# A SIDARTHE Model of COVID-19 Epidemic in Italy

arXiv: https://arxiv.org/abs/2003.09861 · HTML: https://mlsanity.com/paper/2003.09861/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Mar 2020.

## Built on by (most cited first, 20 of 20)

- [A Probabilistic State Space Model for Joint Inference from Differential Equations and Data](https://mlsanity.com/paper/2103.10153/) (2021)
- [Spectral operator learning for parametric PDEs without data reliance](https://mlsanity.com/paper/2310.02013/) (2023)
- [Asymptotic-Preserving Neural Networks for multiscale hyperbolic models of epidemic spread](https://mlsanity.com/paper/2206.12625/) (2022)
- [COVID-19 Modeling: A Review](https://mlsanity.com/paper/2104.12556/) (2021)
- [Advancing Real-time Pandemic Forecasting Using Large Language Models: A COVID-19 Case Study](https://mlsanity.com/paper/2404.06962/) (2024)
- [On the Universal Transformation of Data-Driven Models to Control Systems](https://mlsanity.com/paper/2102.04722/) (2021)
- [Uncertainty Quantification and Propagation in Surrogate-based Bayesian Inference](https://mlsanity.com/paper/2312.05153/) (2023)
- [Enabling AI Scientists to Recognize Innovation: A Domain-Agnostic Algorithm for Assessing Novelty](https://mlsanity.com/paper/2503.01508/) (2025)
- [Decision-Dependent Distributionally Robust Markov Decision Process Method in Dynamic Epidemic Control](https://mlsanity.com/paper/2306.14051/) (2023)
- [The Limits to Learning a Diffusion Model](https://mlsanity.com/paper/2006.06373/) (2020)
- [Robust Pandemic Control Synthesis with Formal Specifications: A Case Study on COVID-19 Pandemic](https://mlsanity.com/paper/2103.14262/) (2021)
- [Learning epidemic trajectories through Kernel Operator Learning: from modelling to optimal control](https://mlsanity.com/paper/2404.11130/) (2024)
- [Impact of Interventional Policies Including Vaccine on Covid-19 Propagation and Socio-Economic Factors](https://mlsanity.com/paper/2101.03944/) (2021)
- [Spatio-Temporal Neural Network for Fitting and Forecasting COVID-19](https://mlsanity.com/paper/2103.11860/) (2021)
- [Modeling time evolving COVID-19 uncertainties with density dependent asymptomatic infections and social reinforcement](https://mlsanity.com/paper/2108.10029/) (2021)
- [Compartmental Models for COVID-19 and Control via Policy Interventions](https://mlsanity.com/paper/2203.02860/) (2022)
- [Epidemic Decision-making System Based Federated Reinforcement Learning](https://mlsanity.com/paper/2311.01749/) (2023)
- [Modeling Epidemic Spread: A Gaussian Process Regression Approach](https://mlsanity.com/paper/2312.09384/) (2023)
- [Bayesian Surrogate Training on Multiple Data Sources: A Hybrid Modeling Strategy](https://mlsanity.com/paper/2412.11875/) (2024)
- [MiCA: A Mobility-Informed Causal Adapter for Lightweight Epidemic Forecasting](https://mlsanity.com/paper/2601.11089/) (2026)

## Abstract (arXiv)

In late December 2019, a novel strand of Coronavirus (SARS-CoV-2) causing a severe, potentially fatal respiratory syndrome (COVID-19) was identified in Wuhan, Hubei Province, China and is causing outbreaks in multiple world countries, soon becoming a pandemic. Italy has now become the most hit country outside of Asia: on March 16, 2020, the Italian Civil Protection documented a total of 27980 confirmed cases and 2158 deaths of people tested positive for SARS-CoV-2. In the context of an emerging infectious disease outbreak, it is of paramount importance to predict the trend of the epidemic in order to plan an effective control strategy and to determine its impact. This paper proposes a new epidemic model that discriminates between infected individuals depending on whether they have been diagnosed and on the severity of their symptoms. The distinction between diagnosed and non-diagnosed is important because non-diagnosed individuals are more likely to spread the infection than diagnosed ones, since the latter are typically isolated, and can explain misperceptions of the case fatality rate and of the seriousness of the epidemic phenomenon. Being able to predict the amount of patients that will develop life-threatening symptoms is important since the disease frequently requires hospitalisation (and even Intensive Care Unit admission) and challenges the healthcare system capacity. We show how the basic reproduction number can be redefined in the new framework, thus capturing the potential for epidemic containment. Simulation results are compared with real data on the COVID-19 epidemic in Italy, to show the validity of the model and compare different possible predicted scenarios depending on the adopted countermeasures.