[1] M. Parsamanesh, M. Erfanian, and A. Akrami, “Modeling of the propagation of infectious diseases: mathematics and population,” Ebnesina, vol. 22, no. 4, pp. 60-74, 2021, doi: 10.22034/22.4.60 [In Persian].
[2] M. Parsamanesh and M. Erfanian, “Global dynamics of a mathematical model for propagation of infection diseases with saturated incidence rate,” J. Adv. Math. Model., vol. 11, no. 1, pp. 69-81, 2021, doi: 10.22055/jamm.2020.33801.1822 [In Persian].
[3] A. Akrami and M. Parsamanesh, “Comparison of Fuzzy and Non-Fuzzy Base Generators in an Epidemic Model for Virus Spread in Computer Networks,” Fuzzy Syst. Appl., vol. 2. No. 2, pp. 109-122, 2019. dor: 20.1001.1.27174409.1398.2.2.5.5 [In Persian].
[4] R. Akhoondi and R. Hosseini, “A Novel Fuzzy-Genetic Differential Evolutionary Algorithm for Optimization of a Fuzzy Expert Systems Applied to Heart Disease Prediction,” Soft Comput. J., vol. 6, no. 2, pp. 32-47, 2017, dor: 20.1001.1.23223707.1396.6.2.3.7 [In Persian].
[5] H. Abbasi, M. Shamsi, and A. Rasuli Kenari, “Approaches of user activity detection and a new fuzzy logic-based method to determine the risk amount of user unusual activity in the smart home,” Soft Comput. J., vol. 9, no. 2, pp. 2-13, 2020 doi: 10.22052/scj.2021.242812.0 [In Persian].
[6] H. Moradi Farahani, J. Asgari, and M. Zakeri, “A Surveying on Type-2 Fuzzy Logic: Its Genesis and Its Application,” Soft Comput. J., vol. 2, no. 1, pp. 22-43, 2013 [In Persian].
[7] M. Parsamanesh, “The role of vaccination in controlling the outbreak of infectious diseases: a mathematical approach,” Vaccine Res., vol. 5, no. 1, pp. 32-40, 2018, doi: 10.29252/vacres.5.1.32.
[8] D.L. Urso, “Coronavirus disease 2019 (COVID-19): a brief report,” Clin. Manag. Issues, vol. 14, no. 1, pp. 15–19, 2020, doi:10.7175/cmi.v14i1.1467.
[9] U.A. Leon, A. Perez, and E. Vales, “An SEIARD epidemic model for COVID-19 in Mexico: mathematical analysis and state-level forecast,” Chaos Solitons Fractals, vol. 140, pp. 110-165, 2020, doi:10.1016/j.chaos.2020.110165.
[10] K. Roosa, Y. Lee, R. Luo, A. Kirpich, R. Rothenberg, J.M. Hyman, P. Yan, and G. Chowell, “Short-term forecasts of the COVID-19 epidemic in Guangdong and Zhejiang, China,” J. Clin. Forensic Med., vol. 9, no. 2, pp. 13–23, 2020, doi:10.3390.jcm9020596.
[11] N. Nuraini, K. Khairuddin, and M. Apri, “Modeling simulation of COVID-19 in Indonesia based on early endemic data,” Commun. Biomath. Sci., vol. 3, no. 1, pp. 1–8, 2020, doi:10.5614/cbms.2020.3.1.1.
[12] A.S. Ahmar and E.B. Val, “Sutte-ARIMA: short-term forecasting method, a case: Covid-19 and stock market in Spain,” Sci. Total Environ., vol. 729, pp. 13-38, 2020, doi:10.1016/j.scitotenv.2020.138883.
[13] S. He, Y. Peng, and K. Sun, “SEIR modeling of the COVID-19 and its dynamics,” Nonlinear Dyn., vol. 101, pp. 1667–1680, 2020, doi: 10.1007/s11071-020-05743-y.
[14] A. Godio, F. Pace, and A. Vergnano, “SEIR modeling of the Italian epidemic of SARS-CoV-2 using computational swarm intelligence,” Int. J. Environ. Res. Public Health, vol. 17, no. 10, pp. 3535, 2020, doi: 10.3390/ijerph17103535.
[15] A. Ajbar and R.T.Alqahtani, “Bifurcation analysis of a SEIR epidemic system with governmental action and individual reaction,” Adv. Differ. Equ., vol. 541, 2020, doi: 10.1186/s13662-020-02997-z.
[16] S. Annas, M.I. Pratama, M. Rifandi, W. Sanusi, and S. Side, “Stability analysis and numerical simulation of SEIR model for pandemic COVID-19 spread in Indonesia,” Chaos Solitans Fractals, vol. 139, 2020, doi: 10.1016/j.chaos.2020.110072.
[17] M. Awais, F.S. Alshammari, S. Ullah, M.A. Khan, and S. Islam, “Modeling and simulation of the novel coronavirus in Caputo derivative,” Results Phys., vol. 19, 2020, doi: 10.1016/j.rinp.2020.103588.
[18] M.A. Khan and A. Atangana, “Modeling the dynamics of novel coronavirus (2019-nCov) with fractional derivative,” Alex. Eng. J., vol. 59, no. 4, pp. 2379–2389, 2020, doi: 10.1016/j.aej.2020.02.033.
[19] M.A. Khan, A. Atangana, E. Alzahrani, and Fatmawati, “The dynamics of COVID-19 with quarantined and isolation,” Adv. Differ. Equ., vol. 425, 2020, doi: 10.1186/s13662-020-02882-9.
[20] L.C. Barros, R.C. Bassanezi, and M.B.F. Leite, “The SI epidemiological models with a fuzzy transmission parameter,” Comput. Math. Appl., vol. 45, pp. 1619–1628, 2003, doi:10.1016/S0898-1221(03)00141-X.
[21] R. Jafelice, L.C. Barros, R.C. Bassanezei, and F. Gomide, “Fuzzy modeling in symptomatic HIV virus infected population,” Bull. Math. Biol., vol. 66, pp. 1597–1620, 2004, doi: 10.1016/j.bulm.2004.03.002.
[22] E. Massad, M.N. Burattini, and N.R.S. Ortega, “Fuzzy logic and measles vaccination: designing a control strategy,” Int. J. Epidemiol., vol. 28, pp. 550–557, 1999, doi: 10.1093/ije/28.3.550.
[23] E. Massad, N.R.S. Ortega, L.C. Barros, and C.J. Struchiner, Fuzzy Logic in Action: Applications in Epidemiology and Beyond, Studies in Fuzziness and Soft Computing, Springer, Berlin, 2008, doi: 10.1007/978-3-540-69094-8.
[24] R. Verma and R.K. Tiwari, Dynamical behaviors of fuzzy SIR epidemic model. In: Advances in Fuzzy Logic and Technology, Springer, 2017, doi: 10.1007/978-3-319-66827-7_45.
[25] E. Massad, N.R.S. Ortega, L.C. de Barros, and C.J. Struchiner, “Fuzzy Dynamical Systems in Epidemic Modeling,” In: Fuzzy Logic in Action: Applications in Epidemiology and Beyond. Studies in Fuzziness and Soft Computing, 232, Springer, Berlin, Heidelberg, 2008, doi: 10.1007/978-3-540-69094-8_9.
[26] P.K. Mondal, S. Jana, P. Haldar, and T.K. Kar, “Dynamical behavior of an epidemic model in a fuzzy transmission,” Int. J. Uncertain. Fuzziness Knowl.-Based Syst., vol. 23, no. 5, pp. 651–665, 2015, doi: 10.1142/S0218488515500282.
)