Researchers are utilizing technology to identify populations at higher risk for HIV and provide treatment, as demonstrated by recent studies and research programs. According to Yen-Tyng Chen, PhD, of Rutgers University, more effective strategies are urgently needed to maximize the impact of HIV biomedical interventions among young Black sexually minoritized men (SMM) and transgender women (TW). "SMM and TW aged 25-34 years contribute the highest number of new HIV infections, and Black SMM and TW are more vulnerable to HIV than other racialized groups," said Chen. She attributes these disparities to structural barriers such as access to health care and stigma related to PrEP use or toward racial and sexual/gender minorities.
In a study involving over 200 individuals, researchers used GPS data to identify venue-based networks for providing HIV prevention interventions. Venues where individuals socialize can play a role in both transmission prevention strategies. The study focused on Black SMM and TW in Chicago, mapping GPS data to venues likely to reach local candidates for PrEP intervention. Participants carried a GPS device for an average of 11 days, visiting pre-identified friendly venues within 50 miles during the study period.
Chen explained that they use big data including GPS, survey, and network data to identify optimal venues for HIV prevention. "By identifying important, feasible, and contextually acceptable venues for HIV interventions, we can maximize the impact of HIV PrEP intervention," she added. However, challenges remain with this approach due to incomplete venue lists and lack of time spent at each location. Future studies aim to develop implementation strategies based on this approach.
Meanwhile, researchers at Yale University are developing technology using temporal patterns to identify early signs of HIV outbreaks. Gregg Gonsalves, PhD from Yale School of Public Health stated that "to maximize the impact of our work," they consulted with health officials and community organizations. He noted that since 2014 there have been over a dozen outbreaks among drug users in the U.S., highlighting clinical implications requiring treatment.
The Yale team is designing algorithms for predicting high-risk areas for outbreaks through early detection methods while focusing on temporal pattern recognition, refined outbreak detection, and adaptive case-finding strategies. They plan on improving traditional surveillance by exploring temporal patterns linked with current case counts alongside historical data like overdose rates.
Gonsalves emphasized high-impact prevention requires containment across an outbreak's lifecycle: pre-deploying resources correctly identified as high-risk before an outbreak occurs; shortening detection time; distinguishing real outbreaks from false alarms; efficiently allocating resources during ongoing outbreaks.
Both projects received support from the National Institute on Drug Abuse with no financial conflicts disclosed by researchers involved.
