Deep Learning for Virus-Spreading Forecasting: a Brief Survey

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 disea...

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Bibliographic Details
Published in:arXiv.org 2021-03
Main Authors: Baldo, Federico, Dall'Olio, Lorenzo, Ceccarelli, Mattia, Scheda, Riccardo, Lombardi, Michele, Borghesi, Andrea, Diciotti, Stefano, Milano, Michela
Format: Article
Language:English
Subjects:
Decision making
Deep learning
Forecasting
Mathematical models
Prediction models
Viruses
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Summary: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.
ISSN:2331-8422