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Cloud computing and epidemiology

Recently, I have stumbled over quite a lot of articles which focus on epidemiology (in a broader sense) and cloud computing.

I definitely do believe in the positive impact of cloud computing on science. In my opinion, this new IT paradigm will bring significant changes to the way research is done today.

Before I will start talking about possibilities and benefits, I would like to give a very brief introduction to cloud computing (I think most of you will know): basically, with cloud computing you can free your applications from your local hardware by accessing powerful computer clusters via the Internet.

The status quo and recent developments

So far, universities and research institutes have relied on their internal sever landscapes for conducting research projects. Until now, there hasn’t been any problem with that. However, several disadvantages are becoming increasingly apparent:

  • High cost: growing server landscapes require large upfront investments and require large maintenance cost
  • Local server capacity is still limited

In addition to that, the following developments in science pose further challenges on local infrastructure:

  • Science discovers at an increasing pace of speed. Therefore, increasing time efficiency in scientific research plays a decisive role.
  • The amount of Data increases potentially. The more sophisticated data becomes, the more complex and data intensive gets its analysis.

Thanks to cloud computing there is a comprehensive approach for solving the stated problems. Scalable server infrastructures allow for more complex and resource intensive calculations and increase speed. Depending on the utilization rate of local servers, significant cost savings result.

Genome sequencing

I would like to introduce genome sequencing for better illustrating the benefits of cloud computing in regard to big data and speed of calculations. Genomic sequencing can be defined as “a laboratory process that determines the complete DNA sequence of an organism’s genome at a single time” .

In a more concrete example, imagine that 250 potential virulence factors, or molecules that are secreted by bacteria, viruses, fungi, or protozoa and then multiply within humans, create nearly 2.3 million three-dimensional models with nearly 30,000 background data packets to study the function of these harmful, disease causing pathogens. In order to make sure that no attribute is missing, scanning is repeated 30 to 40 times.

To show DNA sequenzing’s potential in connection with cloud computing see this year’s practical example:

Genomic analysis enabled the treatment of a California teenager who had difficulty breathing. While doctors couldn’t find out by traditional diagnostic methods what was wrong, mapping of the girl’s genetic code enabled scientists to pinpoint the problem and treat it successfully.

Cloud computing and epidemiology: a matching couple

I think that epidemic research and cloud computing are a matching couple. What can be achieved for individuals now (see the teenager’s example), will be possible for large populations tomorrow soon – due to shorter innovation cycles in technological innovation.

In the future, science will dig deeper in details producing even more data. It will be vital to establish an infrastructure system that will help to process the data generated.

Cloudtweak even goes as far as to say that

„cloud computing can have a monumental impact.“


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