The possibility of worldwide pandemics is likely to increase as travel and international trade grow. But they can be preempted through the use of databases that track key indicators on a global basis.
Recent research has suggested that the Black Death or Plague that devastated Europe in the 1600s actually started in Asia and was carried by gerbils.
Now, with seamless and efficient modern global communications, the threat of widespread pandemics has become a source of considerable worldwide concern. In recent decades we have seen how quickly diseases like H1N1 and Aids can spread across borders and oceans.
Until fairly recently, control of such diseases had to be reactive rather than proactive. But now with much more long-distance travel and freighting, coupled with increasing microbial resistant to antibiotics, this is no longer an adequate response.
So does big data have the potential to predict pandemics, thus giving the medial and healthcare communities have time to prepare an appropriate response?
There would be a need to collect and collate data from many sources. The bulk of this would probably be from global general medical records, so that individual outbreaks could be spotted; if a single case grew into a cluster of several, this could be the earliest possible indication of the beginnings of a widespread problem. If more cases began to appear, their location could be cross-referenced with trade routes and water courses. Further analyses and cross-referencing may include monitoring the populations of animals known to carry certain diseases, focusing on a network of key locations where viral transfer may be most likely, and even monitoring the purchase of prescription and non-prescription pharmaceuticals.
In fact this sort of work is already well-advanced and many successes have been claimed.
Obviously, the data set in such a monitoring system is going to be very, very large and growing constantly. With data being collected from so many sources over very long periods of time, the choice of database management system could be critical to success. Ideally, the technology should be easy to use. It should also be flexible, so that it can be adapted and upgraded over time; there should be no possibility of becoming locked into one vendor’s product (the vendor may cease trading, discontinue the product, or drive up prices). Database management systems like Raima’s RDM may offer the perfect solution for such applications. In terms of flexibility, it can handle APIs (application program interfaces), ODBC (Open Database Connectivity), JDBC (Java Database Connectivity) etc., and with its source code made available to users, RDM is effectively an open source product.
RDM has been well-proven on many projects – large and small – since its original launch 20 years ago and is popular with users across many sectors including defence, business, healthcare, education, research, cartography, etc.
RDM has a track record as one of the world’s leading database management systems. It is often used on big data projects, but is equally at home on a great range of applications.
