# Deep Learning for Virus-Spreading Forecasting: a Brief Survey

arXiv: https://arxiv.org/abs/2103.02346 · HTML: https://mlsanity.com/paper/2103.02346/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 34 references that are in the map.
- First posted on arXiv 3 Mar 2021.

## Builds on (most cited first, 3 of 3)

- [Temporal Convolutional Networks: A Unified Approach to Action Segmentation](https://mlsanity.com/paper/1608.08242/) (2016)
- [Mobile phone data and COVID-19: Missing an opportunity?](https://mlsanity.com/paper/2003.12347/) (2020)
- [COVID-19 Contact Tracing and Privacy: Studying Opinion and Preferences](https://mlsanity.com/paper/2005.06056/) (2020)

## Built on by (most cited first, 1 of 1)

- [Towards Digital Twin Oriented Modelling of Complex Networked Systems and Their Dynamics: A Comprehensive Survey](https://mlsanity.com/paper/2202.09363/) (2022)

## Abstract (arXiv)

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.