A Mathematical Characterisation of COVID-19 in Mauritius

Author:

Sayed-Hassen Sayed ZORCID

Abstract

Since the appearance of the COVID-19 virus, a fair amount of work has been undertaken by researchers around the world to model its progression. It became clear from the start of the pandemic that its spread is affected by numerous factors and the extent to which it does vary within different communities. As a result, the necessary means and the range of measures used to effectively control the virus would vary from place to place. We have been witness to different approaches adopted around the world to curb the spread of the virus both in the short and the long term. Various metrics have been used to mathematically describe the effectiveness of the approaches used. In this work, an attempt is made at determining those metrics for Mauritius and comparing them with that of the rest of the world. We first parameterise mathematical models of the progression of COVID-19 in Mauritius as well as for numerous countries around the world but keep our focus primarily on Europe. These parameters are then compared and linked to the performance of the respective country in its management of the virus. In particular, an intriguing and counter-intuitive observation is made when the growth rate and the normalised ceiling value of the mathematical models are compared. We determined the initial growth rate and computed the basic reproduction number, which provided a ball park figure of the number of subjects a contagious individual was infecting on average at the onset of the pandemic in Mauritius. This value in turn allowed the determination of the percentage of the population needing immunity to stop the spread of the virus. Finally, to better benchmark the performance of Mauritius on the world stage, two other important metrics, the case fatality rate (CFR) and the crude mortality rate (CMR) are compared with a few selected countries.

Publisher

Qeios Ltd

Reference20 articles.

1. COVID-19 pandemic in Mauritius. Wikipedia, January 2023. https://en.wikipedia.org/wiki/ COVID-19_pandemic_in_Mauritius.

2. F. Duarte. Who is ‘patient zero’ in the coronavirus outbreak? BBC News Online, February 2020. https://www.bbc.com/future/article/20200221-coronavirus-the-harmful-hunt-for-covid-19s-patient-zero.

3. D. Kucharavy and R. De Guio. Application of Logistic Growth Curve. Procedia, pages 280–290, 2015.

4. H. Ritchie, E. Mathieu, L. Rodes-Guirao, C. Appel, C. Giattino, E. Ortiz-Ospina, J. Hasell, B. Macdonald, D. Beltekian, and Roser. M. Coronavirus Pandemic (COVID-19). Our World in Data, 2020. https://ourworldindata.org/coronavirus.

5. T. Modis. Strengths and Weaknesses of S-curves. Technological Forecasting and Social Change, 74(6):866–872, 2007.

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3