  Reliable estimation of spatio-temporal trends in population-level HIV
incidence is becoming an increasingly critical component of HIV prevention
policy-making. However, direct measurement is nearly impossible. Current,
widely used models infer incidence from survey and surveillance seroprevalence
data, but they require unrealistic assumptions about spatial independence
across spatial units. In this study, we present an epidemic model of HIV that
explicitly simulates the spatial dynamics of HIV over many small, interacting
areal units. By integrating all available population-level data, we are able to
infer not only spatio-temporally varying incidence, but also ART initiation
rates and patient counts. Our study illustrates the feasibility of applying
compartmental models to larger inferential problems than those to which they
are typically applied, as well as the value of data fusion approaches to
infectious disease modeling.
