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Forecasting HIV-1 Genetic Cluster Growth in Illinois,United States
Manon Ragonnet-Cronin1,2, Christina Hayford3, Richard D'Aquila3
1Department of Medicine, University of California San Diego, San Diego, CA.
Journal of Acquired Immune Deficiency Syndromes (1999)
|December 8, 2021
Summary
Past HIV cluster growth accurately predicts future transmission clusters. Growth-based prioritization schemes, like those used by the Centers for Disease Control and Prevention (CDC), are effective for public health surveillance.
Area of Science:
- Epidemiology
- Molecular Epidemiology
- Public Health Surveillance
Background:
- HIV interventions targeting transmission networks can disrupt spread.
- Molecular HIV sequence data identifies transmission clusters.
- Cluster growth metrics have shown utility in US HIV surveillance.
Purpose of the Study:
- To compare HIV cluster prioritization schemes using Illinois data.
- To assess the predictive ability of cluster growth for future transmission.
Main Methods:
- Analysis of HIV sequence data from Illinois, including Chicago.
- Evaluation of cluster membership and growth prioritization schemes.
- Comparison of Centers for Disease Control and Prevention (CDC) and New York City (NYC) schemes.
Main Results:
- Past cluster growth significantly predicted future cluster growth.
- No substantial difference found between CDC and NYC prioritization schemes.
- Simultaneous selection by both schemes offered no additional predictive improvement.
Conclusions:
- Growth-based prioritization schemes are effective for HIV surveillance.
- These automated tools can identify actively occurring HIV transmission clusters.
- Health departments can use these schemes to guide public health responses.
