Phylogeography of HIV-1 suggests that Ugandan fishing communities are a sink for, not a source of, virus from general populations

Bbosa, Nicholas; Ssemwanga, Deogratius; Nsubuga, Rebecca N.; Salazar-Gonzalez, Jesus F.; Salazar, Maria G.; Nanyonjo, Maria; Kuteesa, Monica; Seeley, Janet; Kiwanuka, Noah; Bagaya, Bernard S.; Yebra, Gonzalo; Brown, Andrew J. Leigh; Kaleebu, Pontiano

doi:10.1038/s41598-018-37458-x

Cited by 43 publications

(60 citation statements)

References 58 publications

(84 reference statements)

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“…Among R15 survey participants, 25% of women out-migrated compared to 22% of men (Poisson regression p-value < 0.001), and of R16 participants, 24% of women were in-migrants compared to 19% of men (Poisson regression p-value < 0.001). In-and out-migrants were less likely to be married and to work in agriculture compared to residents ( 4172 (53) 636 (45) 708 (49) Never married 1969 (24) 509 (27) 404 (18) 2933 (37) 614 (43) 570 (39) Previously married 1593 (19) 363 (19) 0.003 462 (21) <0.001 800 (10) 172 (12) <0.001…”

Section: Cross-community Migration Is Pervasive In Rural Ugandamentioning

confidence: 99%

“…Educational status (%) None 1392 (17) 352 (19) 454 (20) 1684 (21) 312 (22) 371 (26) Primary 3514 (42) 757 (41) 882 (40) 3589 (45) 609 (43) 601 (41) Secondary or higher (45) 741 (40) 918 (41) 1631 21228 (16) 251 (17) Bar/Restaurant work 1133 14238 17312 22Student 1434 (17) 254 (14) 138 (6) 1923 25267 (19) 109 (8) Trader/Shop keeper 1207 (15) 294 (16) 416 (19) 1098 14201 14199 14Other 1443 (17) 400 ( 1030 (55) 1124 (51) 5132 65753 53611 (42) Trading community 1158 (14) 337 (18) 465 (21) 842 11225 (16) 251 (17) Fish landing site 1549 (19) 495 ( 571 (26) 614 (43) 821…”

Section: Cross-community Migration Is Pervasive In Rural Ugandamentioning

confidence: 99%

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Migration, hotspots, and dispersal of HIV infection in Rakai, Uganda

Grabowski¹,

Lessler²,

Bazaale³

et al. 2020

Nat Commun

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HIV prevalence varies markedly throughout Africa, and it is often presumed areas of higher HIV prevalence (i.e., hotspots) serve as sources of infection to neighboring areas of lower prevalence. However, the small-scale geography of migration networks and movement of HIV-positive individuals between communities is poorly understood. Here, we use population-based data from~22,000 persons of known HIV status to characterize migratory patterns and their relationship to HIV among 38 communities in Rakai, Uganda with HIV prevalence ranging from 9 to 43%. We find that migrants moving into hotspots had significantly higher HIV prevalence than migrants moving elsewhere, but out-migration from hotspots was geographically dispersed, contributing minimally to HIV burden in destination locations. Our results challenge the assumption that high prevalence hotspots are drivers of transmission in regional epidemics, instead suggesting that migrants with high HIV prevalence, particularly women, selectively migrate to these areas.

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Section: Cross-community Migration Is Pervasive In Rural Ugandamentioning

confidence: 99%

Section: Cross-community Migration Is Pervasive In Rural Ugandamentioning

confidence: 99%

Migration, hotspots, and dispersal of HIV infection in Rakai, Uganda

Grabowski¹,

Lessler²,

Bazaale³

et al. 2020

Nat Commun

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“…They can be used for identifying and monitoring of HIV transmission clusters, identification of local HIV outbreaks and for targeting subsequent public health interventions [2][3][4]. When the location from which the HIV genomes were obtained is known, phylogeographic methods can be used to describe HIV migration within various geographic regions [4][5][6], or among different epidemiological risk groups.…”

Section: Introductionmentioning

confidence: 99%

Phylodynamics Helps to Evaluate the Impact of an HIV Prevention Intervention

Vasylyeva

Zarebski

Smyrnov

et al. 2020

Viruses

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Assessment of the long-term population-level effects of HIV interventions is an ongoing public health challenge. Following the implementation of a Transmission Reduction Intervention Project (TRIP) in Odessa, Ukraine, in 2013–2016, we obtained HIV pol gene sequences and used phylogenetics to identify HIV transmission clusters. We further applied the birth-death skyline model to the sequences from Odessa (n = 275) and Kyiv (n = 92) in order to estimate changes in the epidemic’s effective reproductive number (Re) and rate of becoming uninfectious (δ). We identified 12 transmission clusters in Odessa; phylogenetic clustering was correlated with younger age and higher average viral load at the time of sampling. Estimated Re were similar in Odessa and Kyiv before the initiation of TRIP; Re started to decline in 2013 and is now below Re = 1 in Odessa (Re = 0.4, 95%HPD 0.06–0.75), but not in Kyiv (Re = 2.3, 95%HPD 0.2–5.4). Similarly, estimates of δ increased in Odessa after the initiation of TRIP. Given that both cities shared the same HIV prevention programs in 2013–2019, apart from TRIP, the observed changes in transmission parameters are likely attributable to the TRIP intervention. We propose that molecular epidemiology analysis can be used as a post-intervention effectiveness assessment tool.

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“…Such groups have high incidence for HIV. These groups include sexual workers, fish folk community and truck drivers [6] [7] [8] [9]. It was observed that sex workers, their clients and regular partners of clients, contributed between 7% and 11% of new infections in Uganda, Swaziland and Zambia [8].…”

Section: Introductionmentioning

confidence: 99%

Inferring Multi-Type Birth-Death Parameters for a Structured Host Population with Application to HIV Epidemic in Africa

Kayondo¹,

Mwalili²,

Mango³

2019

CMB

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Human Immunodeficiency Virus (HIV) dynamics in Africa are purely characterised by sparse sampling of DNA sequences for individuals who are infected. There are some sub-groups that are more at risk than the general population. These sub-groups have higher infectivity rates. We came up with a likelihood inference model of multi-type birth-death process that can be used to make inference for HIV epidemic in an African setting. We employ a likelihood inference that incorporates a probability of removal from infectious pool in the model. We have simulated trees and made parameter inference on the simulated trees as well as investigating whether the model distinguishes between heterogeneous and homogeneous dynamics. The model makes fairly good parameter inference. It distinguishes between heterogeneous and homogeneous dynamics well. Parameter estimation was also performed under sparse sampling scenario. We investigated whether trees obtained from a structured population are more balanced than those from a non-structured host population using tree statistics that measure tree balance and imbalance. Trees from non-structured population were more balanced basing on Colless and Sackin indices.

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Phylogeography of HIV-1 suggests that Ugandan fishing communities are a sink for, not a source of, virus from general populations

Cited by 43 publications

References 58 publications

Migration, hotspots, and dispersal of HIV infection in Rakai, Uganda

Migration, hotspots, and dispersal of HIV infection in Rakai, Uganda

Phylodynamics Helps to Evaluate the Impact of an HIV Prevention Intervention

Inferring Multi-Type Birth-Death Parameters for a Structured Host Population with Application to HIV Epidemic in Africa

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