Population mobility and disease progression in people living with HIV: a machine learning analysis of a 10-year dynamic cohort
Yu Luo, Hong Li, Xiyue Fan, Lu Liu, Xinglan Tan, Wenting Yang · 2026
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Description
Yu Luo and colleagues published a 2026 machine learning analysis of a ten-year HIV cohort in Chongqing, China, asking how movement interacts with progression. Residence-based follow-up systems struggle when patients relocate, so the team built a cohort of 5,213 people followed monthly from 2016 to 2025. They separated local cases, prevalent cases, and incoming migrants, then modeled who would move away during care and who would progress to AIDS or death.
Their migration model reached an AUC of 0.849, and analysis singled out migrant status and injection drug use as the strongest markers of unstable residence. Survival modeling showed incoming migrants fared significantly worse than both other groups. Risk of progression climbed in steps after diagnosis ages of 30 and 50, and unemployed or homebound patients carried the highest risk. The authors argue management must shift from static local registration toward shared data across regions and support that follows patients when they move.
Catalog record
| CREATOR | Yu Luo, Hong Li, Xiyue Fan, Lu Liu, Xinglan Tan, Wenting Yang |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 26 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |