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Activity Number: 464 - SPEED: Infectious Diseases, Spatial Modeling and Environmental Exposures, Speed 1
Type: Contributed
Date/Time: Wednesday, July 31, 2019 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistics in Epidemiology
Abstract #304857
Title: Estimate Booster Vaccination Effect on the Distribution of Antibody Level Using Mixture Model
Author(s): Li Deng*
Companies: Centers for Disease Control and Prevention
Keywords: mixture model; subgroup; booster vaccination; infection rate; marginal distribution/proportion

Previously a mixture model was used to estimate distributions of antibody levels in subgroups (e.g. newly infected). Antibody levels elevate shortly after vaccination and decay afterwards. The newly introduced booster vaccination in adults and adolescents presents a challenge in estimating infection rates in the current population as newly infected and vaccinated have similar levels of antibody. Here we propose a method to incorporate newly vaccinated into mixture models. The proportion of subjects vaccinated t months before data collection is calculated given vaccine coverage by age and evenly distributed vaccination schedules over a period (e.g. 10 years). The chance of jumping from one subgroup to another is determined by a decay function f(t). For simplicity, we partition the whole time range into k intervals. Within each, antibody levels remain constant. By integrating over all strata, defined by vaccination time intervals and age, we obtain the proportion of newly vaccinated and then subtract it to estimate the infection rate. Simulation studies show that our model achieves a lower type I error (i.e. claims a change when there is none) than the one without adjustment.

Authors who are presenting talks have a * after their name.

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