Modeling Sigmoidal Growth Curves to Study the Confirmed Cases of COVID-19 in Egypt

نوع المستند : المقالة الأصلية

المؤلفون

1 Department of Statistics, Faculty of Commerce (Girls’ Branch) Al-Azhar University, Tafahna Al-Ashraf, Egypt

2 Department of Statistics, Faculty of Commerce (Girls’ Branch) Al-Azhar University, Cairo, Egypt

المستخلص

Sigmoid growth models play an important role in describing many natural events that have a sigmoidal curve (S-shaped). In this paper, the two sigmoid growth models based on Burr Type XII distribution called the Burr 1 Type XII and Burr 2 Type XII sigmoid growth models are proposed to be able to describe various situations with accuracy. The methods of estimation of the non-linear least squares and maximum likelihood are used to estimate the parameters of the proposed models. The performance of the new proposed models is investigated and compared with the classical sigmoid growth, Brody and Weibull models in describing the growth of confirmed new cases of COVID-19 in Egypt. The results showed that the new proposed model, Burr 1 Type XII sigmoidal growth is superior over the other models with respect to the coefficient of determination , mean squared error, root mean squared error, model efficiency, and the Akaike information corrected criterion especially when NLS estimation is used.

References
Amarti, Z., Nurkholipah, N. S., Anggriani, N., and Supriatna, A. K. (2018). Numerical solution of a logistic growth model for a population with Allee effect considering fuzzy initial values and fuzzy parameters. IOP Conference Series: Materials Science and Engineering, 332, 1-9.
Archontoulis, S. V., and Miguez, F. E. (2015). Nonlinear regression models and applications in agricultural research. Agronomy Journal, 107(2), 786-798.
Bertalanffy, L. V. (1957). Quantitative laws in metabolism and growth. The Quantitative Review of Biology, 32(3), 217-231.
Brody, S. (1945). Bioenergetics and growth. Rheinhold Publishing, New York.
Burr, I.W. (1942). Cumulative frequency functions. The Annals of Mathematical Statistics, 13, 215-232.
Cao, L., Shi, P.-J., Li, L., and Chen, G. (2019). A new flexible sigmoidal growth model. Symmetry, 11(2), 1-16.
Fekedulegn, D., Mac Siurtain, M. P., and Colbert, J. J. (1999). Parameter estimation of nonlinear growth models in forestry. Silva Fennica, 33(4), 327-336.
Fernandes, T. J., Pereira, A. A., and Muniz, J. A. (2017). Double sigmoidal models describing the growth of coffee berries. Ciência Rural, 47(8), 1-7.
France, J., Dijkstra, J., and Dhanoa, MS. (1996). Growth functions and their application in animal science. Annales de zootechnie, 45, 165-174.
Gavin, H. P. (2017). The Levenberg-Marquardt method for nonlinear least squares curve-fitting problems. Department of Civil and Environmental Engineering, Duke University, 1-15.
Ghaderi-Zefrehei, M., Rafeie, F., Behzadi, M. R. B., Nazari, S., Muhaghaghegh-Dolatabady, M., Samadian, F., Maxwell, T. M. R., and Najafabadi, H. A. (2018). Simple hierarchical and general nonlinear growth modeling in sheep. Turkish Journal of Veterinary and Animal Science, 42, 326-334.
Goshu, A. T., and Koya, P. R. (2013). Derivation of inflection points of nonlinear regression curves-implications to statistics. American Journal of Theoretical and Applied Statistics, 2(6), 268-272.
Kumar, D. (2017). The Burr Type XII distribution with some statistical properties. Journal of Data Science, 15(3), 509-534.
Mahanta, D. J., and Borah, M. (2014). Parameter estimation of Weibull growth models in forestry. International Journal of Mathematics Trends and Technology, 8(3), 157-163.
Malott, C. M. (1990). Maximum likelihood methods for nonlinear regression models with compound-symmetric error covariance. (Ph.D`s Dissertation). University of North Carolina Chapel Hill.
Maruyama, K., Vinyard, B., Akbar, M. K., Shafer, D. J., and Turk, C. M. (2001). Growth curve analyses in selected duck lines. British Poultry Science, 42, 574-582.
Narushin, V. G., and Takma, C. (2003). Sigmoid model for the evaluation of growth and production curves in laying hens. Bio systems Engineering, 84(3), 343-348.
Omori, R., Mizumoto, K., and Chowell, G. (2020). Changes in testing rates could mask the novel coronavirus disease (COVID-19) growth rate. International Journal of Infectious Diseases, 94,116-118.
Ratkowsky, D. A. (1983). Nonlinear regression modeling: a unified practical approach. Marcel Dekker, Inc., New York.
Ribeiro, T. D., Savian, T. V., Fernandes, T. J., and Muniz, J. A. (2018). The use of the nonlinear models in the growth of pears of ‘Shinseiki’ cultivar. Ciência Rural, 48(1), 1-7.
Richards, F. J. (1959). A flexible growth functions for empirical use. Journal of Experimental Botany, 10(29), 290-300.
Seber, G. A. F., and Wild, C. J. (2003). Nonlinear regression. John Wiley and Sons, Inc., Hoboken, New Jersey.
Shen, C. Y. (2020). Logistic growth modelling of COVID-19 proliferation in China and its international implications. International Journal of Infectious Diseases. 96, 582-589.
Souza, F. A. C., Fernandes, T. J., de Moura, R. S., Meirelles, S. L. C., Ribeiro, R. A., Cunha, F. O., and Muniz, J. A. (2017). Nonlinear modeling growth body weight of Mangalarga Marchador horses. Ciência Rural, 47(4), 1-6.
Tjørve, K. M. C., and Tjørve, E. (2017). The use of Gompertz models in growth analyses, and new Gompertz-model approach: an addition to the unified-Richards family. PLoS ONE, 12(6), 1-17.
The Ministry of Health and Population of Egypt. (2020). COVID-19 situation report [Internet]. Available from:
Tsoularis, A., and Wallace, J. (2002). Analysis of logistic growth models. Mathematical Biosciences, 179, 21-55.
Ukalska, J., and Jastrzebowski, S. (2019). Sigmoid growth curves, a new approach to study the dynamics of the epicotyl emergence of oak. Folia Forestalia Polonica, 61(1), 30-41.
Utsunomiya, Y. T., Utsunomiya, A. T. H., Torrecilha, R. B. P., Paulan, S. de C., Milanesi, M., and Garcia, J. F. (2020). Growth rate and acceleration analysis of the COVID-19 pandemic reveals the effect of public health measures in real time. Frontiers in Medicine, 7(247), 1-9.
Vrána, J., Remeš, V., Matysioková, B., Tjørve, K. M. C., and Tjørve, E. (2019). Choosing the right sigmoid growth function using the unified‐models approach. International Journal of Avian Science, 161, 13-26.