Impact of Population Recruitment on the HIV Epidemics and the Effectiveness of HIV Prevention Interventions

Yuqin Zhao, Daniel T. Wood, Hristo V. Kojouharov, Yang Kuang, Dobromir T. Dimitrov

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


Mechanistic mathematical models are increasingly used to evaluate the effectiveness of different interventions for HIV prevention and to inform public health decisions. By focusing exclusively on the impact of the interventions, the importance of the demographic processes in these studies is often underestimated. In this paper, we use simple deterministic models to assess the effectiveness of pre-exposure prophylaxis in reducing the HIV transmission and to explore the influence of the recruitment mechanisms on the epidemic and effectiveness projections. We employ three commonly used formulas that correspond to constant, proportional and logistic recruitment and compare the dynamical properties of the resulting models. Our analysis exposes substantial differences in the transient and asymptotic behavior of the models which result in 47 % variation in population size and more than 6 percentage points variation in HIV prevalence over 40 years between models using different recruitment mechanisms. We outline the strong influence of recruitment assumptions on the impact of HIV prevention interventions and conclude that detailed demographic data should be used to inform the integration of recruitment processes in the models before HIV prevention is considered.

Original languageEnglish (US)
Pages (from-to)2057-2090
Number of pages34
JournalBulletin of mathematical biology
Issue number10
StatePublished - Oct 1 2016


  • HIV prevention
  • Mathematical modeling
  • Population recruitment
  • Pre-exposure prophylaxis

ASJC Scopus subject areas

  • General Neuroscience
  • Immunology
  • General Mathematics
  • General Biochemistry, Genetics and Molecular Biology
  • General Environmental Science
  • Pharmacology
  • General Agricultural and Biological Sciences
  • Computational Theory and Mathematics


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