Identifying groups of people with similar sociobehavioural characteristics in Malawi to inform HIV interventions: a latent class analysis-Reference-Cited by-同舟云学术

Identifying groups of people with similar sociobehavioural characteristics in Malawi to inform HIV interventions: a latent class analysis

Published:2020-09 Issue:9 Volume:23 Page:
ISSN:1758-2652
Container-title:Journal of the International AIDS Society
language:en
Short-container-title:J. Intern. AIDS Soc.

Author:

Merzouki Aziza¹^ORCID,Styles Amanda²,Estill Janne¹³^ORCID,Orel Erol¹,Baranczuk Zofia¹⁴⁵,Petrie Karen²,Keiser Olivia¹

Affiliation:

1. Institute of Global Health University of Geneva Geneva Switzerland

2. University of Dundee Dundee United Kingdom

3. Institute of Mathematical Statistics and Actuarial Science University of Bern Bern Switzerland

4. Department of Psychology University of Zurich Zurich Switzerland

5. Institute of Mathematics University of Zurich Zurich Switzerland

Funder

Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung

Publisher

Wiley

Subject

Infectious Diseases,Public Health, Environmental and Occupational Health

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/jia2.25615

Reference40 articles.

1. UNAIDS.Where we work | Countries | Malawi. [cited 2019 Jun 13]. Available from:https://www.unaids.org/en/regionscountries/countries/malawi

2. PEPFAR.Malawi country operational plan ‐ COP 2019: Strategic direction summary. [cited 2020 Jun 13]2019. Available from:https://www.state.gov/wp‐content/uploads/2019/09/Malawi_COP19‐Strategic‐Directional‐Summary_public.pdf

3. EnglE SmittenaarP SgaierSK.Identifying population segments for effective intervention design and targeting using unsupervised machine learning: an end‐to‐end guide. Gates Open Res. [cited 2019 Nov 5]2019. Available from:https://dqo52087pnd5x.cloudfront.net/manuscripts/14221/75e1d884‐e2d5‐4f97‐ab59‐d738a298d247_13029_‐_sema_sgaier_v2.pdf?doi=10.12688/gatesopenres.13029.2&numberOfBrowsableCollections=2&numberOfBrowsableInstitutionalCollections=0&numberOfBrowsableGateways=4

4. Sociobehavioural characteristics and HIV incidence in 29 sub‐Saharan African countries: Unsupervised machine learning analysis;Merzouki A;Epidemiology,2019

5. Identifying HIV most‐at‐risk groups in Malawi for targeted interventions. A classification tree model;Emina JBO;BMJ Open,2013

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