[1] M. Arnst, C. Soize, and K. Bulthuis, “Computation of Sobol indices in global sensitivity analysis from small data sets by probabilistic learning on manifolds,” Int. J. Uncertain. Quantif., vol. 11, no. 2, pp. 131-145 (2021).
[2] R. Barrio, “Sensitivity analysis of ODEs/DAEs using the Taylor series method,” SIAM, vol. 27, no. 6, pp. 1929–1947 (2006).
[3] V. Bhatnagar, R. C. Poonia, P. Nagar, S. Kumar, V. Singh, L. Raja, and P. Dass, “Descriptive analysis of COVID-19 patients in the context of India,” J. Interdiscip. Math., vol. 24, no. 3, pp. 489–504 (2021).
[4] S. Boatto, C. Bonnet, B. Cazelles, and F. Mazenc, “SIR model with time dependent infectivity parameter: approximating the epidemic attractor and the importance of the initial phase,” (2018). [Online]. Available: https://hal-01677886.
[5] C. Castillo-Chavez and B. Song, “Dynamical models of tuberculosis and their applications,” Math. Biosci. Eng., vol. 1, no. 2, pp. 361 (2004).
[6] C. Chauvière and H. Djellout, “An efficient spectral method for the numerical solution to some classes of stochastic differential equations,” Math. Methods Appl. Sci., vol. 44, no. 7, pp. 5888–5907 (2021).
[7] B. M. Chen-Charpentier and D. Stanescu, “Epidemic models with random coefficients,” Math. Model. Comput., vol. 52, no. 7-8, pp. 1004–1010 (2010).    
[8] G. Chowell, P. W. Fenimore, M. A. Castillo-Garsow, and C. Castillo-Chavez, “SARS outbreaks in Ontario, Hong Kong and Singapore: the role of diagnosis and isolation as a control mechanism,” J. Theor. Biol., vol. 224, no. 1, pp. 1–8 (2003).
[9] G. Dahlquist and A. Björck, Numerical methods in scientific computing, vol. I, Philadelphia, PA: SIAM (1974).
[10] P. J. Davis and P. Rabinowitz, Methods of numerical integration, Mineola, NY: Dover Publications, Inc. (2007).
[11] O. Diekmann, J. A. P. Heesterbeek, and M. G. Roberts, “The construction of next-generation matrices for compartmental epidemic models,” J. R. Soc. Interface, vol. 7, no. 47, pp. 873–885 (2010).
[12] G. H. Golub and J. H. Welsch, “Calculation of Gauss quadrature rules,” Math. Comp., vol. 23, pp. A1–A10 (1969).
[13] D. Gottlieb and S. A. Orszag, Numerical analysis of spectral methods: theory and applications, Philadelphia, PA: SIAM (1977).
[14] S. Jiao and M. Huang, “An SIHR epidemic model of the COVID-19 with general population size dependent contact rate,” Aims Mathematics, vol. 5, no. 6, pp. 6714–6725 (2020).
[15] W. O. Kermack and A. G. McKendrick, “A contribution to the mathematical theory of epidemics,” Proc. Math. Phys. Eng., vol. 115, no. 772, pp. 700–721 (1927).
[16] S. U. Khan and I. Ali, “Numerical analysis of stochastic SIR model by Legendre spectral collocation method,” Adv. Mech. Eng., vol. 11, no. 7, pp. 1687814019862918 (2019).
[17] P. E. Kloeden and V. Kozyakin, “The dynamics of epidemiological systems with nonautonomous and random coefficients,” MESA, vol. 2, no. 2, pp. 159–172 (2011).
[18] Z. Ma, Dynamical modeling and analysis of epidemics, World Scientific (2009).
[19] M. Martcheva, An introduction to mathematical epidemiology, vol. 61, Springer (2015).
[20] J. Morio, “Global and local sensitivity analysis methods for a physical system,” Eur. J. Phys., vol. 32, no. 6, pp. 1577 (2011).
[21] S. T. Pinho, C. P. Ferreira, L. Esteva, F. R. Barreto, V. C. Morato e Silva, and M. G. L. Teixeira, “Modelling the dynamics of dengue real epidemics,” Philos. Trans. Royal Soc. A., vol. 368, no. 1933, pp. 5679–5693 (2010).
[22] S. Rahman, “Extended polynomial dimensional decomposition for arbitrary probability distributions,” J. Eng. Mech., vol. 135, no. 12, pp. 1439–1451 (2009).
[23] M. A. Safi, M. Imran, and A. B. Gumel, “Threshold dynamics of a non-autonomous SEIRS model with quarantine and isolation,” Theory in Biosciences, vol. 131, no. 1, pp. 19–30 (2012).
[24] F. Santonja and B. Chen-Charpentier, “Uncertainty quantification in simulations of epidemics using polynomial chaos,” Comput. Math. Methods Med. (2012).
[25] V. Singh, R. C. Poonia, S. Kumar, P. Dass, P. Agarwal, V. Bhatnagar, and L. Raja, “Prediction of COVID-19 corona virus pandemic based on time series data using support vector machine,” J. Discret. Math. Sci. Cryptogr., vol. 23, no. 8, pp. 1583–1597 (2020).
[26] I. M. Sobol, “Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates,” Math. Comput. Simulation, vol. 55, no. 1-3, pp. 271–280 (2001).
[27] T. Tian, J. Zhang, S. Lin, Y. Jiang, J. Tan, Z. Li, and X. Wang, “Data-driven analysis of the simulations of the spread of COVID-19 under different interventions of China,” J. Appl. Stat., pp. 1–14 (2021).
[28] T. Turányi, “Sensitivity analysis of complex kinetic systems. Tools and applications,” J. Math. Chem., vol. 5, no. 3, pp. 203–248 (1990).
[29] P. Van den Driessche and J. Watmough, “Reproduction numbers and sub-threshold endemic equilibria for compartmental models of disease transmission,” Math. Biosci., vol. 180, no. 1-2, pp. 29–48 (2002).
[30] J. Wallinga and M. Lipsitch, “How generation intervals shape the relationship between growth rates and reproductive numbers,” Proc. R. Soc. B: Biol. Sci., vol. 274, no. 1609, pp. 599–604 (2007).
[31] F. Xiong, S. Chen, and Y. Xiong, “Dynamic system uncertainty propagation using polynomial chaos,” Chin. J. Aeronaut., vol. 27, no. 5, pp. 1156–1170 (2014).
[32] Z. Zhang, R. Gul, and A. Zeb, “Global sensitivity analysis of COVID-19 mathematical model,” Alex. Eng. J., vol. 60, no. 1, pp. 565–572, 2021.