Students develop and test simple kinetic models of the spread of COVID-19 caused by the novel SARS-CoV-2 virus . Microsoft Excel is used as the modeling platform because it's non-threatening to students and because it's widely available . Students develop simple finite difference models and implement them in the cells of preformatted spreadsheets following a guided-inquiry pedagogy . Students fit the resulting models to reported cases-per-day data for the United States using least-squares techniques with Excel's Solver . Using their own spreadsheets, students discover for themselves that the initial exponential growth of COVID-19 can be explained by a simplified unlimited growth model and by the SIR model . They also discover that the effects of social distancing during April and May 2020 can be modeled using a Gaussian transition function for the infection rate coefficient that can be easily implemented in Excel . Answering similar active-learning questions, students discover that the summer surge was caused by prematurely relaxing social distancing and then reimposing stricter social distancing . By fitting published infection rate data up to Thanksgiving (November 26 , 2020), students discover that the beginning of the fall surge can be explained by a return to more relaxed social distancing with a similar infection rate constant as the inception of the summer surge . Students then model the effect of vaccinations and validate the resulting SIR-V model by showing that it successfully predicts the reported cases-per-day data from November 26 , 2020 through the holiday period up to February 20 , 2021 . The success of the model in predicting the spread of COVID-19 during that time is a remarkable validation of the SIR model and its SIR-V variant . See http: //circle4.com/biophysics for free sample textbook chapters and instructional videos.