Simulation of full HIV cluster networks in a nationally representative model indicates intervention opportunities

Details

• Alternative Title:
  J Acquir Immune Defic Syndr
• Personal Author:
  France, Anne Marie ; Panneer, Nivedha ; Farnham, Paul G. ; Oster, Alexandra M. ; Viguerie, Alex ; Gopalappa, Chaitra
• Description:
  Background:
  
  Clusters of rapid HIV transmission in the United States are increasingly recognized through analysis of HIV molecular sequence data reported to the National HIV Surveillance System. Understanding the full extent of cluster networks is important to assess intervention opportunities. However, full cluster networks include undiagnosed and other infections that cannot be systematically observed in real life.
  
  Methods:
  
  We replicated HIV molecular cluster networks during 2015–2017 in the United States using a stochastic dynamic network simulation model of sexual transmission of HIV. Clusters were defined at the 0.5% genetic distance threshold. Ongoing priority clusters had growth of ≥3 diagnoses/year in multiple years; new priority clusters first had ≥3 diagnoses/year in 2017. We assessed the full extent, composition and transmission rates of new and ongoing priority clusters.
  
  Results:
  
  Full clusters were 3–9 times larger than detected clusters, with median detected cluster sizes in new and ongoing priority clusters of 4 (range 3–9) and 11 (range 3–33), respectively, corresponding to full cluster sizes with a median of 14 (3–74) and 94 (7–318), respectively. A median of 36.3% (range 11.1–72.6%) of infections in the full new priority clusters were undiagnosed. HIV transmission rates in these clusters were >4 times the overall rate observed in the entire simulation.
  
  Conclusions:
  
  Priority clusters reflect networks with rapid HIV transmission. The substantially larger full extent of these clusters, high proportion of undiagnosed infections and high transmission rates indicate opportunities for public health intervention and impact.
  
  
• Keywords:
  Cluster Analysis Computer Simulation HIV-1 HIV Infections HIV Molecular Epidemiology HIV Transmission Cluster Humans Network Simulation Model Phylogeny Transmission Rate Undiagnosed Infections United States

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• Source:
  J Acquir Immune Defic Syndr. 95(4):355-361
• Pubmed ID:
  38412046
• Pubmed Central ID:
  PMC10901443
• Document Type:
  Journal Article
• Funding:
  CC999999/ImCDC/Intramural CDC HHSUnited States/ ; R01 AI127236/AI/NIAID NIH HHSUnited States/ ; Number R01AI127236/NH/NIH HHSUnited States/
• Volume:
  95
• Issue:
  4
• Collection(s):
  CDC Public Access
• Main Document Checksum:
  urn:sha-512:6dda7b0ae703eaab9f66d59e1e650446a44557d2645b40518ab74bdf640ba63a6facaf31f010830ed4f533a9e4623a2f521cc75d261006d231ad1f5be210fc2b
• Download URL:
  https://stacks.cdc.gov/view/cdc/150376/cdc_150376_DS1.pdf
• File Type:
  [PDF - 716.27 KB ]