Acta Scientific Microbiology (ASMI) (ISSN: 2581-3226)

Review Article Volume 3 Issue 4

Estimation of the Epidemiological Evolution Througha Modelling Analysis of the Covid-19 Outbreak

Sanglier Contreras G1*, Robas Mora M2 and Jimenez Gómez P2

1Department of Architecture and Design, Construction Engineering Area, Higher Polytechnic School
2Microbiology Area, Pharmaceutical and Health Sciences Department, Faculty of Pharmacy, Universidad San Pablo CEU, Boadilla Del Monte, Madrid, Spain

*Corresponding Author: Sanglier Contreras G, Department of Architecture and Design, Construction Engineering Area, Higher Polytechnic School.

Received: February 26, 2020; Published: March 16, 2020



  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



  1. Huang C., et al. “Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China”. Lancet (2020). 
  2. World Health Organization WHO. (2019). 
  3. Zhu N., et al. “A novel coronavirus from patients with pneumonia in China, 2019”. The New England Journal of Medicine (2020).
  4. Wu P., et al. “Real-time tentative assessment of the epidemiological characteristics of novel coronavirus infections in Wuhan, China”. Eurosurveillance (2020).
  5. World Health Organization (a). WHO issues best practices for naming new human infectious (2020).         
  6. World Health Organization (b). Surveillance case definitions for human infection with novel coronavirus (nCoV) (2020).
  7. CinatlJ., et al. “Treatment of SARS with human interferons”. Lancet 362.9380 (2003): 293-294.
  8. Chan JF., et al. “A familial cluster of pneumonia associated with the 2019 novel coronavirus indicating person-to-person transmission: a study of a family cluster”. Lancet (2019).
  9. Wit E., et al. “SARS and MERS: recent insights into emerging coronaviruses”. Nature Reviews Microbiology 14.8 (2016): 523-534.
  10. Hays JN. “Epidemics and Pandemics: Their Impacts on Human History (2005).
  11. Cui J., et al. “Origin and evolution of pathogenic coronaviruses”. Nature Reviews Microbiology 17.3 (2019):181-192.
  12. Menachery VD., et al. “A SARS-like cluster of circulating bat coronaviruses shows potential for human emergence”. Nature Medicine 21.12 (2015): 1508-1513.
  13. ChanJF., et al. “Broad-spectrum antivirals for the emerging Middle East respiratory syndrome coronavirus”. Journal of Infection 67.6 (2013): 606-616.
  14. Jin-Hong Y. “The Fight against the 2019-nCoV Outbreak: An Arduous March Has Just Begun”. Journal of Korean Medical Science 3 (2020): 354.
  15. Munster VJ., et al. “A novel coronavirus emerging in China - key questions for impact assessment”. The New England Journal of Medicine (2020).
  16. Kampf G., et al. “Persistence of coronaviruses on inanimate surfaces and their inactivation with biocidal agents”. Journal of Hospital Infection (2020).
  17. Geller C., et al. “Human coronaviruses: insights into environmental resistance and its influence on the development of new antiseptic strategies”. Viruses 4 (2012): 3044-3068.
  18. Kermack WO And Mc Kendrick AG. "Contributions to the Mathematical Theory of Epidemics”. Proceedings of the Royal Society A 115 (1927): 700-721.
  19. Hethcote HW. “The mathematics of infectious diseases”. Society for Industrial and Applied Mathematics 42 (2000): 599-653.
  20. Daley DJ and Gani J. “Epidemic Modeling and Introduction”. NY: Cambridge University Press (2005).
  21. Kermack WO and McKendrick AG. “Contributions to the Mathematical theory of Epidemics. III Further studies on the problem of endemicity”. Proceedings of the Royal Society A (1933): 141-194.
  22. Trottier H and Philippe P. “Deterministic modeling of infectious diseases: theory and methods”. The Internet Journal of Infectious Diseases (2001). 


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.


Acceptance rate30%
Acceptance to publication20-30 days

Indexed In

News and Events

Contact US