Milenkovic develops model to predict virus outbreaks, develop better vaccines
Kim Gudeman, Coordinated Science Lab
- Professor Olgica Milenkovic developed a mathematical model for predicting which virus strains will be most harmful.
- Vaccine makers can use the information to create more effective vaccines for influenza.
- The model can be applied to many other viruses such as smallpox, but it does not work against retroviruses such as HIV.
Get a flu shot, prevent the flu? While the vaccine often works, there are other years when a high percentage of patients who received the shot get influenza anyway.
ECE Assistant Professor Olgica Milenkovic believes that it’s possible to create more consistently effective vaccines--and that the solution is better math.
Milenkovic and her research team have developed a new mathematical model for predicting which strains of a virus will be the most virulent and harmful to the general population. Vaccine makers can use the information to create more effective vaccines for influenza and other viruses.
“You can only include three or four strains of influenza in a flu vaccine,” said Milenkovic, a resident researcher in the Coordinated Science Laboratory. “What our model does is help you choose the three or four strains most likely to do the most harm.”
Currently, the Center for Disease Control uses random, one-dimensional sampling throughout the country to identify the viruses that appear most frequently. Researchers use the DNA sequence to classify the strains. The center also works with foreign governments since travelers often bring new strains into the country.
But this approach has limitations. Viruses and their DNA mutate quickly. So if enough time elapses between collection points, it’s possible to find two viruses with similar sequences that are completely different strains.
Furthermore, sampling entire households could also throw off the numbers. If people live together, it’s more likely a virus will spread, but it’s not necessarily the best indicator of the virulence of each strain. A home may have 10 people who all catch a mild virus, but the results should be weighed (and perhaps discounted) against a household with four people who get violently ill with an aggressive strain.
Milenkovic’s probability model tackles these problems with algorithms that account for spatial and time coordinates related to sampling. The model reveals the correct distribution of viruses, the frequency of viruses in the population even using a small sample, the percentage of viruses that may be missing from the sampling, and the original frequencies of the viruses independent of sampling techniques.
“When you don’t know the parameters, the algorithm still works,” she said.
The algorithm easily detects the most aggressive viruses. The group also developed an iterative algorithm that identifies viruses that aren’t as virulent, but spread easily, when compared with 200 virus sequences. Milenkovic’s team is still testing the algorithm against larger populations.
In addition to the flu, the model can be applied to many other viruses such as smallpox. The model does not work against retroviruses such as HIV.