Bias Correction for Sampled Genetic Network Data

• Activity Number: 208 - SPEED: Bayesian Methods and Social Statistics Part 2
• Type: Contributed
• Date/Time: Monday, August 8, 2022 : 2:00 PM to 2:45 PM
• Sponsor: Social Statistics Section
• Author(s): Gabrielle Lemire* and Nicole Carnegie and Ravi Goyal and Breschine Cummins
• Companies: Montana State University and Montana State University and University of California San Diego and Montana State University
• Keywords: networks; genetic networks; HIV; sampling; bias correction

Abstract

Investigation of the spread of HIV in subpopulations is necessary to deliver effective interventions, such as PrEP, ART, and support services (e.g., adherence counseling, mental health and substance support). HIV molecular epidemiology (ME), the use of viral sequences to cluster individuals based on genetic similarity, is emerging as a critical public health tool. The growth of clusters signal within what populations HIV is spreading. However, due to incomplete sequencing of the HIV infected population, the sizes of the clusters identified by ME are biased. We present a network-based approach to investigate this issue as traditional methods (based on the assumption of independent observations) are unable to appropriately adjust for these biases.