  Molecular HIV Surveillance (MHS) has been described as key to enabling rapid
responses to HIV outbreaks. It operates by linking individuals with genetically
similar viral sequences, which forms a network. A major limitation of MHS is
that it depends on sequence collection, which very rarely covers the entire
population of interest. Ignoring missing data by conducting complete case
analysis--which assumes that the observed network is complete--has been shown
to result in significantly biased estimates of network properties. We use MHS
to investigate disease dynamics of the HIV epidemic in Miami-Dade County (MDC)
among men who have sex with men (MSM)--only 30.1% have a reported sequence. To
do so, we present an approach for making Bayesian inferences on partially
observed networks. Through a simulation study, we demonstrate a reduction in
error of 43%-63% between our estimates and complete case analyses. We estimate
increased mixing between MSM communities in MDC, defined by race and
transmission risk compared to the results based on complete case analysis. Our
approach makes use of a flexible network model--congruence class model--to
overcome the high computational burden of previously reported Bayesian
approaches to estimate network properties from partially observed networks.
