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Meta-Analysis
. 2021 Jun 19;13(6):1174.
doi: 10.3390/v13061174.

The Role of Phylogenetics in Discerning HIV-1 Mixing among Vulnerable Populations and Geographic Regions in Sub-Saharan Africa: A Systematic Review

Affiliations
Meta-Analysis

The Role of Phylogenetics in Discerning HIV-1 Mixing among Vulnerable Populations and Geographic Regions in Sub-Saharan Africa: A Systematic Review

George M Nduva et al. Viruses. .

Abstract

To reduce global HIV-1 incidence, there is a need to understand and disentangle HIV-1 transmission dynamics and to determine the geographic areas and populations that act as hubs or drivers of HIV-1 spread. In Sub-Saharan Africa (sSA), the region with the highest HIV-1 burden, information about such transmission dynamics is sparse. Phylogenetic inference is a powerful method for the study of HIV-1 transmission networks and source attribution. In this review, we assessed available phylogenetic data on mixing between HIV-1 hotspots (geographic areas and populations with high HIV-1 incidence and prevalence) and areas or populations with lower HIV-1 burden in sSA. We searched PubMed and identified and reviewed 64 studies on HIV-1 transmission dynamics within and between risk groups and geographic locations in sSA (published 1995-2021). We describe HIV-1 transmission from both a geographic and a risk group perspective in sSA. Finally, we discuss the challenges facing phylogenetic inference in mixed epidemics in sSA and offer our perspectives and potential solutions to the identified challenges.

Keywords: HIV-1; Sub-Saharan Africa; mixed epidemics; phylogenetics; transmission dynamics.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Study flowchart. Overview of the inclusion and exclusion of articles assessed in this review.
Figure 2
Figure 2
Subregions of Sub-Saharan Africa. A map showing different subregions of Sub-Saharan Africa as defined by UNAIDS. Countries belonging to Central and West Africa (N = 25) are coloured blue whereas countries belonging to Eastern and Southern Africa (N = 24) are coloured green. Where published information on HIV-1 transmission is available, the country code is included in the map. Countries belonging to Central and West Africa include Angola (AN), Benin, Burkina Faso, Cameroon (CM), Cape Verde, Chad, Central African Republic, Republic of the Congo (RC), Côte D’Ivoire, Democratic Republic of Congo (DRC), Equatorial Guinea, Gabon, The Gambia, Ghana, Guinea, Guinea-Bissau (GM), Liberia, Mali, Mauritania, Niger, Nigeria (NG), Saint Helena, Senegal, Sierra Leone, and Togo. Countries belonging to Eastern and Southern Africa include Burundi, Botswana (BO), Comoros, Djibouti, Ethiopia (ET), Eritrea, Kenya (KE), Lesotho, Madagascar, Malawi, Mauritius, Mozambique, Réunion, Namibia (NI), Rwanda, Seychelles, Somalia, Somaliland, Tanzania, South Africa (SA), Eswatini (former Swaziland), Uganda (UG), Zambia (ZA), and Zimbabwe (ZI).
Figure 3
Figure 3
HIV-1 prevalence in different risk groups in sub-Saharan Africa (sSA). A comparison of national estimates of HIV-1 prevalence in the heterosexuals (HET) and among vulnerable populations in sSA as reported by UNAIDS in 2020 (https://aidsinfo.unaids.org/ (accessed on 20 January 2021)). East and Southern African (a), and West and Central African (b) regions were grouped together, respectively. The countries in each region were arranged in increasing HIV-1 prevalence among (HET), and HIV-1 prevalence data have been transformed into a log scale on the x-axis. Different risk groups are coloured as shown in the legend (Red: female sex workers; Brown: HET; Green: men having sex with men; Sky Blue: prisoners; Dark Blue: PWID; and Pink: transgender persons).

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References

    1. Field N., Cohen T., Struelens M.J., Palm D., Cookson B., Glynn J.R., Gallo V., Ramsay M., Sonnenberg P., MacCannell D. Strengthening the Reporting of Molecular Epidemiology for Infectious Diseases (STROME-ID): An extension of the STROBE statement. Lancet Infect. Dis. 2014;14:341–352. doi: 10.1016/S1473-3099(13)70324-4. - DOI - PubMed
    1. Riley L.W. Molecular Epidemiology of Infectious Diseases: Principles and Practices. American Society for Microbiology; Washington, DC, USA: 2004.
    1. Lemey P., Suchard M., Rambaut A. Reconstructing the initial global spread of a human influenza pandemic: A Bayesian spatial-temporal model for the global spread of H1N1pdm. PLoS Curr. 2009;1 doi: 10.1371/currents.RRN1031. - DOI - PMC - PubMed
    1. Rambaut A., Holmes E.C., O’Toole Á., Hill V., McCrone J.T., Ruis C., du Plessis L., Pybus O.G. A dynamic nomenclature proposal for SARS-CoV-2 lineages to assist genomic epidemiology. Nat. Microbiol. 2020;5:1403–1407. doi: 10.1038/s41564-020-0770-5. - DOI - PMC - PubMed
    1. Worobey M., Pekar J., Larsen B.B., Nelson M.I., Hill V., Joy J.B., Rambaut A., Suchard M.A., Wertheim J.O., Lemey P. The emergence of SARS-CoV-2 in Europe and North America. Science. 2020;370:564–570. doi: 10.1126/science.abc8169. - DOI - PMC - PubMed

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