Advances in Research of HIV Transmission Networks
Transmission network analysis is a critical tool for understanding the characteristics of the human immunodeficiency virus (HIV) epidemic, developing evidence-based prevention strategies, and contributing to various aspects of HIV/acquired immunodeficiency syndrome (AIDS) prevention and control. Over the past few decades, significant progress has been made in the modes, methods, and applications of transmission networks. These networks, including social, sexual, and molecular transmission networks, have played a pivotal role in HIV research. Each method has its advantages and limitations, and this review systematically examines these networks in terms of their definitions, applications, limitations, recent progress, and synthetic applications.
Introduction
Network analysis provides a framework for applying analytical insights and computing techniques to study social support, social impacts, and infectious disease flows within populations and organizations. Viewing the entire population as a group of interconnected individuals forming a large social network offers a more comprehensive understanding of the propagation of infectious diseases like HIV. Early research on transmission networks utilized social networks to explore the characteristics of the HIV/AIDS epidemic and corresponding prevention strategies. HIV transmission typically occurs through risky behaviors, such as close and frequent contacts, making transmission networks essential for understanding the social relationships in which these contacts are embedded. Over time, transmission network studies have made significant progress in models, methods, and applications, leading to a better understanding of HIV spread and the development of effective intervention strategies.
Social Networks
Definition
A social network consists of a group of social participants or node members connected by one or more types of relationships. Social network analyses include studies of egocentric (or local) networks and sociocentric (or sociometric, complete, or global) networks. Egocentric networks focus on direct connections to focal individuals (ego), while sociocentric networks include both direct and indirect connections, mapping the entire sample. Characteristics of sexual networks, such as size, composition, and density, are associated with HIV risk behaviors like sharing syringes, drug addiction, having multiple concurrent sexual partners, and practicing unprotected sex. Structural network characteristics can be described from multiple perspectives, such as centrality and groups. Common centrality measures include degree, closeness, and betweenness. Degree refers to the number of links to and from a particular individual, closeness measures the average distance between a node and every other node in the network, and betweenness is the frequency of ties with which an individual is on the shortest path connecting pairs of others in the network. Groups are defined as any subset of a network, and parameters like K-core, n-Clique, and k-plexes determine the network structure.
Applications
Social network analyses have diverse applications in HIV research, prevention, and treatment. They can collect valuable information on the HIV epidemic, investigate individual behaviors related to HIV risk factors, and study patterns of high-risk behaviors like drug use and sexual behaviors. Social networks facilitate the collection of static information on network members and their relationships, as well as the exploration of dynamic characteristics. They also provide low-cost and sustainable HIV prevention interventions, such as peer education for intravenous drug users (IVDU) and reaching men who have sex with men (MSM) for HIV testing and healthcare linkage. Social networks are also useful for optimizing HIV medical care and adherence.
Limitations
Despite their advantages, social network approaches have several limitations. HIV patients may have been infected for several years by the time of diagnosis, and social network data are often collected through face-to-face interviews or self-reports, leading to recall and social desirability bias. Most index participants are recruited through targeted outreach, which may introduce selection bias. Additionally, linkages may change during the investigation period, potentially misinterpreting the statistical validity of relationships.
Advances
Recent advances in social network research include the use of online platforms and other methods to improve social network analysis and intervention. Online tools like Facebook have been used to increase HIV prevention discussions and testing requests among at-risk groups. Social networks have also been used to understand the characteristics of HIV transmission, such as collective venue avoidance among younger African-American MSM and the impact of social network factors on Pre-exposure prophylaxis (PrEP) use among young MSM (YMSM) and young transgender women. Social networks have also been used to explore the impact of non-parental adults (NPAs) on substance use among YMSM and the social network characteristics associated with depressive symptoms and social support among HIV-infected women of color (WOC). Interventions like chemsex (sexualized drug use) among MSM and HIV-positive individuals as advocates in their social networks have been explored, demonstrating the high acceptability and feasibility of such interventions.
Sexual Networks
Definition
Sexual networks consist of people having either direct or indirect sexual contact with each other, typically referring to the retrospective sets of individuals who have engaged in sexual contact during a specified period. Sexual networks are critical for sustaining the spread of HIV in communities and have key characteristics, including a sufficient number of individuals, medium to high density of contact, centrality of infected persons, and sexual partner selection patterns. Sexual networks can overlap with social networks but have their own unique focus and characteristics. They can be illustrated using random graphs, where nodes represent individuals and edges represent sexual contact. The number of edges adjacent to a particular node indicates the degree, and the sets of node degrees denote the degree of distribution of the population. Changes in the degree distribution are associated with the propagation of HIV.
Applications
The characteristics of sexual networks, such as size, density, and structure, are closely related to HIV transmission. Studies have shown that extensive and stable sexual networks are associated with higher HIV prevalence, and network characteristics are a determinant of HIV transmission dynamics within the population. Sexual network models are characterized by concurrency and mixing between different subpopulations, contributing to the spread of HIV. Concurrent sexual partnerships, defined as two or more partnerships with overlapping dates, increase the likelihood of HIV transmission. Sexual mixing patterns, such as assortative and disassortative mixing, also play a critical role in HIV transmission. Prevention strategies can be developed based on the characteristics of sexual transmission, such as behavioral and biological interventions for community-wide risk reduction and the use of suppressive antiretroviral therapy (ART) in serodiscordant couples.
Limitations
Limitations of sexual network analyses include recall bias, as individuals may not report all sexual partners. Network members from outside the research area may not be enrolled, limiting the ability to generalize findings. There is also potential for distortion of self-reported risk behaviors and sex network members due to social desirability. Quantifying the degree in sexual networks using self-reported cross-sectional data may lead to bias due to the uncertainty of future sexual behavior.
Advances
Recent research on sexual networks has focused on understanding the characteristics of HIV transmission and formulating effective prevention strategies. Studies have explored the sexual networks of female sex workers (FSWs) and their role as bridges in HIV/AIDS transmission. Sexual network analysis has also been used to explore the reasons for the decline in HIV diagnoses among Black/African-Americans. HIV self-testing (HIVST) among MSM based on sexual networks has been shown to be feasible and effective. Disclosure of HIV status has been associated with lower HIV sexual risk behaviors among HIV-positive African-American MSM, providing a potential guide for developing prevention protocols.
Molecular Transmission Networks
Definition
Molecular transmission networks leverage the high evolution rate of HIV to establish connections between viral strains from different individuals. The degree of each individual in the network is defined as the number of links with other individuals, and clusters are defined as connected components of the network containing two or more nodes. Epidemiologic contact information is not necessary for clustering in molecular networks, as the link between individuals does not imply direct transmission but rather two recently related viral strains, referred to as putative transmission chains. Genetic clustering methods include pairwise distance measures and phylogenetic subtree interpretations. Pairwise genetic distances are computed using models like the Tamura-Nei 93 (TN93) nucleotide substitution model, and distance cut-offs are defined based on study objectives. Molecular phylogeny is a tree-based model of gene sequence linkage by common ancestors.
Applications
Molecular transmission networks enable the scrutiny of network characteristics on a larger scale and longer study period than social or sexual networks. They can be conducted on a national or global scale, providing a comprehensive depiction of HIV-1 spread. Molecular transmission networks have been applied to describe HIV transmission dynamics, determine the prevalence and features of transmitted drug resistance (TDR), explore epidemic history, identify highly relevant transmission groups, detect recent outbreaks, and investigate hotspots of rapid transmission. As HIV treatment centers accumulate sequence-based genotypes, there is growing interest in using this resource for real-time HIV prevention and control measures.
Limitations
Molecular transmission network analyses have limitations, including the inability of phylogenetic methods to confirm direct transmission or the order of transmission. Contributions of undiagnosed individuals, diagnosed individuals without genotyping, and individuals tested before or after the study period cannot be assessed. Additionally, clusters do not always reflect outbreaks, as HIV molecular epidemiology hypothesizes that outbreaks are usually reflected as clusters, but the reverse is not always true.
Advances
Recent research on molecular transmission networks has focused on identifying transmission networks among populations, monitoring HIV transmission dynamics, identifying long-term trends, establishing innovative analytical methods, developing intervention strategies, and evaluating intervention effectiveness. Studies have used partial pol gene sequences to construct transmission networks, investigate HIV-1 transmission networks among MSM, IVDUs, FSWs, and heterosexuals, and analyze sequences from immigrants to explore their connectivity with their countries of origin. Models using molecular HIV surveillance data have been developed to predict the probability of transmission, and the relationship between progression through care continuums and transmission clusters has been explored. Molecular epidemiological analysis has been used to assess the effectiveness of interventions.
Combinations of Networks
There is no universal method for understanding HIV propagation, and a comprehensive understanding can only be achieved by combining multiple network methods. Transmission networks, including social, sexual, and molecular transmission, are of immense value for preventing and controlling the HIV epidemic. Each network has its own focus and advantages, as well as limitations. Effective combinations of various transmission network methods can provide a more comprehensive and multi-dimensional picture of the HIV epidemic, leading to well-rounded, efficient, and accurate descriptions of HIV transmission characteristics and the formulation of powerful prevention and intervention measures. Studies have investigated the role of social and sexual network factors in PrEP use among YMSM and transgender women, explored the impact of HIV status disclosure to social and sexual partners among YMSM, and compared sexual/drug-use partners and genetic transmission networks. Combining various network data can provide valuable information for prevention and intervention, such as integrating phylogenetic, clinical, and behavioral data to analyze transmission dynamics and developing transmission network scores (TNS) to assess the risk of HIV transmission and tailor effective prevention interventions.
In conclusion, this review provides an overview of the basic definitions, common applications, existing limitations, and recent research progress of social, sexual, and molecular transmission networks, as well as their joint application in related research. Combining various transmission networks increases our understanding of HIV transmission networks, which is essential for monitoring, preventing, and ultimately eradicating HIV.
doi.org/10.1097/CM9.0000000000001155
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