Inferring disease transmission networks is important in epidemiology in order to understand and prevent the spread of infectious diseases. Reconstruction of the infection transmission networks requires insight into viral genome data as well as social interactions. For the HIV-1 epidemic, current research either uses genetic information of patients' virus to infer the past infection events or uses statistics of sexual interactions to model the network structure of viral spreading. Methods for a reliable reconstruction of HIV-1 transmission dynamics, taking into account both molecular and societal data are still lacking. The aim of this study is to combine information from both genetic and epidemiological scales to characterize and analyse a transmission network of the HIV-1 epidemic in central Italy.We introduce a novel filter-reduction method to build a network of HIV infected patients based on their social and treatment information. The network is then combined with a genetic network, to infer a hypothetical infection transmission network. We apply this method to a cohort study of HIV-1 infected patients in central Italy and find that patients who are highly connected in the network have longer untreated infection periods. We also find that the network structures for homosexual males and heterosexual populations are heterogeneous, consisting of a majority of 'peripheral nodes' that have only a few sexual interactions and a minority of 'hub nodes' that have many sexual interactions. Inferring HIV-1 transmission networks using this novel combined approach reveals remarkable correlations between high out-degree individuals and longer untreated infection periods. These findings signify the importance of early treatment and support the potential benefit of wide population screening, management of early diagnoses and anticipated antiretroviral treatment to prevent viral transmission and spread. The approach presented here for reconstructing HIV-1 transmission networks can have important repercussions in the design of intervention strategies for disease control.
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J Int AIDS Soc
February 2025
AP-HP, Hôpital Bichat Claude Bernard, Service de Virologie, INSERM, IAME, Paris, France.
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January 2025
Guangxi Key Laboratory of AIDS Prevention Control and Translation, Guangxi Zhuang Autonomous Region Center for Disease Control and Prevention, Nanning, Guangxi, China.
In Guangxi, the number of newly diagnosed HIV-1 infections among students is continuously increasing, highlighting the need for a detailed understanding of local transmission dynamics, particularly focusing on key drivers of transmission. We recruited individuals newly diagnosed with HIV-1 in Nanning, Guangxi, and amplified and sequenced the HIV-1 pol gene to construct a molecular network. Bayesian phylogenetic analysis was utilized to identify migration events, and multivariable logistic regression was employed to analyze factors influencing clustering and high linkage.
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State Key Laboratory for Animal Disease Control and Prevention, Harbin Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Harbin, China.
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January 2025
State Key Laboratory of Infectious Disease Prevention and Control, National Center for AIDS/STD Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China.
Introduction: The widespread adoption of integrase strand transfer inhibitors (INSTIs) has led to the emergence of INSTI-associated drug-resistance mutations. This cross-sectional study conducted a comprehensive national survey to investigate the prevalence of pretreatment drug resistance (PDR) to INSTIs among newly diagnosed human immunodeficiency virus (HIV) individuals in China.
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Viruses
January 2025
Chemical Biology Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Frederick, MD 21702, USA.
Second-generation integrase strand transfer inhibitors (INSTIs) are strongly recommended for people living with HIV-1 (PLWH). The emergence of resistance to second-generation INSTIs has been infrequent and has not yet been a major issue in high-income countries. However, the delayed rollouts of these INSTIs in low- to middle-income countries during the COVID-19 pandemic combined with increased transmission of drug-resistant mutants worldwide are leading to an increase in INSTI resistance.
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