Towards a Phylogenetic Measure to Quantify HIV Incidence

Pieter Libin, Nassim Versbraegen, Ana B. Abecasis, Perpetua Gomes, Tom Lenaerts, Ann Nowé

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


One of the cornerstones in combating the HIV pandemic is the ability to assess the current state and evolution of local HIV epidemics. This remains a complex problem, as many HIV infected individuals remain unaware of their infection status, leading to parts of HIV epidemics being undiagnosed and under-reported. We first present a method to learn epidemiological parameters from phylogenetic trees, using approximate Bayesian computation (ABC). The epidemiological parameters learned as a result of applying ABC are subsequently used in epidemiological models that aim to simulate a specific epidemic. Secondly, we continue by describing the development of a tree statistic, rooted in coalescent theory, which we use to relate epidemiological parameters to a phylogenetic tree, by using the simulated epidemics. We show that the presented tree statistic enables differentiation of epidemiological parameters, while only relying on phylogenetic trees, thus enabling the construction of new methods to ascertain the epidemiological state of an HIV epidemic. By using genetic data to infer epidemic sizes, we expect to enhance our understanding of the portions of the infected population in which diagnosis rates are low.

Original languageEnglish
Title of host publicationArtificial Intelligence and Machine Learning - 31st Benelux AI Conference, BNAIC 2019, and 28th Belgian-Dutch Machine Learning Conference, BENELEARN 2019, Revised Selected Papers
EditorsBart Bogaerts, Gianluca Bontempi, Pierre Geurts, Nick Harley, Bertrand Lebichot, Tom Lenaerts, Gilles Louppe
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages17
ISBN (Print)9783030651534
Publication statusPublished - 2020
Event31st Benelux Conference on Artificial Intelligence, BNAIC 2019 and 28th Belgian Dutch Machine Learning Conference, BENELEARN 2019 - Brussels, Belgium
Duration: 6 Nov 20198 Nov 2019

Publication series

NameCommunications in Computer and Information Science
ISSN (Print)1865-0929
ISSN (Electronic)1865-0937


Conference31st Benelux Conference on Artificial Intelligence, BNAIC 2019 and 28th Belgian Dutch Machine Learning Conference, BENELEARN 2019


  • Approximate bayesian computation
  • Coalescent theory
  • HIV incidence
  • Phylogenetics


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