Bayesian adaptive algorithms for locating HIV mobile testing services.

ABSTRACT:

Background

We have previously conducted computer-based tournaments to compare the yield of alternative approaches to deploying mobile HIV testing services in settings where the prevalence of undetected infection may be characterized by 'hotspots'. We report here on three refinements to our prior assessments and their implications for decision-making. Specifically, (1) enlarging the number of geographic zones; (2) including spatial correlation in the prevalence of undetected infection; and (3) evaluating a prospective search algorithm that accounts for such correlation.

Methods

Building on our prior work, we used a simulation model to create a hypothetical city consisting of up to 100 contiguous geographic zones. Each zone was randomly assigned a prevalence of undetected HIV

SUBMITTER: Gonsalves GS 

PROVIDER: S-EPMC6120098 | biostudies-literature | 2018 Sep

REPOSITORIES: biostudies-literature