Early in the pandemic, when COVID-19 spread quickly, chemist Jixin Chen spotted a unique possibility for his kinetics lab, which studies reaction speeds.
When he initially conducted the experiment in the spring of 2021, undergraduate students came to the conclusion that lockdowns, face masks, and social seclusion were efficient strategies to decrease the rate of COVID spread.
However, they also learned about the limitations of modeling, finding that an increase in the pace of spread wasn’t always correlated with a significant number of verified cases. Students from the following lab recommended that researchers continue this effort when infection and vaccination rates were appreciable in a journal article they authored about their experience.
That’s exactly what took place. The mathematical model was expanded by the spring 2022 lab students to make predictions about the rate of COVID-19 dissemination in the United States with widespread immunization.
Additionally, they ran the model for Ohio until fall 2022 and were able to forecast the spike in cases that the state would experience towards the conclusion of the summer.
The second batch of lab students similarly documented their experiences, and this time they had their work accepted by the Journal of Chemical Education for publication. All of the students left the lab with the necessary paperwork completed.
However, they might also add a few paragraphs to their resumes mentioning their familiarity with modeling software, data analysis abilities, and journal publications. The COVID concept allowed Chen’s pupils to operate on their own computers with freely available material and applications in the spring of 2021 when the globe became distant.
It worked so well that undergraduate students submitted a journal article on their experience, noting, “The viral spreading model is complicated but parameters, such as its reproduction number, Rt, can be estimated with the susceptible, infectious, or recovered model. COVID-19 data for many states and countries are widely available online. This provides an opportunity for the students to analyze its spreading kinetics remotely.”
The most challenging part for me was working out the bugs in our formulas. This can be tricky when you’ve been staring at your data for hours. It all just starts to swirl around in the mind, but that’s just when you need to come back to it later when you have a clear mind.
Emma Lintelman
When it comes to describing the steady-state approximation for various models in the textbook, Chen saw that COVID modeling offered a benefit. The pupils stated that they gained something from investigating Excel’s simulation feature.
“The most surprising and fun thing for me was how research can be accessible. We only used resources and data from free websites, but from there we were able to push further and dive into something so relevant to today’s society,” said Emma Lintelman, a rising senior chemistry major with a biological sciences minor in the College of Arts & Sciences.
Chen and his students advanced the numerical simulation of kinetics and regression modeling in the spring of 2022.
“The first time we used this approach, the students were able to apply the kinetic techniques learned in physical chemistry to analyze an ongoing real-world problem through a remote learning environment,” Chen said.
“This year, another group of undergraduate students led by graduate students Dylan Smith and Tharushi Ambagaspitiya did the same practice and extended the mathematical model to predict COVID-19 spread in the U.S. with massive vaccination.”
The lab uses the susceptible-infectious-recovery (SIR) and SIR-vaccinated (SIRV) models to examine COVID-19 spread data from the U.S. Centers for Disease Control and Prevention (CDC).
By fitting the data with the models, the fundamental reproduction number R0 and the real-time reproduction number Rt of COVID-19 are obtained, which explains the spreading kinetics and predicts the spreading tendency in a certain condition.
According to Chen, the pupils are able to distinguish between the SIR and SIRV models with ease. The SIRV model takes vaccination into account, which aids in explaining the progression of the present epidemic.
Making predictions for the upcoming months allowed the students to learn about the models’ ability to predict the future.
“I think the most surprising part of performing our COVID-19 kinetics simulation was seeing the drastic effects of varying the time-dependent reproduction number in our simulation,” said David McEwen, a senior majoring in chemistry and minoring in business.
“This allowed us to directly simulate different levels of regulation of the virus through masking, social distancing, etc. By changing the number by a large amount, we were able to directly see with our data the increased or decreased spread rate of the virus, which was astounding at times.”
“I think the most challenging parts for me were initially setting up our simulation parameters and fitting the simulated data to the collected case data. Fitting the simulated data to the actual case numbers sometimes required precise adjustment and took some time,” McEwen said.
Lintelman concurred.
“The most challenging part for me was working out the bugs in our formulas,” she said. “This can be tricky when you’ve been staring at your data for hours. It all just starts to swirl around in the mind, but that’s just when you need to come back to it later when you have a clear mind.”
Chen noted that a National Institutes of Health grant (Super-Resolution Optical Mapping for DNA Analysis Using Triplex-Forming Oligonucleotides as Stochastic Molecular Probes) partly funded the teaching lab.
