By Jason Semprini

Roughly two years prior to becoming Vice President of the United States, then Governor Mike Pence declared a public health emergency in Indiana. Amid the statewide opioid crisis, and fueled by heightened levels of poverty, Scott County experienced one of the largest known AIDS outbreaks in U.S. history. One community of less than five thousand people had 135 diagnosed cases of HIVten times the national rate. The outbreak led to a series of evidence-based public health interventions unprecedented in rural regions. One study from the Journal of Infectious Diseases integrated surveys, medical tests, and big-data analytics to map the social network of this HIV outbreak, bridging the gap between study techniques, clarifying how the virus spread, and suggesting future policy interventions to prevent the next epidemic.

One reason HIV is so difficult to treat is because the virus is able to quickly adapt and evolve after infection. This study aimed to understand the dynamic relationships of this epidemic—connections from person to person, and connections from HIV adaptation to adaptation—by identifying high-risk groups through social network mapping.

The investigators began by interviewing and conducting medical tests on residents of Scott County to match risk-behavior and biometric records with HIV genome data. Rather than asking about high-risk behaviors, the interviews focused on high-risk contacts. Each individual was asked with whom they had engaged in risky activities (e.g. unprotected sex, needle sharing). Next, the researchers mined viral databases from the Centers for Disease Control. The viral genome data identified the specific strand of HIV, which, when added into this network analysis model, predicted not just the length of HIV infection, but the point of transmission.

A simulation linked each person to those they had engaged with in high-risk behavior and confirmed that 80 percent of all the high-risk connections involved only injection drug use. Nearly all of the individuals with three or more drug-injection partners, roughly 92 percent, were HIV positive by the time of the study. Mapping the genomic data with these high-risk connections confirmed that 52 percent of all HIV transmissions were the result of injection drug-use only (40 percent unconfirmed, 6 percent sex and drug-use, and 1 percent sex only). This finding validated the establishment of syringe-exchange programs initiated after the emergency declaration.

The final component of the study aimed to understand this outbreak’s growth. Researchers accurately estimated how long each individual had HIV by tracking viral adaptation of each from high-risk contacts and point of infection. The investigators found that this emergency epidemic started with a single strain of HIV from an (undiagnosed) individual who acquired HIV/AIDS a decade before the outbreak. The subsequent infections were estimated to have started mid-2013, with 80 percent of those receiving a diagnosis within the two-year period before the March 2015 declaration.

After exponential growth, the epidemic leveled off within three weeks of the declaration due, in part, to the subsequent commitment to sound public health interventionsStategovernments across the country are financially supporting syringe-exchange programs, HIV reporting platforms, and other evidence-based methods to fight the side effects of the opioid crisis. This study adds to that effort by highlighting modern statistical tools and bioinformatic approaches for analyzing pre-epidemic outbreaks. While there are not enough resources to survey and test every individual in a potential hot zone, the technological capacity of automatic research software, as well as our ability to manipulate them, is improving. The investigators predict that we are nearing the ability to automate network models of high-risk rural communities. Then, the question for policymakers will not be whether or not we are facing a crisis, but how to identify and treat an entire region before an epidemic ever occurs.

Article source: Campbell, E. M., H. Jia, A. Shankar, D. Hanson, W. Luo, S. Masciotra, S. M. Owen, A. M. Oster, R. R. Galang, M. W. Spiller, S. J. Blosser, E. Chapman, J. C. Roseberry, J. Gentry, P. Pontones, J. Duwve, P. Peyrani, R. M. Kagan, J. M. Whitcomb, P. J. Peters, W. Heneine, J. T. Brooks, and W. M. Switzer. “Detailed Transmission Network Analysis of a Large Opiate-Driven Outbreak of HIV Infection in the United States.” The Journal of Infectious Diseases, Vol. 216, Issue 9 (2017): 1053–1062.