key: cord-0814105-ujmitzve
authors: Nduva, George M.; Nazziwa, Jamirah; Hassan, Amin S.; Sanders, Eduard J.; Esbjörnsson, Joakim
title: The Role of Phylogenetics in Discerning HIV-1 Mixing among Vulnerable Populations and Geographic Regions in Sub-Saharan Africa: A Systematic Review
date: 2021-06-19
journal: Viruses
DOI: 10.3390/v13061174
sha: 21ec3d32c241f3dae2713df28d5367db87e55266
doc_id: 814105
cord_uid: ujmitzve

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.

Molecular phylogenetic approaches have evolved into powerful tools in understanding pathogens and how they cause disease in human populations [1] . Based on genetic relatedness between pathogen strains, these studies have been coupled with epidemiological data to decipher transmission events in infected hosts [2] . This approach has several applications and has been used to understand the geographic distribution of a large number of pathogens (e.g., reconstructing the 2009 global spread of human influenza A H1N1 pandemic and, more recently, characterising the emergence and global spread of SARS-CoV-2) [3] [4] [5] [6] . Phylogenetics and phylodynamics have also been used to reconstruct and date the emergence and early spread of HIV-1, to assess epidemic growth dynamics, to determine HIV-1 genetic diversity and the prevalence of antiretroviral resistance mutations, to infer putative transmission events, and to determine evolutionary rates and spread of specific HIV-1 strains [7] [8] [9] [10] [11] [12] [13] [14] [15] . In recent years, the phylogenetic analysis of HIV-1 sequences from individuals with known risk behaviour and/or geographic location has become a powerful tool to identify sources of infections that potentially could be targeted to reduce HIV-1 incidence [13, [16] [17] [18] [19] [20] [21] [22] . However, most of these studies have been conducted in wellresourced countries with HIV-1 epidemics that are likely to differ in transmission dynamics groups). Any discordance between the two independent reviewers on the eligibility of articles was resolved through discussions for a consensus.

Shortlisted articles were imported into EndNote X8 (Clarivate, Philadelphia, PA, USA) for further management and to compile the information presented in this review.

Based on a literature search done on 12 March 2021, 2722 articles were identified. Among these, 357 articles were not in English or involved nonhuman subjects, 2000 were ineligible by title review, 85 were ineligible by abstract review, and 216 were ineligible by a full-text review as they did not address HIV-1 transmission dynamics from a phylogenetic perspective ( Figure 1 ). Sixty-four articles were considered eligible for full-text review, including 29 articles assessing geographic dispersion (Table 1) Central and West African countries F1 1958 (1934-1973) Spread from DRC, derived from a single founder event. "Pure" F1 variants are most common in Angola.

Angola F1 1983 (1978) (1979) (1980) (1981) (1982) (1983) (1984) (1985) (1986) (1987) (1988) (1989) The Angolan civil war was associated with a wave of emigration and a phase of negative migratory outflow during 1960-1980. 22484759 C 1978 (1973-1985) 1979 (1973) (1974) (1975) (1976) (1977) (1978) (1979) (1980) (1981) (1982) (1983) (1984) (1985) 1983 (1977) (1978) (1979) (1980) (1981) (1982) (1983) (1984) (1985) (1986) (1987) (1988) (1989) (1990) 1990 (1982) (1983) (1984) (1985) (1986) (1987) (1988) (1989) (1990) (1991) (1992) (1993) (1994) (1995) (1996) (1997) 1994 (1989) (1990) (1991) (1992) (1993) (1994) (1995) (1996) (1997) (1998) 2005 (2002) (2003) (2004) (2005) (2006) (2007) (2008) HIV-1 subtype C epidemic in Angola originated from multiple independent introductions from Burundi, Zambia, Zimbabwe, and South Africa. The civil war may have contributed to the emergence of the HIV-1 epidemic in Angola.

HIV-1 subtypes J and H seem to have been present in Angola since at least 1993. 27098898 Group M 1978 Group M (1975 Group M -1985 The majority of sequences sampled in 2008-2010 in Luanda clustered together which is consistent with a locally fuelled epidemic.

Cameroon CRF02_AG 1973 (1972 -1975 Two distinct lineages of CRF02_AG seem to have ignited in the urban centre of Cameroon. Ethnographic data suggests that well-supported HIV-1 migration was related to chance exportation events rather than by sustained human migratory flows. 1976 (1966-1984) 1976 (1968-1986) 1979 (1953-1989) Three monophyletic variants were identified and emerged in the mid-1970's and spread slowly over 30 General Eastward and Southward trends in the spread of HIV-1 from the Kinshasa-Brazzaville and the Pointe-Noire areas to other population centres. 1981 (1974-1986) 1976 (1968-1982) 1980 (1974-1984) 1979 (1972-1984) 1981 (1975-1985) 1979 Multiple introductions of CRF02_AG 1976-1981, and a single introduction of sub-subtype A3 in 1979 (median estimates). HIV-1 was introduced into the urban centre (the Capital Bissau) from where it spread to rural areas. 1975 (1969-1982) 1963 (1948-1974) 1970 (1960-1980) 1960 (1947-1974) 1971 (1952-1983) Urban areas (Abuja and Lagos) were the major hubs of HIV-1 transmission in Nigeria. HIV-1 first emerged and expanded within large urban centres before migrating to smaller rural areas. 1960 (1950-1968) Ugandan epidemics originated in rural Southwestern Uganda with subsequent spread to other locations without any substantial HIV-1 introductions into this location suggesting that emerging infections from this low-incidence location are mostly from within the region.

21365013 Nigeria G CRF02_AG CRF43_02G

D 1973 (1970-1977) 33182587 The HIV-1 epidemic in sSA is driven by different HIV-1 subtypes, often geographically restricted [33, 34] . Between 2010 and 2015, about 99% of the HIV-1 infections in Southern African countries and Ethiopia were subtype C, whereas the dominating HIV-1 strains in East Africa were sub-subtype A1 and subtype D [35] . The epidemic in West Africa is mainly driven by the circulating recombinant form (CRF) 02_AG, sub-subtype A3, and subtype G [12, 35, 36] . In contrast, the HIV-1 epidemic in Central Africa is more complex and diverse, and most HIV-1 subtypes have been found in this region [35, 37] . In this review, we grouped the assessment of HIV-1 transmissions in sSA into two geographic regions according to the UNAIDS classification (https://aidsinfo.unaids.org/ (accessed on 20 January 2021)): East and Southern Africa; and West and Central Africa ( Figure 2 ). 

Nigeria is the most populous country in sSA. A recent study by Nazziwa et al. used 1442 pol sequences (collected in 1999-2013) from four geopolitical zones in Nigeria (Southwest, North Central, Northeast, and Northwest) to reconstruct HIV-1 transmission dynamics [12] . Phylogeographic analyses suggested that HIV-1 first emerged and expanded within the large urban centres (Lagos and Abuja), before migrating to smaller and more rural areas. Abuja, the capital city of Nigeria, was estimated to be the geographical origin of both subtype G and CRF02_AG in Nigeria. In addition, the analysis indicated that one single introduction resulted in the main Nigerian subtype G epidemic (time to the most recent common ancestor, tMRCA; 1987). In contrast, the CRF02_AG had multiple introductions which expanded into larger subepidemics (tMRCAs; 1974, 1972 and 1961) [12] . Another study in Guinea-Bissau by Esbjörnsson et al. (based on 82 Guinean HIV-1 env sequences collected 1993-2008) found that the dominating HIV-1 strains were CRF02_AG and sub-subtype A3. In line with the study by Nazziwa et al., both subepidemics originated in the capital before dispersing out to smaller and more rural areas [36] . Interestingly, although the two HIV-1 strains were introduced into the country around the same time (median estimates 1976-1981), the phylogeographic analysis suggested that the CRF02_AG strain started to migrate to more rural areas almost instantly after being introduced into the Capital Bissau. In contrast, sub-subtype A3 was estimated to have circulated within the capital for approximately 10 years before migrating to more rural areas of the country. The underlying reasons for this, however, remain to be determined.

In Cameroon, Véras et al. used 291 HIV-1 CRF02_AG pol sequences collected in 1996-2007 with geographic information system (GIS) data to assess HIV-1 transmission dynamics. Seventy percent of the sequences were found in three distinct clusters, suggesting several subepidemics with different origins [38] . Southern Cameroon has denser human mobility networks compared to the rest of the country; a large cluster comprising sequences from southern Cameroon was identified, suggesting that human mobility may play a role in increasing HIV-1 transmission. In another Cameroonian study, based on 336 HIV-1 gag, pol, and env sequences collected in 1996-2004, two HIV-1 CRF02_AG Cameroonian clusters were identified [39] . Interestingly, both clusters were estimated to have originated in Yaoundé, the capital of Cameroon, before spreading to the Littoral and West regions of Cameroon and remote areas in the South and East. In a study in the Democratic Republic of the Congo (DRC), Faria et al. used a phylogeographic approach to analyse 346 HIV-1 pol sequences (subtypes A1, C and D) collected in 2008 from four locations-the capital Kinshasa, Matadi (West DRC), Mbuji-Mayi (Central DRC), and Lubumbashi (South DRC) [9] . Mbuji-Mayi was suggested as the origin of the subtype C epidemic, whereas the origin for subtypes A1 and D was Kinshasa. The study also indicated that several group M HIV-1 strains had spread from the DRC to other countries. In another study, analysis of env C2V3 sequences collected at multiple sites in the DRC (from Bwamanda in North, Kisangani and Mbuji-Mayi in Central, the Capital Kinshasa, Lubumbashi and Likasi in South), and the Republic of the Congo (from the Capital city Brazzaville, and Porte-Noire in West) suggested that HIV-1 dispersed from Kinshasa to Brazzaville, as well as from Bwamanda and Kisangani [8, 40] . The authors suggested that good transport connectivity and human mobility linked to mining activities may have been involved in the rapid expansion of HIV-1 spread between Kinshasa, Brazzaville, Lubumbashi, and Mbuji-Mayi [8] .

The HIV-1 epidemic in Angola is one of the most diverse in sSA, and all HIV-1 group M subtypes and several CRFs have been identified here [37, 41, 42] . In a study by Bártolo et al., 364 HIV-1 pol sequences collected in 1993-2010 from Luanda and seven other provinces in Angola were analysed. The results indicated that 36% of the sequences formed relatively small Angolan clusters. Seventy-four percent of the sequences in the identified clusters were from Luanda, indicating extensive local transmission and much lower transmission (24% of the clusters) beyond the capital city of Luanda [42] .

The HIV-1 epidemic in Kenya is diverse and has had multiple and separate introductions [10, 43, 44] . Hue and colleagues performed a phylogeographic analysis based on 153 sequences collected in Kilifi county in 2008-2009 together with published Kenyan sequences to investigate how HIV-1 transmission in rural Coastal Kenya related to the region [43] . It was observed that 73% of the HIV-1 sub-subtype A1 sequences from Kilifi clustered with sequences from other areas in Kenya and that there was substantial clustering with strains from other East African countries, such as Uganda and Tanzania, possibly indicating HIV-1 transmission links between these countries. HIV-1 transmission in Uganda has been well studied, especially in rural Southwestern Uganda (which is suggested to be the geographic origin of HIV-1 sub-subtype A1 and subtype D in Uganda) [45] . To understand HIV-1 transmission dynamics in Uganda, Ssemwanga et al. used 3796 HIV-1 pol sequences collected between 2003 and 2015 from Southwestern, Central, and Eastern Uganda [46] . HIV-1 subtype A infections were more common in Central Uganda, whereas subtype D infections were more common in Southwestern Uganda. The study also found a high proportion of localized clustering among sequences from Southwestern Uganda and significant virus export from this region to other regions. However, no virus introductions into this region were observed. In another study, Yebra et al. used 162 HIV-1 pol sequences collected in 2005-2010 from Kampala, Masaka, and Entebbe and 414 previously published pol sequences from Rakai, Kampala, and Entebbe and observed that HIV-1 subtype D initially spread from the rural Southwest, then to the Capital Kampala, before spreading to areas around Lake Victoria [45] . In Ethiopia, the HIV-1 epidemic is dominated by two phylogenetically distinct subtype C types-the Ethiopian HIV-1 C'-ET, and the East African HIV-1 C-EA [47] . Arimide et al. used 301 HIV-1 pol sequences collected in 2003-2013 from Gondar (Northern Ethiopia) to define and understand the transmission dynamics among these variants. The study showed that the C-EA sequences in Gondar clustered with sequences from other East African countries and that multiple introductions of the South African subtype C (C-SA) were observed in Gondar [47] .

In Southern Africa, the mass migration of people into South Africa from neighbouring countries has been suggested to have an impact on the local HIV-1 epidemic [48, 49] . To understand the transmission dynamics of HIV-1 within South Africa and its neighbouring countries, Wilkinson and colleagues analysed 15257 HIV-1 subtype C southern African sequences [50] . The analysis indicated that Johannesburg and KwaZulu-Natal were the main epicentres of HIV-1 dissemination in South Africa. Viruses from KwaZulu-Natal spread to the Northern regions close to the Mozambican and Swaziland borders, and to Johannesburg, whereas viruses from Johannesburg spread to KwaZulu-Natal, Kimberly, Bloemfontein, Mpumalanga, and Western and Eastern Cape. Another study quantified the contribution of local transmission and external introductions to the HIV-1 incidence specifically in KwaZulu-Natal [51] . [54] . The study indicated that the current CRF02_AG diversity resulted from the spread of a small number of founder strains from Central to West Africa in the period of 1960-1980. The study identified five different CRF02_AG variants, four of which were restricted to Cameroon and one that grew out into West Africa. In addition, other phylogeographic studies have indicated Cameroon as the epicentre of the dissemination of HIV-1 CRF11_cpx to Central African Republic, Chad, Gabon, and Equatorial Guinea. However, it has also been suggested that CRF06_cpx spread from Burkina Faso to Mali, Nigeria, and the rest of West-Central Africa [55, 56] .

A phylogeographic study of the dissemination routes of HIV-1 subtype G in West and Central Africa by Delatorre et al., using 305 HIV-1 pol sequences collected in 1992-2011 from 11 countries, showed that the African subtype G epidemic could be divided into two subepidemics according to sequence location, i.e., West and Central Africa [57] . Sequences from West Africa were further subdivided into two large monophyletic clusters that were nested within the Central African variant. One of the Western African variants emerged from Nigeria and spread to Benin, Cameroon, Equatorial Guinea, Ghana, and Senegal. The other West African variant emerged from Togo and/or Ghana from which it spread to Nigeria and then to Benin, Cameroon, Gabon, and Senegal [57] .

To reconstruct the HIV-1 transmission dynamics of subtype C in East Africa, Delatorre et al. analysed 1981 pol sequences collected in 1990-2010 from 13 countries in Central, East, and Southern Africa [58] . Subtype C sequences from East Africa (Burundi, Ethiopia, Kenya, Tanzania, and Uganda) formed one large monophyletic cluster separate from sequences from Southern Africa. In addition to the East African C variant, another monophyletic cluster exclusive to Ethiopia was observed. The East Africa subtype C cluster disseminated from Burundi and later spread to other East African countries where local epidemics were established [58] . A later study including sequences collected in recent years (2013) (2014) (2015) (2016) showed that most of the East African subtype C sequences still clustered into one monophyletic variant, consistent with strong interconnectivity between population centres across the East African region, which has likely fostered the rapid growth of the HIV-1 subtype A1, C, and D epidemic [59, 60] .

A comparative genetic analysis of HIV-1 subtypes A1, C, and D using 8701 pol sequences collected in 1996-2011 from DRC, Burundi, Kenya, Rwanda, Tanzania, and Uganda by Faria et al. indicated that subtypes A1 and D originated from DRC and that sequences from the same regions clustered closely together [9] . Additionally, 80% of total transmissions occurred within national borders and only 20% of transmissions were due to cross-border virus movements. Furthermore, Rwanda, DRC, and Tanzania were identified as the main exporters of subtype C in the Central and Eastern Africa region, whereas Uganda was the source of subtypes A1 and D.

To understand how human migration has influenced HIV-1 diversity and spread in Southern Africa, Wilkinson et al. performed a phylogeographic analysis of 11,289 sequences collected from DRC, Tanzania, Zambia, Malawi, Mozambique, Zimbabwe, Botswana, Namibia, Swaziland, Lesotho, and South Africa. The study showed that the high level of subtype C diversity in South Africa was linked to multiple HIV-1 introductions into the country [49] . Zambia, Botswana, Malawi, and Zimbabwe contributed to most of the HIV-1 introductions into South Africa between 1985 and 2000. However, South Africa also contributed to HIV-1 export to its neighbouring countries. HIV-1 mixing between Zimbabwe and other neighbouring countries (South Africa, Botswana, Zambia, Malawi, Mozambique, and Tanzania) has also been reported in a study by Dalai et al. [61] . Moreover, subtype C sequences from Southern and Central Africa have been shown to cluster closely together but separate from other subtype C sequences from other parts of the world, suggesting strong HIV-1 panmixia in Southern Africa [48, 62] .

In summary, the HIV-1 epidemics in West and Central Africa seem to have emerged and expanded within urban areas before spreading to rural areas-possibly driven by human mobility [12, 36, 39, 42] . In other instances, HIV-1 mixing between rural and urban locations, as well as across national borders, has also been observed [9, 42, 43, 47] . Some HIV-1 subepidemics appear to be localized in specific communities where HIV-1 mixing with neighbouring communities is not observed [54] . In contrast, in other settings localized HIV-1 subepidemics serve as important sources of HIV-1 infection to neighbouring communities [47, 48] . Furthermore, human migration linked to economic activities such as mining and fishing may contribute to increased HIV-1 transmission [9, 49, 63] .

The early HIV-1 epidemic in sSA was exclusively defined as heterosexual and involving FSW and long-distance truck drivers [64] [65] [66] . The role of other HIV-1 key populations such as MSM and PWID was not apparent and this, coupled with ethical-legal hurdles, led to the exclusion of these key populations from early HIV-1 responses in sSA [67] [68] [69] . HIV-1 key populations in sSA are strongly affected by legal and social stigma, where risk behaviour associated with these populations (e.g., same-sex behaviour) are often criminalized [70] . As a consequence, individuals in these populations often withhold risk information, which results in limited HIV-1 research involving key populations [27] . Additionally, there is evidence of overlapping sexual networks and phylogenetic linkages between HIV-1 key populations and HET, which may have implications for the dynamics of HIV-1 spread [44, 65] . [71] . This study was conducted in Mochudi, a periurban community, and it proposed tracking HIV-1 transmission clusters at the community level and extinguishing them, one by one, through targeted interventions. In a similar setting in South Africa, another analysis by Sivay et al. reported partial transmission chains constructed among young women attending high school in rural South Africa (in the context of missed sampling among males in the community) and revealed a stable local epidemic with no evidence of super-spreading events or large networks [72] . In addition, this study also showed that recent HIV-1 transmissions may play a key role in driving the local HIV-1 epidemic. In Zambia, a phylogenetic analysis of 149 married couples by Trask et al. showed that the majority (87%) of couples were epidemiologically linked [73] . However, 13% of pairs in the study had distantly related viruses, suggesting possible extramarital HIV-1 transmission. In Rwanda, a phylogenetic analysis of men and high-risk women in the context of multiple heterosexual partnerships by Rusine et al. identified only three potentially linked transmission pairs [74] . In the DRC, a study by Rubio-Garrido et al. using 165 newly generated HIV-1 pol sequences representing adults and majority paediatric individuals found only four clusters, one of which had sequences from children with no epidemiological links, indicating under sampling in otherwise denser networks [75] . Small clusters have also been observed in Ethiopia by Arimide et al., where data on MSM and PWID is not available, but the epidemic among FSW and HET is acknowledged [47] . Overall, multiple studies in all geographic regions of Sub-Saharan Africa have characterised HIV-1 phylogenetic linkages involving heterosexual transmission. The majority of these studies identified only small clusters, highlighting challenges in sampling coverage which results in many missing links in otherwise large networks [76] .

In the context of homosexual transmission, phylogenetic studies in East Africa have demonstrated extensive clustering among MSM [10, 44, 77, 78] . Studies in Coastal Kenya, have demonstrated extensive clustering of HIV-1 pol sequences from men who have sex with men only (MSM only) and bisexual men, suggesting that bisexual MSM may link infections across different risk groups although such linkages may only be modest as observed in Coastal Kenya [10, 44] . In West Africa, studies in Nigeria have observed clustering among MSM in a cohort involving a majority (62%) bisexual men [79, 80] . Relatively large clusters (with up to 15 individuals per cluster) have been found in this cohort. Interestingly, 37% of bisexual men in this cohort were in clusters involving MSM [80] . In this cohort, clustering between newly infected MSM and previously diagnosed MSM has been reported, indicating ongoing transmission among MSM, the majority of whom were not in treatment and did not report consistent condom use. Elsewhere in the region, phylogenetic clustering analysis of 67 Senegalese MSM (of whom 80% reported to be married) identified 15 transmission clusters, three of which involved MSM from multiple regions in Senegal, indicating linked MSM networks with a wide geographic presence [81] . High numbers of MSM having female sex contacts and exclusive clustering among Senegalese MSM have also been reported by Ndiaye and colleagues [82] . Although cross-risk groups linkages between MSM and HET were not reported in either of these studies in West Africa, such mixing could be expected, to some extent, as has been observed in East Africa [44] . Overall, although MSM in the majority of cohorts in sSA often report being married or having female sex partners, phylogenetic evidence of HIV-1 transmission often reveals MSM exclusive clusters and only a few clusters involving HIV-1 sequences from MSM and HET, suggesting limited mixing.

Phylogenetic studies involving PWID in sSA are exceedingly rare and have only been reported at a subnational scale in Kenya [44, 78] . Nduva et al. used 658 sequences to investigate HIV-1 phylogenetic linkages involving MSM, FSW, PWID, and HET in Coastal Kenya [44] . Whereas MSM, FSW, and HET were found in several small clusters (indicating introduction from multiple sources), the vast majority of PWID sequences were found in one large PWID-exclusive cluster suggesting introduction from one single source and long-term gradual spread within the PWID in Coastal Kenya. Phylodynamic analysis of PWID sequences in this study suggested that HIV-1 infections had increased steadily among PWID since the date of origin in 1987. Additionally, unlike in previous studies (non-African) where PWID sequences clustered with exceptionally low genetic diversity, the genetic diversity among PWID in the Coastal Kenyan cluster was high [17, 21, 83] . The reason for this could be long times between infection and sampling dates and/or low sampling density among PWID in the region. Overall, studies so far suggest separate transmission for PWID with limited overlap between other key populations. However, more research is warranted as the molecular epidemiology of PWID in sSA is largely understudied.

Very few studies in sSA have investigated HIV-1 linkages involving individuals belonging to different risk groups. In Southern Africa, Bártolo et al. assessed HIV-1 phylogenetic linkages in Angola using 364 HIV-1 pol sequences collected in 1993-2010 and identified 48 transmission clusters (size range: two to seven) [42] . More than half of the clustering sequences did not have risk group information. However, three clusters involving mixing between MSM and females were identified, suggesting HIV-1 genetic mixing between HET and MSM. In South Africa, Wilkinson and colleagues detected phylogenetic mixing between HET and MSM, where linkages involving two MSM (infected through homosexual contact) and an incarcerated man (infected in a prison setting) were found within a large cluster dominated by HET (including female individuals) [84] . HIV-1 mixing involving bisexual MSM and HET has also been reported in Cape Town, South Africa [85] .

In West Africa, phylogenetic intermixing of HIV-1 variants between HET women and MSM has also been documented in Senegal, where sequences from HET females were found among MSM clusters [86] . Another study has reported on the intermixing of HIV-1 between MSM and HET in Togo [87] . The authors describe extensive clustering among 79 MSM, where at least 40% of MSM were found in recent transmission chains of two to seven sequences, and where almost half (49%) of MSM were found in one major CRF02_AG cluster, indicating infections within a close network. Additionally, in this study, a comparison of 950 published HIV-1 sequences from HET, perinatally infected infants, and MSM indicated HIV-1 mixing between MSM and HET because strains from infants and HET females were found among MSM-dominated clusters.

In East Africa, two studies in Kenya have reported limited mixing between key populations and HET [44, 77] . Bezemer et al. found only one single transmission pair of an MSM and a known HET female partner in Coastal Kenya-indicating infrequent HIV-1 mixing between MSM and HET in Coastal Kenya [77] . A follow-up study by Nduva et al. used a larger sample size to study mixing between MSM, PWID, FSW, and HET in Coastal Kenya and found that only 7% of the clusters had MSM and HET sequences, indicating limited mixing between MSM and HET in Coastal Kenya [44] .

In Uganda, phylogenetic clustering has been studied among Lake Victoria's fishing communities (considered an HIV-1 vulnerable population) [88] [89] [90] , and HIV-1 mixing between fishing communities and HET residing in in-land regions has been reported [90, 91] . Grabowski et al. showed that HIV-1 diversity is similar both within and between fishing communities and with HET in surrounding communities [91] . In a different study, phylodynamic analysis of sequences from FSW, fishing communities, and HET identified only a few small clusters of exclusively HET individuals [45] . Although the sample size and the sampling coverage were low, no mixing between risk groups was observed. However, in the context of missed sampling of sex partners of FSW, a study in Kampala observed clustering among FSW, suggesting infection from the same source-possibly linked to frequent partner exchange among FSW [92] . Overall, multiple studies have provided evidence of HIV-1 phylogenetic linkages between HIV-1 key populations and HET in Sub-Saharan Africa. A common observation in most of these studies is clustering between HET females and MSM, in addition to the expected links between HET and FSW owing to sex work.

HIV-1 mixing appears to be at relatively low rates across the region (although this has been difficult to quantify empirically because of the dearth of HIV-1 sequence data from MSM, FSW, and PWID).

Few studies have investigated the directionality in HIV-1 transmission involving different risk groups in sSA. Recent phylogenetic analyses have shown that fishing communities do not serve as a source of HIV-1 infection to much larger populations with lower HIV-1 prevalence in Uganda [46, 90, 93] . In Senegal, Nascimento et al. showed that 3.2% of infections in HET females were acquired from MSM, whereas 0.3% infections among MSM were acquired from HET females [94] . [97] . The analysis showed that male partners were the most likely index partners (and hence the source of infection) to married women.

Phylogenetic studies have also revealed the role of age-disparate HET relationships in perpetuating local HIV-1 transmission in Sub-Saharan Africa [46, 98, 99] . In Uganda, Ssemwanga et al. found that HET individuals older than 25 years were more likely to appear in phylogenetic clusters than younger individuals [48] . This study suggested that high-risk HET behaviour involving older individuals living with HIV-1 may drive recurring new infections. In Botswana, a country-wide study involving 6078 sequences by Novitsky et al. identified 984 phylogenetically distinct clusters, revealing complex HIV-1 phylogenetic linkages with mixing between different communities and geographic regions [99] . This study suggested that HIV-1 may first be transmitted from older women to middle aged men, followed by transmission from these men to young women. This HIV-1 transmission cycle had been described earlier in KwaZulu-Natal, South Africa, where HIV-1 is first transmitted from women aged 25-40 years to men aged 25-40 years who would then transmit to girls and young women (15-25 years) [98] . Overall, research has shown that key populations may contribute a modest fraction of infections to the HET population and that key populations may be a sink and not the major source of infections in the mixed epidemic. Further research is needed to reveal the drivers of the HIV-1 epidemic in sSA [90, 100] .

First, most sequence-based studies in sSA have focused on transmitted drug resistance, and more phylogenetic studies dissecting how HIV-1 in different populations mix and spread are warranted. Second, there is a need to incorporate mobility networks into the phylogenetic spatiotemporal models to quantify the movement patterns and links between urban and rural communities more precisely. Although these mobility methodologies have been developed and used to quantify the impact of human mobility on malaria transmission in different African countries, including Kenya and Madagascar, their application in deciphering HIV-1 transmission is limited [101] [102] [103] . While these phylogeographic models can reveal and quantify the movement of viruses between locations, they are limited in the in-depth determination of how and where virus transmission has occurred without additional information, e.g., on human movement. Residents in a community may get infected while living or travelling outside their homes, and such external introductions could be further disentangled by combining movement and migration data with virus data. However, foreseeable hurdles include obtaining mobility data from telecommunication companies as well as individual rights protection issues. Third, many phylogenetic studies in sSA have been constrained by low sampling and limited geographic coverage. This limits the extent to which the entirety of HIV-1 transmission dynamics in a country may be characterised. A low sampling density generally results in missing links and smaller clusters of HIV-1 sequences and may therefore limit the reliability of phylogenetic evidence in guiding policy decisions [76] . A potential solution to this problem would be for studies in sSA to aim to increase sampling efforts to achieve larger and proportional sample coverage across all risk groups and geographic locations. Another related challenge is skewed sampling between risk groups and locations resulting in the over representation of some populations and, as a result, a bias in the phylogenetic assessment of transmission dynamics and trait linkage. In the absence of dense sampling, some insights may be accomplished through subsampling available datasets relative to HIV-1 prevalence per risk group or geographic location for proportional representation, albeit with a loss of links due to exclusion of some sequences [8, 9, 12] . Fourth, a substantial number of published sequences lack information on patient demographics, sampling location, and sampling date, hence limiting their use in phylogeographic studies. In the case of published sequences lacking risk data in sSA, such sequences could be assumed to have been collected from HET individuals (the dominant route of HIV-1 transmission in sSA) [44] . Thereafter, based on phylogenetic clustering, the probable risk group for nodes within a cluster with inadequate annotation may be deduced from association with nodes with a known risk group-as was done to identify potential nondisclosed MSM (self-reported HET men who clustered only with men) in the United Kingdom [104] . With the establishment of the PANGEA consortium (although no data on the contribution of MSM, FSW, or PWID to the epidemic have been reported), a more homogenous and dense sampling from the participating countries may improve and strengthen the limitations of phylodynamic methods [24] . Finally, a potential limitation of our literature search is that it was restricted to studies available only in the PubMed database (https://pubmed.ncbi.nlm.nih.gov/ (accessed on 12 March 2021)). It is therefore possible that some studies were not assessed in our analysis.

Determining the drivers of the HIV-1 epidemic may be important to guide targeted HIV-1 prevention [29] . Phylogenetic methods could help in characterising such drivers but rely on the availability of large numbers of sequences obtained from wellcharacterised cohorts. Where these criteria have been achieved (e.g., in European and Northern American settings with dense sampling among infected individuals and patient demographics), phylogenetic studies have provided useful information for HIV-1 prevention [13, 16, 18, 20, 21, [104] [105] [106] . Low sampling density is a constant limitation to phylogenetic studies in Africa, and the shortage of HIV-1 sequences from key and vulnerable populations has limited our understanding of the contribution of these populations to the HIV-1 epidemic in sSA. Where data involving populations that are at high risk for HIV-1 infection (such as young girls and fishing communities) are available in sSA, phylogenetic characterisation of sources and directionality of HIV-1 transmission involving these vulnerable populations has been achieved [63, 90, 93, 98, 99] . Likewise, if HIV-1 sequences from HIV-1 key populations (i.e., MSM, PWID, and FSW) are made available, phylogenetic studies may guide understanding HIV-1 transmission dynamics and contemporary drivers in these populations. Phylogenetic studies analysing densely sampled and well-characterised HIV-1 key and vulnerable populations sampled in recent years from multiple geographic locations may play a key role in identifying patterns that could be useful in informing HIV-1 prevention strategies in sSA. Overall, although limited, available data from different studies suggest that epidemics among MSM and PWID are more separated and could thus be targeted to reduce population-level incidence. Given that limited HIV-1 sequence data in Africa may continue to present a challenge in the unforeseen future, there is a need to develop statistical and or phylogenetic models that could control for missed sampling.

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