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Towards a Phylogenetic Measure to Quantify HIV Incidence

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Artificial Intelligence and Machine Learning (BNAIC 2019, BENELEARN 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1196))

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Abstract

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.

These authors contributed equally to this work.

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Acknowledgments

Pieter Libin was supported by a PhD grant of the FWO (Fonds Wetenschappelijk Onderzoek Vlaanderen) and a grant of the VUB research council (VUB/OZR2714).

Author information

Authors and Affiliations

  1. Artificial Intelligence Lab, Department of Computer Science, Vrije Universiteit Brussel, Brussels, Belgium

    Pieter Libin, Tom Lenaerts & Ann Nowé

  2. Department of Microbiology and Immunology, Rega Institute for Medical Research, KU Leuven - University of Leuven, Leuven, Belgium

    Pieter Libin & Ana B. Abecasis

  3. Global Health and Tropical Medicine, GHTM, Instituto de Higiene e Medicina Tropical, IHMT, Universidade Nova de Lisboa, UNL, Lisbon, Portugal

    Ana B. Abecasis

  4. Laboratorio Biologia Molecular, LMCBM, SPC, HEM, Centro Hospitalar Lisboa Ocidental, Centro de Investigação Interdisciplinar Egas Moniz (CiiEM), Caparica, Portugal

    Perpetua Gomes

  5. Machine Learning Group, Université Libre de Bruxelles, Boulevard du Triomphe CP212, 1050, Bruxelles, Belgium

    Nassim Versbraegen & Tom Lenaerts

  6. Interuniversity Institute of Bioinformatics in Brussels, Université Libre de Bruxelles-Vrije Universiteit Brussel, 1050, Brussels, Belgium

    Nassim Versbraegen & Tom Lenaerts

Authors
  1. Pieter Libin
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  2. Nassim Versbraegen
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  3. Ana B. Abecasis
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  4. Perpetua Gomes
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  5. Tom Lenaerts
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  6. Ann Nowé
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Corresponding authors

Correspondence to Pieter Libin or Nassim Versbraegen .

Editor information

Editors and Affiliations

  1. Vrije Universiteit Brussel, Brussels, Belgium

    Bart Bogaerts

  2. Université Libre de Bruxelles, Brussels, Belgium

    Gianluca Bontempi

  3. Université de Liège, Liège, Belgium

    Pierre Geurts

  4. Vrije Universiteit Brussel, Brussels, Belgium

    Nick Harley

  5. Université Libre de Bruxelles, Brussels, Belgium

    Bertrand Lebichot

  6. Université Libre de Bruxelles, Brussels, Belgium

    Tom Lenaerts

  7. Université de Liège, Liège, Belgium

    Gilles Louppe

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Libin, P., Versbraegen, N., Abecasis, A.B., Gomes, P., Lenaerts, T., Nowé, A. (2020). Towards a Phylogenetic Measure to Quantify HIV Incidence. In: Bogaerts, B., et al. Artificial Intelligence and Machine Learning. BNAIC BENELEARN 2019 2019. Communications in Computer and Information Science, vol 1196. Springer, Cham. https://doi.org/10.1007/978-3-030-65154-1_3

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  • DOI: https://doi.org/10.1007/978-3-030-65154-1_3

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