# Mobility Based SIR Model For Pandemics -- With Case Study Of COVID-19

arXiv: https://arxiv.org/abs/2004.13015 · HTML: https://mlsanity.com/paper/2004.13015/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Apr 2020.

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

- [Deep diffusion-based forecasting of COVID-19 by incorporating network-level mobility information](https://mlsanity.com/paper/2111.05199/) (2021)
- [The Limits to Learning a Diffusion Model](https://mlsanity.com/paper/2006.06373/) (2020)
- [Predicting Socio-Economic Well-being Using Mobile Apps Data: A Case Study of France](https://mlsanity.com/paper/2301.09986/) (2023)
- [Human Behavior in the Time of COVID-19: Learning from Big Data](https://mlsanity.com/paper/2303.13452/) (2023)
- [Predicting sub-population specific viral evolution](https://mlsanity.com/paper/2410.21518/) (2024)
- [Spatio-Temporal SIR Model of Pandemic Spread During Warfare with Optimal Dual-use Healthcare System Administration using Deep Reinforcement Learning](https://mlsanity.com/paper/2412.14039/) (2024)

## Abstract (arXiv)

In the last decade, humanity has faced many different pandemics such as SARS, H1N1, and presently novel coronavirus (COVID-19). On one side, scientists are focusing on vaccinations, and on the other side, there is a need to propose models that can help us in understanding the spread of these pandemics as it can help governmental and other concerned agencies to be well prepared, especially from pandemics, which spreads faster like COVID-19. The main reason for some epidemic turning into pandemics is the connectivity among different regions of the world, which makes it easier to affect a wider geographical area, often worldwide. In addition, the population distribution and social coherence in the different regions of the world is non-uniform. Thus, once the epidemic enters a region, then the local population distribution plays an important role. Inspired by these ideas, we proposed a mobility-based SIR model for epidemics, which especially takes into account pandemic situations. To the best of our knowledge, this model is first of its kind, which takes into account the population distribution and connectivity of different geographic locations across the globe. In addition to presenting the mathematical proof of our model, we have performed extensive simulations using synthetic data to demonstrate our model's generalizability. To demonstrate the wider scope of our model, we used our model to forecast the COVID-19 cases for Estonia.