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oai:figshare.com:article/34038915 2026-10-01T04:50:05Z category_303 portal_316 item_type_3 month_year_10_2026

Table 2_Identification of active sequences in HIV-1 molecular transmission networks using machine learning: a cross-sectional study in Hainan Province, south China.docx Dee Yu (12967749) Nengde Zhang (25152300) Xin Chen (14149) Ruolan Fu (24603999) Zhenjiao Zhang (15366743) Chunyun Lu (18081590) Jing Su (23377) Qionggui Zhou (23168797) Qiongjun Xu (17294970) Haiqing Xu (494011) Bo Bi (576362) Zhaoqian Wang (23729522) JindongDing Petersen (25152303) Clinical Microbiology active sequence cross-sectional study gradient boosting model HIV-1 molecular transmission network Background<p>New HIV-1 infections continue to increase in Hainan Province, a major tourist destination in southern China characterized by high population mobility and diverse viral subtypes. Understanding transmission dynamics within molecular networks is critical for precision prevention.</p>Methods<p>We conducted a cross-sectional study of participants living with HIV-1 in Hainan Province between 2021 and 2025. HIV-1 molecular transmission networks were constructed using HIV-TRACE. Sequences from individuals diagnosed by the end of 2023 were used to construct the baseline network, and sequences from newly reported cases during 2024–2025 were added to form the final network. Active sequences were defined as those from newly reported cases in 2024–2025 or those directly linked to newly reported cases within the final network. Multivariable logistic regression was used to identify factors independently associated with active sequences, and gradient boosting to assess variable importance.</p>Results<p>Among 2,342 sequences, 1,071 (45.7%) were included in the final molecular transmission network, comprising 505 (47.2%) active sequences. Multivariable analysis showed that male sex (aOR = 2.340, 95% CI:1.416–3.866), age ≥50 years (aOR = 3.806, 95% CI:2.485–5.830), drug resistance (aOR = 1.777, 95% CI:1.227-2.575), and other subtypes (aOR = 2.204, 95% CI:1.560-3.115) were independently associated with active sequences. The gradient boosting model corroborated these findings, ranking age at diagnosis, HIV-1 subtype, drug resistance, and sex as the top four factors discriminating active sequences.</p>Conclusions<p>Our findings indicate that older age (≥50 years), less prevalent subtypes, drug resistance, and male sex are key factors associated with active transmission in Hainan. Targeted interventions, encompassing routine screening for older adults, baseline resistance testing, and enhanced molecular surveillance, should be prioritized to curb ongoing HIV-1 dissemination.</p> 2026-10-01T04:50:05Z Dataset Dataset 10.3389/fcimb.2026.1925404.s002 https://figshare.com/articles/dataset/Table_2_Identification_of_active_sequences_in_HIV-1_molecular_transmission_networks_using_machine_learning_a_cross-sectional_study_in_Hainan_Province_south_China_docx/34038915 CC BY 4.0