Muskan's Project Page

Who:  Hi! My name is Muskan (Moose-Con), and I’m from Utah. I am immensely grateful to be at the U and to be part of the ACCESS program! ACCESS has advocated for my curiosity and reinforced my passion for science through involvement in innovative research on campus. I love to garden, listen to podcasts, make pottery, bird watch, and explore Trader Joes!

My scientific interests:  I have been captivated by the STEM field ever since I was a young girl. As I furthered my interests in the field, my excitement grew exponentially. The vast amounts of information in science became clear, and I had a desire to learn more. My fascination with mathematics, and mathematical modeling, was prompted by my ACCESS summer, and I have loved getting to explore those interests with my research group!

Academic goals:  As a mathematics and philosophy double major, I look forward to coupling the principles of mathematics with medicine. I will continue working in my ACCESS lab and ensuring that the research I contribute to works to create a more just and equitable society. I plan to attend graduation school where I will enroll in a dual degree program.

Career goals:  In my lab, to have had a glimpse of the depths of mathematics is motivating! I foresee that mathematics has vast potential in my own generation. That I will have the opportunity to contribute to its growth by pursuing a career involving mathematical research is thrilling! I also want to serve as a mentor to youth within the STEM disciplines. My mentors have provided me the confidence to be a primary force in the progression of STEM research, and I hope to give back to the next generation!

Testing has been critical for surveillance and contact tracing of the COVID-19 pandemic. Even as the number of vaccinations increase, robust testing measures need to remain in place to track the spread of the SARS-CoV-2 virus. As the Biden administration begins to release their vision for the testing strategy, it is imperative that we recognize that no test is perfect. In particular, every test makes mistakes; no test has perfect sensitivity (no false negatives) and perfect specificity (no false positive). Furthermore, the sensitivity and specificity of a test change over the course of an infection. We adapted the fundamental epidemiological SIR model for spread of disease to model the number of cells infected with SARS-CoV-2 in a closed population over time. The model reveals that PCR tests, while currently regarded as the gold standard, are likely to produce false positives (and therefore falsely recommend people for quarantine) because these tests are extremely sensitive and detect dead viruses even after a patient has recovered from COVID-19 and is no longer infectious. Additionally, our model illustrates that antigen tests are likely to produce false negatives early in an infection before infected cells have released a large number of viruses. We will extend this preliminary model and findings to simulate the time when tests are administered relative to the duration of true positivity, integrating scenarios where there is variation in infectious periods and symptom onset to identify which type of test is most informative. Ultimately, these models will help optimize the goals of the testing program by taking into account the fact that all tests make mistakes.