Still fresh in the minds of many today, SARS was probably the largest global epidemic of the decade when it took place between November 2002 and July 2003. According to the statistics of the World Health Organisation, there were altogether 8,086 known infected cases and 774 deaths, during the near year-long battle against this fatal respiratory disease.

The bird flu, or avian influenza, strain H5N1, was even more destructive when approximately 60 per cent of humans known to have been infected with it died. In fact, warnings from the United Nations in 2005 further estimated that a global outbreak of avian influenza can easily claim up to 150 million lives, should it ever take place.

Most recently, the swine influenza virus (swine flu) epidemic spread to over 20 countries and caused a world-wide state of panic. To date, there were over 2,099 confirmed cases and 44 confirmed deaths. The World Health Organization warned that this could become a global pandemic.

So how do governments use technology to mitigate health risks? Before authorities can deal with any outbreak of swine flu, they need to know as much as possible about it. They need to know where it entered the country and when, how it is spreading and how quickly, and they need to know about it soon enough for the authorities to act. This means they need data as close as possible to real-time.
A real world example of how this can be achieved was recently published in The Australian entitled: Real-time data vital in swine flu fight.

Governments and companies alike face this ‘real-time’ challenge; how do I get complete, accurate and timely data from external sources to drive effective decisions. This data can come from many different sources? in many formats and is often of varying quality levels. Having received this data it needs to be transformed into a standard and consistent format and loaded into an environment from which meaningful information can be derived. It ought to be pretty simple, but as is often the case what should be simple often turns out to be far from the case.

So how do we overcome these challenges? First we want the ability to access, read, and interpret that data irrespective of the format that it is received in. Next we want to ensure that this data conforms to data metrics relevant to make it fit for purpose and workable. After that we need to get it into an environment from which to derive the maximum value. Lastly by not hardwiring the process we want the highest levels of flexibility with which to enhance or augment our source reporting data. This last point is really relevant if we are to meet unexpected and high priority demands.

As reported in the Sydney Morning Herald on the 8th of May 2009