Deep Learning for Virus-Spreading Forecasting: a Brief Survey

Federico Baldo; Lorenzo Dall'Olio; Mattia Ceccarelli; Riccardo Scheda; Michele Lombardi; Andrea Borghesi; Stefano Diciotti; Michela Milano

arXiv · 2103.02346

Deep Learning for Virus-Spreading Forecasting: a Brief Survey

Federico Baldo·Lorenzo Dall'Olio·Mattia Ceccarelli·Riccardo Scheda·Michele Lombardi·Andrea Borghesi·Stefano Diciotti·Michela Milano

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Abstract

The advent of the coronavirus pandemic has sparked the interest in predictive models capable of forecasting virus-spreading, especially for boosting and supporting decision-making processes. In this paper, we will outline the main Deep Learning approaches aimed at predicting the spreading of a disease in space and time. The aim is to show the emerging trends in this area of research and provide a general perspective on the possible strategies to approach this problem. In doing so, we will mainly focus on two macro-categories: classical Deep Learning approaches and Hybrid models. Finally, we will discuss the main advantages and disadvantages of different models, and underline the most promising development directions to improve these approaches.

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Federico Baldo, Lorenzo Dall'Olio, Mattia Ceccarelli, Riccardo Scheda, Michele Lombardi, Andrea Borghesi, Stefano Diciotti, Michela Milano. 2021-03-03. Deep Learning for Virus-Spreading Forecasting: a Brief Survey. https://arxiv.org/abs/2103.02346

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