Forecasting HIV-1 Genetic Cluster Growth in Illinois,United States

Abstract

Background: HIV intervention activities directed toward both those most likely to transmit and their HIV-negative partners have the potential to substantially disrupt HIV transmission. Using HIV sequence data to construct molecular transmission clusters can reveal individuals whose viruses are connected. The utility of various cluster prioritization schemes measuring cluster growth have been demonstrated using surveillance data in New York City and across the United States, by the Centers for Disease Control and Prevention (CDC). Methods:We examined clustering and cluster growth prioritization schemes using Illinois HIV sequence data that include cases from Chicago, a large urban center with high HIV prevalence, to compare their ability to predict future cluster growth. Results:We found that past cluster growth was a far better predictor of future cluster growth than cluster membership alone but found no substantive difference between the schemes used by CDC and the relative cluster growth scheme previously used in New York City (NYC). Focusing on individuals selected simultaneously by both the CDC and the NYC schemes did not provide additional improvements. Conclusion:Growth-based prioritization schemes can easily be automated in HIV surveillance tools and can be used by health departments to identify and respond to clusters where HIV transmission may be actively occurring.

Characteristics of the different HIV-1 risk populations based on the genetic transmission network of the newly diagnosed HIV cases in Jiangsu, Eastern China

“…However, to fully and deeply analyze the genetic transmission network, we integrated all sequences of the new diagnoses from our database and identified the key populations in the network. Previous studies on the genetic transmission network mostly prioritized the largest clusters or clusters that recently enlarged to generate more nodes for public health surveillance [ 15 , 16 ]. However, the risk factors considered in our study were more comprehensive.…”

Auto-Tune: Selecting the Distance Threshold for Inferring Hiv Transmission Clusters

“…Lower thresholds will capture the most closely related sequences, while higher thresholds will capture long-term epidemics and chronically infected individuals (Junqueira et al, 2019). Cluster analysis, i.e., identification and analysis of connected network components, in public health has been used for early identification of increased transmission (Oster et al, 2021, 2018), monitoring response to an HIV outbreak (Tumpney et al, 2020; Sizemore et al, 2020; Tookes et al, 2020), evaluating the effectiveness of interventions (Peters et al, 2016; Wang et al, 2015; Liu et al, 2020) or predicting clusters that are most likely to grow in the near future (Erly et al, 2021; Ragonnet-Cronin et al, 2022). This balance can be achieved through careful analysis and consideration of the specific disease and context.…”

Modeling homophily in dynamic networks with application to HIV molecular surveillance

“…We refer to the resulting network as a viral genetic linkage network. As in previously reported analyses, two PWH are considered linked if the genetic distance between their HIV sequences is less than a given threshold [ 23 , 25 ]. Previous studies have shown that a short genetic distance between two infected PWH implies proximity in a transmission network–in the sense that either one of the infected people directly transmitted the virus to the other or indirectly transmitted it through a small number of intermediaries [ 2 ].…”