Short-Term Dynamic and Local Epidemiological Trends in the South American HIV-1B Epidemic.
The human displacement and sexual behavior are the main factors driving the HIV-1 pandemic to the current profile. The intrinsic structure of the HIV transmission among different individuals has valuable importance for the understanding of the epidemic and for the public health response. The aim of this study was to characterize the HIV-1 subtype B (HIV-1B) epidemic in South America through the identification of transmission links and infer trends about geographical patterns and median time of transmission between individuals. Sequences of the protease and reverse transcriptase coding regions from 4,810 individuals were selected from GenBank. Maximum likelihood phylogenies were inferred and submitted to ClusterPicker to identify transmission links. Bayesian analyses were applied only for clusters including ≥5 dated samples in order to estimate the median maximum inter-transmission interval. This study analyzed sequences sampled from 12 South American countries, from individuals of different exposure categories, under different antiretroviral profiles, and from a wide period of time (1989-2013). Continentally, Brazil, Argentina and Venezuela were revealed important sites for the spread of HIV-1B among countries inside South America. Of note, from all the clusters identified about 70% of the HIV-1B infections are primarily occurring among individuals living in the same geographic region. In addition, these transmissions seem to occur early after the infection of an individual, taking in average 2.39 years (95% CI 1.48-3.30) to succeed. Homosexual/Bisexual individuals transmit the virus as quickly as almost half time of that estimated for the general population sampled here. Public health services can be broadly benefitted from this kind of information whether to focus on specific programs of response to the epidemic whether as guiding of prevention campaigns to specific risk groups.
Centro Universitário Ritter dos Reis-Uniritter, Departamento de Ciências da Saúde, Porto Alegre, RS, Brazil.|Programa de Pós-Graduação em Genética e Biologia Molecular, Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, RS, Brazil.|Centro de Desenvolvimento Científico e Tecnológico (CDCT), Fundação Estadual de Produção e Pesquisa em Saúde (FEPPS), Porto Alegre, RS, Brazil.
Dennis Maletich
DM
Junqueira
Programa de Pós-Graduação em Genética e Biologia Molecular, Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, RS, Brazil.|Centro de Desenvolvimento Científico e Tecnológico (CDCT), Fundação Estadual de Produção e Pesquisa em Saúde (FEPPS), Porto Alegre, RS, Brazil.
Rubia Marília
RM
de Medeiros
Centro de Desenvolvimento Científico e Tecnológico (CDCT), Fundação Estadual de Produção e Pesquisa em Saúde (FEPPS), Porto Alegre, RS, Brazil.|Programa de Pós-graduação em Biotecnologia e Biociências, Universidade Federal de Santa Catarina, Florianópolis, SC, Brazil.
Tiago
T
Gräf
Programa de Pós-Graduação em Genética e Biologia Molecular, Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, RS, Brazil.|Centro de Desenvolvimento Científico e Tecnológico (CDCT), Fundação Estadual de Produção e Pesquisa em Saúde (FEPPS), Porto Alegre, RS, Brazil.|Instituto de Ciências da Saúde, Universidade FEEVALE, Novo Hamburgo, RS, Brazil.
Sabrina Esteves de Matos
SE
Almeida
D001118:Argentina / Q000453:epidemiology; D001499:Bayes Theorem; D001938:Brazil / Q000453:epidemiology; D016000:Cluster Analysis; D004252:DNA Mutational Analysis; D024882:Drug Resistance, Viral; D058872:Epidemics; D005260:Female; D015658:HIV Infections / Q000453:epidemiology* / Q000821:virology*; D015497:HIV-1; D006801:Humans; D016013:Likelihood Functions; D008297:Male; D009154:Mutation; D010802:Phylogeny; D014687:Venezuela / Q000453:epidemiology