A quick decision-making process in response and management of epidemics has been the most common approach, as accurate and relevant decisions have been demonstrated to have beneficial impacts on life preservation as well as on global and local economies. However, any disaster or epidemic is rarely represented by a set of single and linear parameters, as they often exhibit highly complex and chaotic behaviours, where interconnected unknowns rapidly evolve. As a consequence, any such decision-making approach must be computationally robust and able to process large amounts of data, whilst evaluating the potential outcomes based on specific decisions in real time.
|Title of host publication||Data Science Advancements in Pandemic and Outbreak Management|
|Editors||Eleana Asimakopoulou, Nik Bessis|
|Publication status||Published - 9 Apr 2021|