Abstract
This paper introduces some elements of mathematical modeling to show suspected, recovered and deceased COVID-19 patients, and the chains adopted in deploying operations around the world, and it is still not definitively known when people acquire long-term immunity, but the formulation of proposed models for probability distributions allows for the definition of the finite difference scheme. Non-standard, some graphical results have been presented to some carefully selected countries. The results showed that health safety plans and isolation of infected and suspected humans, in general, is the only way so far that can reduce the risk of the spread of this epidemic in the near future, and also through statistical analysis using fitted models that revealed a high and unstable exponential growth of the number of confirmed cases. And deaths and cases that responded to treatment based on the results of experimental COVID-19 predictions, and it is expected that the number of infected cases and daily deaths will stabilize after the measures taken by most countries, and this situation will continue until the largest number of people are vaccinated in order to obtain herd immunity, and control the causes. As the epidemic spreads like human gatherings and contact, the results of this work will be useful to practitioners in various fields of theoretical and applied sciences.
References
[1] Stigler, Stephen M. (1990). The History of Statistics: The Measurement of Uncertainty before 1900. Academic Trade, booksellers, U.S. and Canada.
[2] Zeb Anwar, Alzahrani Ebraheem, Erturk Vedat Suat, and Zaman Gul. (2020). Mathematical Model for Covid-19 Disease 2019 (COVID-19) Containing Isolation Class. BioMed Research International, 145(2020), 16-30.
[3] Bachioua Lahcene. (2018). On Extended Normal Distribution Model with Application in Health Care. International Journal of Statistics in Medical Research, N(7), pp. 88-95.
[4] Bachioua Lahcene, (2019). On Extended Normal Inverse Gaussian Distribution: Theory, Methodology, Properties and Applications. American Journal of Applied Mathematics and Statistics, Vol. (7), No. (6), 224-230.
[5] Bachioua Lahcene. (2018). On Recent Modifications of Extended Rayleigh Distribution and its Applications. JP Journal of Fundamental and Applied Statistics, Vol. (12), Issue (1): pp. 1-13.
[6] Bachioua Lahcene. (2020). On Extended Exponential Distribution: Properties and Applications In Tracking the Pandemic Covid-19. SunText Review of Medical and Clinical Research, Vol. (1), No. (2), pp. 1-8.
[7] Bachioua Lahcene. (2021). A New Extended-Gamma Family of Distributions: Properties and Applications. Journal of Applied Mathematics and Computation, 5(1), pp. 9-17.
[8] Bachioua Lahcene. (2020). Extended Lognormal Distribution: Properties and Applications. World Scient i f ic News, 145(2020), 16-30.
[9] Bachioua Lahcene. (2018). On Recent Modifications of Extended Weibull Families Distributions and Its Applications. Asian Journal of Fuzzy and Applied Mathematics, Vol. (06), No. (01), February, pp. 1-11.
[10] Overtonak, Christopher E., Stagea, Helena B. (2020). Using Statistics and Mathematical Modelling to Understand Infectious Disease Outbreaks: COVID-19 as an Example. Infectious Disease Modelling, Vol. (5), pp. 409-441.
How to cite this paper
Probability Distributions Related to Modeling Epidemic Spread Data "COVID-19 Status and Developments"
How to cite this paper: Bachioua Lahcene. (2021) Probability Distributions Related to Modeling Epidemic Spread Data "COVID-19 Status and Developments". Journal of Applied Mathematics and Computation, 5(2), 134-144.
DOI: http://dx.doi.org/10.26855/jamc.2021.06.008