A patient gets tested with the OraQuick Rapid HIV-1 Antibody test at the Whitman-Walker clinic in Washington. Researchers have developed computer simulations that can accurately predict HIV transmissions across populations. Photo by Michael Kleinfeld/UPI | License Photo
Aug. 2 (UPI) -- Researchers have developed computer simulations that can accurately predict HIV transmissions across populations in an effort to prevent the disease's spread.
The simulations, conducted by the U.S. Department of Energy's Los Alamos National Laboratory, yielded results consistent with actual DNA data from a public database of more than 840,000 HIV sequences from throughout the world. The findings were published this week in the journal Nature Microbiology.
"We looked for special genetic patterns that we had seen in the simulations, and we can confirm that these patterns also hold for real data covering the entire epidemic," lead author Thomas Leitner, a computational biologist at Los Alamos, said in a press release.
HIV, which stands for the human immunodeficiency virus, attacks a person's immune system. It can lead to acquired immunodeficiency syndrome, or AIDS, if not treated.
Because the virus mutates rapidly and constantly within each infected individual, Leitner said HIV is interesting to study this way.
With changing "genetic signatures" of its code, it provides a way for researchers to follow the origin and timeframe of an infection. In the past, computer simulations have been successful in tracking and predicting the virus' movements through populations.
But rapid mutational capability of the virus makes it difficult to disrupt with a vaccine.
Using phylogenetic methods, researchers examined evolutionary relationships in the virus' genetic code to evaluate how HIV is transmitted.
A total of 272 phylogenetic "family tree" patterns were correlated to DNA data from 955 pairs of people. They represented diverse geography, risk groups, subtypes and genomic regions.
"These HIV transmissions had known linkage based on epidemiological information such as partner studies, mother-to-child transmission, pairs identified by contact tracing and criminal cases," the authors wrote.
The researchers plan to develop public health computational tools for agencies to track the disease and allocate resources for prevention. They already are collaborating with Colorado and Michigan state health agencies.
"We hope these tools will help to hinder new infections in the future," Leitner said.
He said these simulations tools can also be used to predict the patterns of other rapidly evolving infectious diseases.