Honors Theses
Advisor
Matthew Wascher, Ph.D.
Department
Mathematics
Publication Date
4-23-2025
Document Type
Honors Thesis
Abstract
Many mathematical models of infectious disease assume the population is well-mixed, meaning every pair of individuals is equally likely to contact each other, potentially spreading the disease. In reality, populations are rarely well-mixed, and an important way in which they are not is assortative mixing, that is, when pairs of individuals who are similar are more likely to contact one another than pairs of individuals who are different. Failing to account for assortative mixing by vaccine status leads to biased estimates of important quantities that characterize disease transmission, including reproduction numbers. We expand on this by developing a model that can overcome this bias using a framework called dynamic survival analysis that studies the epidemic using techniques from survival analysis. Additionally, our model circumvents gaps in the information required. For example, our model works when test times, rather than infection times, are known.
Permission Statement
This item is protected by copyright law (Title 17, U.S. Code) and may only be used for noncommercial, educational, and scholarly purposes.
Keywords
Undergraduate research
eCommons Citation
Biesecker-Mast, Jabob D., "Estimating Disease Transmissions with Assortative Mixing by Vaccination Status" (2025). Honors Theses. 461.
https://ecommons.udayton.edu/uhp_theses/461
COinS
