Deep Learning Approaches in Pandemic and Disaster Management

Marcello Trovati, Eleana Asimakopoulou, Nik Bessis

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review


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.
Original languageEnglish
Title of host publicationData Science Advancements in Pandemic and Outbreak Management
EditorsEleana Asimakopoulou, Nik Bessis
Publication statusPublished - 9 Apr 2021


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