The spread of HIV can be both accurately tracked and predicted using computer simulations according to a study published in Nature Microbiology.
The news could help India build on its success in reducing rates of HIV transmission. New HIV infections in the country fell from 120,000 in 2010 to 88,000 in 2017. AIDS-related deaths fell from 160,000 to 69,000 across the same period.
While this considerable drop in disease cases is a great achievement, this rate of decline is unlikely to continue as the numbers dwindle and cases become limited to areas that fall through the gaps in the healthcare system. Many individuals in rural areas may never have even been tested for HIV.
“The spread of HIV can be both accurately tracked and predicted using computer simulations”
Outbreaks such as one that occurred in Uttar Pradesh may occur in isolated regions and go unchecked for some time, allowing the infection to spread. The source of the infection in Uttar Pradesh was a hotly debated issue, with some blaming a wandering quack doctor known to reuse needles. Others claimed migrant workers moving through the region were responsible.
In order to continue reducing disease cases the use of modern simulation techniques may be vital. In a population of over one billion individuals, tracking where a disease may spread could prove invaluable.
HIV as a viral disease goes through rapid changes to its genetic structure in each individual it infects. These alterations can occur every time the virus replicates within an infected person. It is for this reason that a vaccine is so difficult to establish.
Due to these genetic changes, a course can be tracked to determine the spread and origin of each strain of the virus. This can essentially allow an investigation to pinpoint the individuals that originally began spreading the virus in an area.
It is this data that the study used to evaluate the accuracy of its simulation models.
“HIV as a viral disease goes through rapid changes to its genetic structure in each individual it infects”
“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,” said lead author Thomas Leitner.
The study simulated the spread of the virus based on initial information of the strain coupled with demographic and regional data, then compared the results of the simulation with previously established studies based on genetic data. The results showed that the simulation and the real life data matched up with a fair degree of accuracy.
If the simulation model is developed further it could provide the basis of a predictive modeling tool that could establish the regions where the virus is most likely to spread. By doing so, resources needed for prevention campaigns could be more accurately targeted.