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Cited by 26COVID-19 epidemiological studiesData-Driven Disease SurveillanceHuman Mobility and Location-Based Analysis

Predicting COVID-19 Spread from Large-Scale Mobility Data

Schwabe, Amray, Persson, Joel, Feuerriegel, Stefan·2021·Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining
DOI 10.1145/3447548.3467157
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Abstract

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.

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  • The mobility marked Hawkes model outperforms other models in forecasting infections 5-21 days in the future in Switzerland.

    “The model outperforms the other models in forecasting the infections in the future for 5–21 days in Switzerland.”

    From Human-centred artificial intelligence for mobile health sensing: challenges and opportunities (2023) · cites this paper as [100]

  • Schwabe et al. proposed a mobility marked Hawkes model combining a Hawkes process to model transmission dynamics and a regularized Poisson regression to incorporate mobility covariates extracted from telecommunication data.

    “For example, Schwabe et al. [100] proposed mobility marked Hawkes model combining Hawkes process to model the transmission dynamics of the disease and a regularized Poisson regression to incorporate mobility covariates extracted from telecommunication data.”

    From Human-centred artificial intelligence for mobile health sensing: challenges and opportunities (2023) · cites this paper as [100]

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Authors
Schwabe, A., Persson, J., & Feuerriegel, S.
Affiliations
ETH Zurich (CH)
ORCID
Venue
Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining
Publisher
ACM
Published
2021-08-14
Pages
3531-3539
DOI
10.1145/3447548.3467157
OpenAlex
W3168624044
Cited by
26
References
40
Citations by year
2026: 4 · 2025: 3 · 2024: 5 · 2023: 4 · 2022: 10
FWCI
9.22
Type
conference paper
Language
en
Open access
closed
Licence
www.acm.org/publications/policies/copyright_policy#Background (vor)
Locations
ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) (publishedVersion)
Funders
Hasler Stiftung
Topics
  • Physical Sciences › Mathematics › Modeling and Simulation › COVID-19 epidemiological studies
  • Health Sciences › Medicine › Epidemiology › Data-Driven Disease Surveillance
  • Social Sciences › Transportation › Human Mobility and Location-Based Analysis
Keywords
COVID-19 pandemic, epidemic forecasting, human mobility, Hawkes process, telecommunications data, infectious disease transmission, early warning systems, point of interest data, Switzerland

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