Ndlovu Meshach, Mpofu Mqhelewenkosi A, Moyo Rodwell G
Gwanda State University Department of Geomatics and Surveying, Zimbabwe.
Phys Chem Earth (2002). 2022 Dec;128:103288. doi: 10.1016/j.pce.2022.103288. Epub 2022 Nov 2.
In this research paper we present a mathematical model for COVID-19 in high-risk settings and low-risk settings which might be infection dynamics between hotspots and less risky communities. The main idea was to couple the SIR model with alternating risk levels from the two different settings high and low-risk settings. Therefore, building from this model we partition the infected class into two categories, the symptomatic and the asymptomatic. Using this approach we simulated COVID-19 dynamics in low and high-risk settings with auto-switching risk settings. Again, the model was analyzed using both analytic methods and numerical methods. The results of this study suggest that switching risk levels in different settings plays a pivotal role in COVID-19 progression dynamics. Hence, population reaction time to adhere to preventative measures and interventions ought to be implemented with flash speed targeting first the high-risk setting while containing the dynamics in low-risk settings.
在本研究论文中，我们提出了一个针对高风险环境和低风险环境中新冠疫情的数学模型，该模型可能描述了热点地区与风险较低社区之间的感染动态。主要思路是将易感-感染-康复（SIR）模型与来自高风险和低风险这两种不同环境的交替风险水平相结合。因此，基于该模型，我们将感染人群分为两类，即有症状感染者和无症状感染者。使用这种方法，我们模拟了具有自动切换风险设置的高风险和低风险环境中的新冠疫情动态。同样，我们使用解析方法和数值方法对该模型进行了分析。本研究结果表明，在不同环境中切换风险水平在新冠疫情的发展动态中起着关键作用。因此，应该以闪电般的速度实施针对高风险环境的人群反应时间，以遵守预防措施和干预措施，同时控制低风险环境中的疫情动态。
