# Predicting COVID-19 Spread from Large-Scale Mobility Data

arXiv: https://arxiv.org/abs/2106.00356 · HTML: https://mlsanity.com/paper/2106.00356/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Jun 2021.

## Built on by (most cited first, 7 of 7)

- [Human Mobility Modeling During the COVID-19 Pandemic via Deep Graph Diffusion Infomax](https://mlsanity.com/paper/2212.05707/) (2022)
- [Multiwave COVID-19 Prediction from Social Awareness using Web Search and Mobility Data](https://mlsanity.com/paper/2110.11584/) (2021)
- [Advances in Temporal Point Processes: Bayesian, Neural, and LLM Approaches](https://mlsanity.com/paper/2501.14291/) (2025)
- [Web Mining to Inform Locations of Charging Stations for Electric Vehicles](https://mlsanity.com/paper/2203.07081/) (2022)
- [A Mobility-Aware Deep Learning Model for Long-Term COVID-19 Pandemic Prediction and Policy Impact Analysis](https://mlsanity.com/paper/2212.02575/) (2022)
- [A comparison of short-term probabilistic forecasts for the incidence of COVID-19 using mechanistic and statistical time series models](https://mlsanity.com/paper/2305.00933/) (2023)
- [GenHAR: Generalizing Cross-domain Human Activity Recognition for Last-mile Delivery](https://mlsanity.com/paper/2605.22086/) (2026)

## Abstract (arXiv)

To manage the COVID-19 epidemic effectively, decision-makers in public health need accurate forecasts of case numbers. A potential near real-time predictor of future case numbers is human mobility; however, research on the predictive power of mobility is lacking. To fill this gap, we introduce a novel model for epidemic forecasting based on mobility data, called mobility marked Hawkes model. The proposed model consists of three components: (1) A Hawkes process captures the transmission dynamics of infectious diseases. (2) A mark modulates the rate of infections, thus accounting for how the reproduction number R varies across space and time. The mark is modeled using a regularized Poisson regression based on mobility covariates. (3) A correction procedure incorporates new cases seeded by people traveling between regions. Our model was evaluated on the COVID-19 epidemic in Switzerland. Specifically, we used mobility data from February through April 2020, amounting to approximately 1.5 billion trips. Trip counts were derived from large-scale telecommunication data, i.e., cell phone pings from the Swisscom network, the largest telecommunication provider in Switzerland. We compared our model against various state-of-the-art baselines in terms of out-of-sample root mean squared error. We found that our model outperformed the baselines by 15.52%. The improvement was consistently achieved across different forecast horizons between 5 and 21 days. In addition, we assessed the predictive power of conventional point of interest data, confirming that telecommunication data is superior. To the best of our knowledge, our work is the first to predict the spread of COVID-19 from telecommunication data. Altogether, our work contributes to previous research by developing a scalable early warning system for decision-makers in public health tasked with controlling the spread of infectious diseases.