Global efforts around the world are focused on to discuss several health care strategies for minimizing the impact of the new coronavirus (COVID-19) on the community. As it is clear that this virus becomes a public health threat and spreading easily among individuals. Mathematical models with computational simulations are effective tools that help global efforts to estimate key transmission parameters and further improvements for controlling this disease. This is an infectious disease and can be modeled as a system of non-linear differential equations with reaction rates.

This work reviews and develops some suggested models for the COVID-19 that can address important questions about global health care and suggest important notes. Then, we suggest an updated model that includes a system of differential equations with transmission parameters. Some key computational simulations and sensitivity analysis are investigated. Also, the local sensitivities for each model state concerning the model parameters are computed using three different techniques: non-normalizations, half normalizations, and full normalizations.

Results based on the computational simulations show that the model dynamics are significantly changed for different key model parameters. Interestingly, we identify that transition rates between asymptomatic infected with both reported and unreported symptomatic infected individuals are very sensitive parameters concerning model variables in spreading this disease. This helps international efforts to reduce the number of infected individuals from the disease and to prevent the propagation of new coronavirus more widely on the community. Another novelty of this paper is the identification of the critical model parameters, which makes it easy to be used by biologists with less knowledge of mathematical modeling and also facilitates the improvement of the model for future development theoretically and practically.

世界各地的全球努力重点讨论几种医疗保健策略，以最大限度地减少新型冠状病毒 (COVID-19) 对社区的影响。很明显，这种病毒已成为公共卫生威胁，并且很容易在个人之间传播。具有计算模拟的数学模型是帮助全球努力估计关键传播参数并进一步改进控制这种疾病的有效工具。这是一种传染病，可以建模为具有反应速率的非线性微分方程组。

这项工作回顾并开发了一些针对 COVID-19 的建议模型，这些模型可以解决有关全球医疗保健的重要问题并提出重要注意事项。然后，我们建议更新模型，其中包括带有传输参数的微分方程组。研究了一些关键的计算模拟和敏感性分析。此外，涉及模型参数的每个模型状态的局部灵敏度是使用三种不同的技术计算的：非归一化、半归一化和完全归一化。

基于计算模拟的结果表明，不同关键模型参数的模型动力学发生显着变化。有趣的是，我们发现无症状感染者与报告和未报告的有症状感染者之间的转换率是关于传播这种疾病的模型变量的非常敏感的参数。这有助于国际社会努力减少该疾病的感染人数，并防止新冠病毒在社区更广泛地传播。本文的另一个新颖之处是关键模型参数的识别，这使得数学建模知识较少的生物学家很容易使用，也有利于模型在理论和实践上的进一步发展。