A time-dependent SIR model for COVID-19 in Azerbaijan
Research article: A time-dependent SIR model for COVID-19 in Azerbaijan
Authors: R.I. Khalilov1*, G.A. Aslanova2
1 Department of Biophysics and Biochemistry, Baku State University, 23 academician Z.Khalilov Str., Baku AZ1148, Azerbaijan
2 Department of Molecular Biology and Biotechnologies, Baku State University, 23 academician Z.Khalilov Str., Baku AZ1148, Azerbaijan
*For correspondence: hrovshan@hotmail.com
Received 03 Novembe 2020; Received in revised form 18 November 2020; Accepted 23 Novembe 2020
Abstract:
A novel coronavirus named ‘‘2019-nCoV’’, has been causing the deadliest pandemic in late 2019 and early 2020. This novel virus was defined as the coronavirus disease 2019 (COVID-19) by the World Health Organization (WHO). Diseases have afflicted humans ever since there have been human be- ings. From AD 541 to 542, the global pandemic known as “the Plague of Justinian” is one of the worst pandemics in the world and is estimated to have killed 15–25% of the world’s 200 million population. Today we are battling to control and prevent the spread of COVID-19. Coronavirus has the potential to cause the deadliest pandemic in human history. The number of cases of COVID-19 outside China has drastically grown up since 16th March, 2020. On 28 February, 2020 Azerbaijan has confirmed first positive case of COVID-19 within its border. The patient, a Russian national, had traveled from Iran to Azerbaijan. On 31 October, 2020 the total number of confirmed coronavirus cases is 55.269 in Azerbaijan. In this paper, we conduct mathematical and numerical analyses of COVID-19. We have applied the SIR model considering data from Azerbaijan. Assuming the published data are reliable, the SIR model can be applied to assess the spread of the COVID-19 disease and predict the number of infected, removed and recovered populations and deaths in the communities, accommo- dating at the same time possible surges in the number of susceptible individuals.
Keywords: COVID-19, SIR, mathematical model, simulation, susceptible, infected, recovered
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