Complex Systems: Computer Modelling of Epidemics
Complex systems – including such things as power and data grids, communication and transport systems, social networks, ecosystems and the spread of disease – evolve and ‘self-organise’ over time, resulting in both benefits and challenges.
Influenza pandemics, for example, emerge at unpredictable intervals. Several major infections have occurred during the last 100 years, including the 1918 influenza pandemic (“Spanish Flu”) that infected an estimated 500 million people — one-third of the world’s population! — and caused an estimated 50 million deaths.
An influenza pandemic today, of the magnitude of the 1918 Spanish Flu, would cause 33 million deaths globally within six months.
Professor Mikhail Prokopenko reveals how the development of very realistic computer models of our world helps us better understand and better deal with complex problems like flu epidemics. Recent research has indicated that the more urbanised society is, the more vulnerable it is to the spread of disease due to increased population in major cities and international air traffic. This, in turn, helps us identify the best ways to intervene and curtail pandemics through the management of our cities.
Professor Mikhail Prokopenko
Professor Prokopenko has a strong international reputation in complex self-organising systems, with more than 180 publications, patents and edited books. Since 2014, he has been the Director of the Complex Systems Research Group (Faculty of Engineering and IT) at the University of Sydney. He also leads the post-graduate program on Complex Systems, including Master of Complex Systems.