Estimating Disease Transmissions with Assortative Mixing by Vaccination Status

Estimating Disease Transmissions with Assortative Mixing by Vaccination Status

    Presenter(s)

    Jacob Biesecker-Mast

    Comments

    1:20-1:40, Kennedy Union 310

    Description

    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.

    Publication Date

    4-23-2025

    Project Designation

    Honors Thesis

    Primary Advisor

    Atif A. Abueida

    Primary Advisor's Department

    Mathematics

    Keywords

    Stander Symposium, College of Arts and Sciences

    Institutional Learning Goals

    Scholarship; Practical Wisdom; Community

    Estimating Disease Transmissions with Assortative Mixing by Vaccination Status

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