Mbogo Rachel Waema, Orwa Titus Okello
Institute of Mathematical Sciences, Strathmore University, Box 59857 00200, Nairobi, Kenya.
Infect Dis Model. 2021;6:370-380. doi: 10.1016/j.idm.2021.01.009. Epub 2021 Jan 27.
The coronavirus disease 2019 (COVID-19) pandemic reached Kenya in March 2020 with the initial cases reported in the capital city Nairobi and in the coastal area Mombasa. As reported by the World Health Organization, the outbreak of COVID-19 has spread across the world, killed many, collapsed economies and changed the way people live since it was first reported in Wuhan, China, in the end of 2019. As at the end of December 2020, it had led to over 2.8 million confirmed cases in Africa with over 67 thousand deaths. The trend poses a huge threat to global public health. Understanding the early transmission dynamics of the infection and evaluating the effectiveness of control measures is crucial for assessing the potential for sustained transmission to occur in new areas. We employed a SEIHCRD mathematical transmission model with reported Kenyan data on cases of COVID-19 to estimate how transmission varies over time. The model is concise in structure, and successfully captures the course of the COVID-19 outbreak, and thus sheds light on understanding the trends of the outbreak. The next generation matrix approach was adopted to calculate the basic reproduction number ( ) from the model to assess the factors driving the infection. The model illustrates the effect of mass testing on COVID-19 as well as individual self initiated behavioral change. The results have significant impact on the management of COVID-19 and implementation of prevention policies. The results from the model analysis shows that aggressive and effective mass testing as well as individual self initiated behaviour change play a big role in getting rid of the COVID-19 epidemic otherwise the rate of infection will continue to increase despite the increased rate of recovery.
2019年冠状病毒病（COVID-19）疫情于2020年3月蔓延至肯尼亚，最初的病例报告来自首都内罗毕和沿海地区蒙巴萨。据世界卫生组织报告，自2019年底在中国武汉首次报告以来，COVID-19疫情已蔓延至全球，造成许多人死亡，经济崩溃，并改变了人们的生活方式。截至2020年12月底，非洲已报告超过280万确诊病例，死亡人数超过6.7万。这一趋势对全球公共卫生构成了巨大威胁。了解感染的早期传播动态并评估控制措施的有效性对于评估新地区持续传播的可能性至关重要。我们采用了一个带有肯尼亚COVID-19病例报告数据的SEIHCRD数学传播模型来估计传播如何随时间变化。该模型结构简洁，成功捕捉了COVID-19疫情的发展过程，从而有助于理解疫情趋势。采用下一代矩阵方法从模型中计算基本再生数（ ），以评估驱动感染的因素。该模型说明了大规模检测对COVID-19的影响以及个人自发的行为变化。研究结果对COVID-19的管理和预防政策的实施具有重大影响。模型分析结果表明，积极有效的大规模检测以及个人自发的行为变化在消除COVID-19疫情方面发挥着重要作用，否则尽管康复率有所提高，但感染率仍将继续上升。
