Author(s): Martin Nyamu, Benard Daniel Mutwiri and Babu Lawrence

Abstract: The Novel Coronavirus (COVID-19), which originated from Wuhan city in Central China late 2019 is still spreading rapidly worldwide with the current infections standing at 16,899, 326 cases and 663, 540 deaths as at 29^th July 2020 as captured in the World meter. Coupled with a rampant transmissibility rate, the virus has taken advantage of globalization and interconnectedness of the world as opposed to the previous years’ pandemics and claimed a number of lives in a very short span. Indeed, despite the significant interventions and containment measures imposed by different nations, the virus seems to have exponentially doubled its prevalence with the peak yet to register in numerous countries, including Kenya. Therefore, the aim of this article was to model the COVID-19 (Coronavirus) cases in Kenya using an epidemiological model (SIR). Specifically, the article sought to expound the use of differential equations in infectious disease modelling, as well as, the importance of herd immunity and need for an urgent coronavirus vaccination. The study used a simple SIR model to predict and model the spread of Coronavirus in Kenya. The model was fitted using data of the reported incidences of coronavirus in Kenya (dating 13^th March, 2020 to 21^st July, 2020), extracted from the {coronavirus} package in R developed by Rami Krispin. Based on the analysis findings, the graphs postulated that the number of cases estimated by the SIR model slightly deviated from the exponential growth, though evidenced a significant exponent rise in the coronavirus cases in Kenya from March to July. Moreover, with a reproduction number of 1.20, the model suggested that about 16.67% of the population requires vaccination to stop the spread of the infection. However, with no intervention or measures to deter the spread of the pandemic, the peak in Kenya is expected to be reached by 30^th of September with approximately 443,720 infections and 7,810 deaths (given a fatality rate of 1.8%). Overall, the findings of this study offer insight to the government of Kenya to impose appropriate and relevant measures and policy that would stop the spread of the virus to a larger population. Additionally, the model creates a public knowhow on the importance of the currently imposed containment measures and regulations to reduce the spread of viruses in Kenya.

DOI: 10.22271/maths.2020.v5.i5a.579

Pages: 20-24 | Views: 1595 | Downloads: 56

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