Bayero University, Kano, Nigeria.
Near East University, TRNC, Turkey.
Comput Math Methods Med. 2020 Oct 13;2020:5248569. doi: 10.1155/2020/5248569. eCollection 2020.
In this paper, we developed a model that suggests the use of robots in identifying COVID-19-positive patients and which studied the effectiveness of the government policy of prohibiting migration of individuals into their countries especially from those countries that were known to have COVID-19 epidemic. Two compartmental models consisting of two equations each were constructed. The models studied the use of robots for the identification of COVID-19-positive patients. The effect of migration ban strategy was also studied. Four biologically meaningful equilibrium points were found. Their local stability analysis was also carried out. Numerical simulations were carried out, and the most effective strategy to curtail the spread of the disease was shown.
在本文中，我们开发了一个模型，建议使用机器人来识别 COVID-19 阳性患者，并研究了政府禁止个人入境，特别是来自已知存在 COVID-19 疫情的国家的移民政策的有效性。构建了两个由两个方程组成的 compartmental 模型。这些模型研究了使用机器人来识别 COVID-19 阳性患者。还研究了移民禁令策略的效果。发现了四个具有生物学意义的平衡点。对它们的局部稳定性进行了分析。进行了数值模拟，并展示了最有效的策略来遏制疾病的传播。
