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Big Data (Part 2): Flu shots – the right dose for the right strain

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Every year I diligently get in line for my annual flu (or more technically accurate “seasonal influenza”) shot. I’m not particularly fond of needles, but I have seen what the flu can do and the how many die each year from this seasonal virus.

When you get the flu shot – or, now, the nasal mist – you and I are trusting a lot of people that what you are taking will actually help protect you. According to the CDC (Centers for Disease Control and Prevention), there are 3 three strains, (A, B &C Antigenic) of influenza virus and of those three types, two cause the seasonal epidemics we suffer through each year.

Not to get too technical, but I learned that the A strain is further segregated by 2 proteins and are given code names like H1N1, H3N2 and H5N1. They can even be updated by year if there is a change in them. An example of this was in 2009, when the H1N1 became the 2009 H1N1. So where we may just call it H1N1, the World Health Organization has a whole taxonomy to describe a seasonal influenza strain.

 

This taxonomy includes:

  • The antigenic type (e.g., A, B, C)
  • The host of origin (e.g., swine, equine, chicken, etc.)
  • Geographical origin (e.g., Denver, Taiwan, etc.)
  • Strain number (e.g., 15, 7, etc.)
  • Year of isolation (e.g., 57, 2009, etc.)
  • For influenza A viruses, parentheses denote the hemagglutinin and neuraminidase antigens [e.g., (H1N1), (H5N1)]

As you can see, it can really get complicated quickly. If you would like to go deeper, you can read more about this here. While much of this information seems pretty arcane to the lay reader, you quickly can see that the sheer volume of information collected, stored and analyzed to combat seasonal influenza is a great example of big data.

In the US, once the CDC sifts through this data – using big data analytics tools – it uses its findings to determine what strains might affect the US and build a flu shot to combat those strains. During the 2012/2013 season, the predominant virus was Influenza A (H3N2), though some influenza B viruses contained a dash of influenza A (H1N1) pdm09 (pH1N1). (See the full report here.)

In addition to identifying dominant viruses, the CDC also uses big data to track the spread and potential effect on the population. Reviewing information from prior outbreaks, population data, and even weather patterns, the CDC uses big data analytics to quickly estimate and attempt to determine where viruses might hit first, hardest and longest so that a targeted vaccine can be produced in sufficient quantities, in the required timeframe and even for the right geography. The faster and more accurately this can be done, the more people can get this potentially life saving vaccine before the virus travels to their area.

As I stated in my previous blog post, the Hadoop® architecture is a great tool for efficiently storing and processing the growing amount of data worldwide, but Hadoop is only as good as the processing and storage performance that supports it. As with weather predictions, the more data you can quickly and efficiently analyze, the greater the likelihood of an accurate prediction. When it comes to weather and flu vaccines, these predictions can help save lives. In my final blog post in this series, I will explore how big data helps the fashion industry.

Whether in fashion, medical, weather or other fields , the use of Hadoop for high levels of speed and accuracy in big data analysis requires computers with application acceleration. One such tool is Seagate Nytro™ Application Acceleration.

This post is part of a three-part series on real world uses for big data
Part 1 examines big data for weather forecasting.
Part 2 examines big data in the medical industry.
Part 3 examines big data in the fashion industry.

 

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