当前位置: X-MOL 学术Math. Biosci. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Modeling the transmission dynamics of the COVID-19 Pandemic in South Africa.
Mathematical Biosciences ( IF 4.3 ) Pub Date : 2020-08-04 , DOI: 10.1016/j.mbs.2020.108441
Salisu M Garba 1 , Jean M-S Lubuma 1 , Berge Tsanou 2
Affiliation  

Since its emergence late in 2019, the COVID-19 pandemic continues to exude major public health and socio-economic burden globally. South Africa is currently the epicenter for the pandemic in Africa. This study is based on the use of a compartmental model to analyze the transmission dynamics of the disease in South Africa. A notable feature of the model is the incorporation of the role of environmental contamination by COVID-infected individuals. The model, which is fitted and parametrized using cumulative mortality data from South Africa, is used to assess the impact of various control and mitigation strategies. Rigorous analysis of the model reveals that its associated continuum of disease-free equilibria is globally-asymptotically stable whenever the control reproduction number is less than unity. The epidemiological implication of this result is that the disease will eventually die out, particularly if control measures are implemented early and for a sustainable period of time. For instance, numerical simulations suggest that if the lockdown measures in South Africa were implemented a week later than the 26 March, 2020 date it was implemented, this will result in the extension of the predicted peak time of the pandemic, and causing about 10% more cumulative deaths. In addition to illustrating the effectiveness of self-isolation in reducing the number of cases, our study emphasizes the importance of surveillance testing and contact tracing of the contacts and confirmed cases in curtailing the pandemic in South Africa.



中文翻译:

对南非COVID-19大流行的传播动力学进行建模。

自2019年末出现以来,COVID-19大流行继续在全球散布主要公共卫生和社会经济负担。南非目前是非洲大流行的中心。这项研究基于隔间模型的使用,以分析该疾病在南非的传播动态。该模型的显着特征是感染了COVID的个体对环境的污染作用。该模型使用来自南非的累积死亡率数据进行拟合和参数化,用于评估各种控制和缓解策略的影响。对模型的严格分析显示,只要控制繁殖数小于1,其相关的无病平衡连续体就全局渐近稳定。该结果的流行病学含义是该疾病最终将消灭,特别是如果尽早实施控制措施并持续一段可持续的时期。例如,数值模拟表明,如果南非在2020年3月26日实施锁定措施的日期之后一周实施锁定措施,这将导致预计的大流行高峰时间延长,并导致大约10%更多累计死亡人数。除了说明自我隔离在减少病例数方面的有效性外,我们的研究还强调了对接触者和已证实病例进行监视测试和联系人追踪的重要性,以减少南非的大流行。特别是如果尽早且持续地实施控制措施。例如,数值模拟表明,如果南非在2020年3月26日实施锁定措施的日期之后一周实施锁定措施,这将导致预计的大流行高峰时间延长,并导致大约10%更多累计死亡人数。除了说明自我隔离在减少病例数方面的有效性外,我们的研究还强调了监视测试以及对联系人和已确认病例的联系进行追踪的重要性,以减少南非的大流行。特别是如果尽早且持续地实施控制措施。例如,数值模拟表明,如果南非在2020年3月26日实施锁定措施的日期之后一周实施锁定措施,这将导致预计的大流行高峰时间延长,并导致大约10%更多累积死亡人数。除了说明自我隔离在减少病例数方面的有效性外,我们的研究还强调了对接触者和已证实病例进行监视测试和联系人追踪的重要性,以减少南非的大流行。这将导致大流行的预测高峰时间延长,并导致更多的累积死亡人数增加10%。除了说明自我隔离在减少病例数方面的有效性外,我们的研究还强调了对接触者和已证实病例进行监视测试和联系人追踪的重要性,以减少南非的大流行。这将导致大流行的预测高峰时间延长,并导致更多的累积死亡人数增加10%。除了说明自我隔离在减少病例数方面的有效性外,我们的研究还强调了对接触者和已证实病例进行监视测试和联系人追踪的重要性,以减少南非的大流行。

更新日期:2020-08-12
down
wechat
bug