumor model:a case study. Math Comput Modelling, 2003, 37(11):1221-1244
[23] Lisette G D P, Gu W, Fister K R, et al. Chemotherapy for tumors:an analysis of the dynamics and a study of quadratic and linear optimal controls. Math Biosci, 2007, 209(1):292-315
[24] Alberto D O, Urszula L, Helmut M, Heinz S. On optimal delivery of combination therapy for tumors. Math Biosci, 2009, 222(1):13-26
[25] Mehmet I, Metin U S, Stephen P B. Optimal control of drug therapy in cancer treatment. Nonlinear Analysis:Theory. Methods & Applications, 2009, 71(12):e1473-e1486
[26] Rihan F A, Abdelrahman D H, Al-Maskari F, et al. Delay differential model for tumour-immune response with chemoimmunotherapy and optimal control. Comput Math Methods Med, 2014 (2014):1-15
[27] Pang L Y, Zhao Z, Song X Y. Cost-effectiveness analysis of optimal strategy for tumor treatment. Chaos Solitons Fractals, 2016, 87:293-301
[28] Sarkar R R, Sandip B. Cancer self remission and tumor stability-a stochastic approach. Math Biosci, 2005, 196(1):65-81
[29] Albano G, Giorno V. A stochastic model in tumor growth. J Theoret Biol, 2006, 242(2):329-336
[30] Thomas B, Steffen T. Stochastic model for tumor growth with immunization. Phys Rev E, 2009, 79(5):051903
[31] Xu Y, Feng J, Li J J, Zhang H Q. Stochastic bifurcation for a tumor-immune system with symmetric lvy noise. Physica A:Statal Mechanics and its Applications, 2013, 392(20):4739-4748
[32] Kim K S, Kim S, Jung I H. Dynamics of tumor virotherapy:a deterministic and stochastic model approach. Stoch Anal Appl, 2016, 34(3):483-495
[33] Deng Y, Liu M. Analysis of a stochastic tumor-immune model with regime switching and impulsive perturbations. Applied Mathematical Modelling, 2020, 78:482-504
[34] Bashkirtseva I, Ryashko L, Dawson K A, et al. Analysis of noise-induced phenomena in the nonlinear tumor-mmune system. Physica A:Statal Mechanics and its Applications, 2019, 549:123923
[35] Samanta G P, Ricardo G A, Sharma S. Analysis of a mathematical model of periodically pulsed chemotherapy treatment. International Journal of Dynamics and Control, 2015, 5(3):842-857
[36] Samanta G P, Sen P, Maiti A. A delayed epidemic model of diseases through droplet infection and direct contact with saturation incidence and pulse vaccination. Systems Science and Control Engineering, 2016, 4(1):320-333
[37] Samanta G P, Ricardo G A. Analysis of a delayed epidemic model of diseases through droplet infection and direct contact with pulse vaccination. International Journal of Dynamics and Control, 2015, 3(3):275-287
[38] Samanta G P. Mathematical Analysis of a Chlamydia Epidemic Model with Pulse Vaccination Strategy. Acta Biotheoretica, 2015, 63(1):1-21
[39] Samanta G P, Sharma S. Analysis of a delayed Chlamydia epidemic model with pulse vaccination. Applied Mathematics and Computation, 2014, 230:555-569
[40] Samanta G P. Analysis of a delayed epidemic model with pulse vaccination. Chaos, Solitons and Fractals, 2014, 66:74-85
[41] Samanta G P, Bera S P. Analysis of a Chlamydia epidemic model with pulse vaccination strategy in a random environment. Nonlinear Analysis:Modelling and Control, 2018, 23(4):457-474
[42] Jing Y, Mei L Q, Song X Y, Tian W J, Ding X M. Analysis of an impulsive epidemic model with time delays and nonlinear incidence rate. Acta Mathematica Scientia, 2012, 32A(4):670-684
[43] Ling L, Liu S Y, Jiang G R. Bifurcation analysis of a SIRS epidemic model with saturating contact rate and vertical transmission. Acta Mathematica Scientia, 2014, 34A(6):1415-1425
[44] Ma Z E, Cui G R, Wang W D. Persistence and extinction of a population in a polluted environment. Math Biosci, 1990, 101:75-97
[45] Ma Z E, Hallam T G. Effects of parameter fluctuations on community survival. Math Biosci, 1987, 86(1):35-49
[46] Liu M, Wang K. Persistence and extinction in stochastic non-autonomous logistic systems. J Math Anal Appl, 2011, 375(2):443-57
[47] Li D X, Cheng F J. Threshold for extinction and survival in stochastic tumor immune system. Communications in Nonlinear ence & Numerical Simulations, 2017, 51(OCT):1-12
[48] Lan G J, Ye C, Zhang S W, Wei C J. Dynamics of a stochastic glucose-insulin model with impulsive injection of insulin. Commun Math Biol Neurosci, 2020, 2020:6
[49] Kloeden P E, Platen E. Numerical solution of stochastic differential equations. New York:Springer-Verlag, 1992
[50] Shochat E, Hart D, Agur Z. Using computer simulations for evaluating the efficacy of breast cancer chemotherapy protocols. Mathematical Models and Methods in Applied Sciences, 1999, 9(4):599-615
