Goodreau, Steven MPollock, Emily2021-08-262021-08-262021-08-262021Pollock_washington_0250E_23127.pdfhttp://hdl.handle.net/1773/47315Thesis (Ph.D.)--University of Washington, 2021The complex social networks that we maintain and navigate every day make our uniquely elaborate life possible, but also put us at risk of exposure to infectious diseases. This dissertation explores the effects of different demographic processes on dynamic networks and examines the intersection of sexual behavior, biomedical interventions, and acquired immunity in the reinfection of chlamydia trachomatis. Chapter 1 demonstrates that simulating dynamic networks (separable, temporal, exponential random graph models, or STERGMs) with open populations can have unexpected effects on key network metrics and implements several corrections. Chapter 2 uses survival analysis to conclude that de-coupling marriages and cohabitations in the dissolution components of STERGMs can improve the representation of the full distribution of relationship lengths in populations with a large age range. Finally, Chapter 3 finds that while concurrency can increase the rate of reinfection of chlamydia, the greater effect is the treatment of chlamydia without concurrent treatment of a patient's local network. Reinfection rates among those treated with low levels of expedited partner therapy (EPT) are higher than among those whose infections clear naturally, but high levels of EPT treatment can be protective.application/pdfen-USCC BYChlamydiaDemographyMathematical ModelingSocial NetworksDemographyEpidemiologyAnthropologyThesis