Abstract

  In December 2019, an outbreak of pneumonia of unknown origin began in Wuhan, China. The causative pathogen was identified as a new strain of coronavirus, COVID-19, similar to SARS-CoV. Since then and until today, epidemiological data confirm that It is spreading worldwide at a high rate. Several vaccine strategies have been developed,but so far they have only been evaluated in animals. Currently, there is no specific antiviral therapy for CoV and the main treatments are supportive. In the same vein,easy transmissibility raises the scenario of massive spread. From the branching processes (Galton-Watson process)which are discrete stochastic processes, a population is modeled that evolves in time and in each stage the process can take whole non-negative values, which will represent the total size of the population in that period. One of the tools to achieve these prediction and warning objectives consists of the mathematical modelling of the contagious processes, more specifically, the formulation of reliable indicators to evaluate their evolution overtime. There are models to predict the evolution of each of the three populations by applying differential equations where certain boundary conditions are taken into account such as the variation of the model parameters according to some characteristics of the infection such as the infection rate,population size, duration of the infection period, etc. The present work deals with the development of models based on multi dimensional adjustment by means of polynomial equations assuming a linear dependence of the function with respect to each of the variables on which it depends in order to assess, in one way or another, the development of epidemics such as that of coronavirus (COVID- 19). Unless the mass distribution of an effective vaccine is achieved, the analysis and modeling of data yield results of great proportions.

Keywords: Estimation; Epidemiological; Througha Modelling

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Citation

Citation: Sanglier Contreras G., et al. “Estimation of the Epidemiological Evolution Througha Modelling Analysis of the Covid-19 Outbreak". Acta Scientific Microbiology 3.4 (2020): 152-158.

Copyright

Copyright: © 2020 Sanglier Contreras G., et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.