Athapaththu Deepani V, Ambagaspitiya Tharushi D, Chamberlain Andrew, Demase Darrion, Harasin Emily, Hicks Robby, McIntosh David, Minute Gwen, Petzold Sarah, Tefft Lauren, Chen Jixin
Department of Chemistry and Biochemistry, Nanoscale & Quantum Phenomena Institute, Ohio University, Athens Ohio 45701.
J Chem Educ. 2024 Jul 9;101(7):2892-2898. doi: 10.1021/acs.jchemed.4c00015. Epub 2024 Jun 18.
The COVID-19 pandemic has passed. It gives us a real-world example of kinetic data analysis practice for our undergraduate physical chemistry laboratory class. It is a great example to connect this seemingly very different problem to the kinetic theories for chemical reactions that the students have learned in the lecture class. At the beginning of the spring 2023 semester, we obtained COVID-19 kinetic data from the "Our World in Data" database, which summarizes the World Health Organization (WHO) data reported from different countries. We analyzed the effective spreading kinetics based on the susceptible-infectious-recovered-vaccinated (SIR-V) model. We then compared the effective rate constants represented by the real-time reproduction numbers ( ) underlining the reported data for these countries and discussed the results and the limitations of the model with the students.
新冠疫情已经过去。它为我们本科物理化学实验室课程提供了一个实际数据分析实践的实例。这是一个将这个看似截然不同的问题与学生在课堂上学到的化学反应动力学理论联系起来的绝佳例子。在2023年春季学期开始时，我们从“Our World in Data”数据库获取了新冠疫情动力学数据，该数据库汇总了不同国家报告的世界卫生组织（WHO）数据。我们基于易感-感染-康复-接种（SIR-V）模型分析了有效传播动力学。然后，我们比较了这些国家报告数据背后由实时再生数（ ）表示的有效速率常数，并与学生们讨论了模型的结果和局限性。
