Comparative Circulation Dynamics of the Five Main HIV Types in China

Abstract

The HIV epidemic in China accounts for 3% of the global HIV incidence. We compared the patterns and determinants of interprovincial spread of the five most prevalent circulating types. HIV pol sequences sampled across China were used to identify relevant transmission networks of the five most relevant HIV-1 types (B, CRF01_AE, CRF07_BC, CRF08_BC and CRF55_01B) in China. From these, the dispersal history across provinces was inferred. A generalized linear model (GLM) was used to test the association between migration rates among provinces and several measures of human mobility. A total of 10,707 sequences between 2004-2017 across 26 provinces were collected, among which 1,962 newly reported here. A mean of 18 (Min-Max:1-54) independent transmission networks involving up to 17 provinces were identified. Discrete phylogeographic analysis largely recapitulate the documented spread of the HIV types which, in turn, to large extent mirror within-China population migration flows. In line with the different spatio-temporal spread dynamics, the identified drivers thereof were also heterogeneous but are consistent with a central role of human mobility. The comparative analysis of the dispersal dynamics of the five main HIV types circulating in China suggests a key role of large populations centers and developed transportation infrastructures as hubs of HIV dispersal. This advocates for coordinated public health efforts in addition to local targeted interventions. IMPORTANCE While traditional epidemiological studies are of great interest in describing the dynamics of epidemics, they cannot fully capture the geospatial dynamics and factors driving the dispersal of pathogens such as HIV as they struggle to capture linkages between infections. To overcome this, we used a discrete phylogeographic approach coupled to a generalized linear model extension to characterize the dynamics and drivers of the across-province spread of the five main HIV types circulating in China. Our results indicate that large urbanized areas with dense population and developed transportation infrastructures are facilitators of HIV dispersal throughout China, and highlight the need to consider harmonized country-wide public policies to control local HIV epidemics.

The prevalence of CRF55_01B among HIV-1 strain and its connection with traffic development in China

“…This study found that CRF55_01B had some historical circulation in 19 provinces (Table S9). Compared with other studies [7,12], this study obtained more information about the spread of CRF55_01B in other provinces.…”

“…CRF55_01B stood out among CRF01_AE and B subtype recombinant strains (CRF59_01B [8], CRF67_01B, CRF68_01B [9]) discovered in the same year, as well as many new CRFs (CRF79_0107 [10], CRF80_0107 [11], etc.) found in MSMs and became the fifth most prevalent strain of HIV-1 in China [12]. Therefore, this study will focus on the analysis of how CRF55_01B rapidly spread to the whole country in a short time and the characteristics of transmission among provinces and infected people.…”

Long-Term Changes of HIV/AIDS Incidence Rate in China and the U.S. Population From 1994 to 2019: A Join-Point and Age-Period-Cohort Analysis

“…With its immense population and as the largest developing country in the world, minds, economy, lifestyle, even attitudes to sex in Chinese citizens were gradually close to the U.S. ( 12 ). Men who have sex with men (MSM), heterosexuals, and IDU were the three main transmission routes of HIV infections in China and the U.S. More significantly, guidance for the HIV epidemic control efforts in China may be gained from the experience of the U.S. ( 13 ). In addition, the study of Gao et al ( 14 ) found that the risk of HIV mortality in China rapidly increases and Liu et al ( 15 ) denoted existing great differences in the HIV incidence between men and women.…”

The Viral Founder Effect and Economic-Driven Human Mobility Shaped the Distinct Epidemic Pattern of HIV-1 CRF01_AE in Northeast China

“…Therefore, we performed a down-sampling procedure to avoid the over-sampling bias in homosexuals and bisexuals ( Supplementary Table 1 ). The ancestral origins and evolutionary rates among different risk groups were estimated using the GTR+I+G4 model, a relaxed lognormal clock model and a Bayesian non-parametrized Skygrid model ( 15 ) in BEAST v1.10.4. The reference sequences from Africa and Thailand ( 16 ) were chosen to fix the topology of the maximum clade credibility (MCC) tree and to calibrate the time to most recent common ancestor (tMRCA).…”