# Evidence of economic segregation from mobility lockdown during COVID-19 epidemic

arXiv: https://arxiv.org/abs/2004.05455 · HTML: https://mlsanity.com/paper/2004.05455/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Apr 2020.

## Built on by (most cited first, 6 of 6)

- [Combining Graph Neural Networks and Spatio-temporal Disease Models to Predict COVID-19 Cases in Germany](https://mlsanity.com/paper/2101.00661/) (2021)
- [Modelling the COVID-19 virus evolution with Incremental Machine Learning](https://mlsanity.com/paper/2104.09325/) (2021)
- [Reinforced Contact Tracing and Epidemic Intervention](https://mlsanity.com/paper/2102.08251/) (2021)
- [Effectiveness of probabilistic contact tracing in epidemic containment: the role of super-spreaders and transmission path reconstruction](https://mlsanity.com/paper/2312.00910/) (2023)
- [PANDORA: Deep graph learning based COVID-19 infection risk level forecasting](https://mlsanity.com/paper/2406.06618/) (2024)
- [Detecting Transportation Mode Using Dense Smartphone GPS Trajectories and Transformer Models](https://mlsanity.com/paper/2603.00340/) (2026)

## Abstract (arXiv)

In response to the COVID-19 pandemic, National governments have applied lockdown restrictions to reduce the infection rate. We perform a massive analysis on near real-time Italian data provided by Facebook to investigate how lockdown strategies affect economic conditions of individuals and local governments. We model the change in mobility as an exogenous shock similar to a natural disaster. We identify two ways through which mobility restrictions affect Italian citizens. First, we find that the impact of lockdown is stronger in municipalities with higher fiscal capacity. Second, we find a segregation effect, since mobility restrictions are stronger in municipalities for which inequality is higher and where individuals have lower income per capita.