Example 4: l-i SEIR-Vaccination model - Effect of Vaccination on COVID-19 Spread in the United States

In examples 1 to 3, we have demonstrated how to use Excel to calculate variables Sn, En, In, Rn, yn in l-i SEIR (Susceptible-Exposed-Infectious-Recovered) model, to determine the time-dependent kn, and to find the number of actual total infections in the absence of vaccination and breakthrough infections. In the l-i SEIR model, l is the time length of latent period, i is the time length of infectious period, and yn is the number of daily-confirmed cases of infections. In this section (Example 4), we will extend l-i SEIR model to l-i SEIR-vaccination model for examining the effect of vaccination on COVID-19 transmission. Two files (one Word file and one Excel files) are attached. In the Word file, the author described how to build the l-i SEIR-vaccination model and how to calculate the number of daily confirmed cases of COVID-19 infections, yn, in Excel. The calculated yn and the reported yn have been compared to each other and displayed graphically in the Excel file

在示例1至3中，我们已演示了如何使用Excel计算l-i SEIR（易感者-暴露者-感染者-恢复者，Susceptible-Exposed-Infectious-Recovered）模型中的变量Sₙ、Eₙ、Iₙ、Rₙ、yₙ，以确定随时间变化的kₙ，并求得无疫苗接种与突破感染情况下的实际总感染人数。在该l-i SEIR模型中，l代表潜伏期时长，i代表传染期时长，yₙ为每日确诊感染病例数。在本节（示例4）中，我们将把l-i SEIR模型拓展为l-i SEIR-疫苗接种模型，以探究疫苗接种对新冠（COVID-19）传播的影响。本次附带两份文件（1份Word文档与1份Excel文件），其中Word文档阐述了如何构建l-i SEIR-疫苗接种模型，以及如何在Excel中计算新冠每日确诊感染病例数yₙ；Excel文件则将计算得到的yₙ与报告的yₙ进行了对比，并以图表形式展示了该对比结果。