Experience-Based Learning Approach to Chemical Kinetics: Learning from the COVID-19 Pandemic

Herein, we present an experience-based learning approach that uses the COVID-19 pandemics knowledge about virus spread and epidemics to establish an analogy between a simple epidemics model-the SIR model (susceptible-infected-removed), and a second-order autocatalytic reaction with subsequent catalyst deactivation. Our approach provides a simple and engaging way for students to learn chemical kinetics from a current situation (the pandemic) while presenting them with programming tools to numerically solve any system of differential equations that may result from more complex chemical kinetic schemes. We include the option to fit experimental data to extract meaningful rate constants. Following Kolb's cycle, we first establish the theoretical background of chemical kinetics and the SIR model and describe the analogy. Then, we propose the use of MATLAB and Python to numerically solve the system of differential equations associated with this model and plot the results showing the behavior of the system when changing the constants that describe the process, making the analogy with chemical kinetic constants. Finally, we fitted experimental data from the COVID-19 pandemic (up to early June 2020) to test the model and discuss the goodness of the fitting, the fitting parameters, and the utility of real kinetic data.