Predicting COVID-19 Spread from Large-Scale Mobility Data

• URL: http://arxiv.org/abs/2106.00356v1
• Date: Tue, 1 Jun 2021 10:05:02 GMT
• Title: Predicting COVID-19 Spread from Large-Scale Mobility Data
• Authors: Amray Schwabe, Joel Persson and Stefan Feuerriegel
• Abstract summary: A potential near real-time predictor of future case numbers is human mobility. We introduce a novel model for epidemic forecasting based on mobility data, called mobility marked Hawkes model. Our work is the first to predict the spread of COVID-19 from telecommunication data.
• Score: 22.55034017418318
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• A fairness assessment of mobility-based COVID-19 case prediction models [0.0]
  We tested the hypothesis that bias in the mobility data used to train the predictive models might lead to unfairly less accurate predictions for certain demographic groups. Specifically, the models tend to favor large, highly educated, wealthy young, urban, and non-black-dominated counties.
  arXiv  Detail & Related papers  (2022-10-08T03:43:51Z)
• A spatiotemporal machine learning approach to forecasting COVID-19 incidence at the county level in the United States [2.9822184411723645]
  We present COVID-LSTM, a data-driven model based on a Long Short-term memory architecture for forecasting COVID-19 incidence at the county-level in the US. We use the weekly number of new cases as temporal input, and hand-engineered spatial features from Facebook to capture the spread of the disease in time and space. Over the 4-week forecast horizon, our model is on average 50 cases per county more accurate than the COVIDhub-ensemble.
  arXiv  Detail & Related papers  (2021-09-24T17:40:08Z)
• Early Detection of COVID-19 Hotspots Using Spatio-Temporal Data [66.70036251870988]
  The Centers for Disease Control and Prevention (CDC) has worked with other federal agencies to identify counties with increasing coronavirus 2019 (CO-19) incidence (hotspots) This paper presents a sparse model for early detection of COVID-19 hotspots (at the county level) in the United States. Deep neural networks are introduced to enhance the model's representative power while still enjoying the interpretability of the kernel.
  arXiv  Detail & Related papers  (2021-05-31T19:28:17Z)
• Modeling the geospatial evolution of COVID-19 using spatio-temporal convolutional sequence-to-sequence neural networks [48.7576911714538]
  Portugal was the country in the world with the largest incidence rate, with 14-days incidence rates per 100,000 inhabitants in excess of 1000. Despite its importance, accurate prediction of the geospatial evolution of COVID-19 remains a challenge.
  arXiv  Detail & Related papers  (2021-05-06T15:24:00Z)
• Approximate Bayesian Computation for an Explicit-Duration Hidden Markov Model of COVID-19 Hospital Trajectories [55.786207368853084]
  We address the problem of modeling constrained hospital resources in the midst of the COVID-19 pandemic. For broad applicability, we focus on the common yet challenging scenario where patient-level data for a region of interest are not available. We propose an aggregate count explicit-duration hidden Markov model, nicknamed the ACED-HMM, with an interpretable, compact parameterization.
  arXiv  Detail & Related papers  (2021-04-28T15:32:42Z)
• STELAR: Spatio-temporal Tensor Factorization with Latent Epidemiological Regularization [76.57716281104938]
  We develop a tensor method to predict the evolution of epidemic trends for many regions simultaneously. STELAR enables long-term prediction by incorporating latent temporal regularization through a system of discrete-time difference equations. We conduct experiments using both county- and state-level COVID-19 data and show that our model can identify interesting latent patterns of the epidemic.
  arXiv  Detail & Related papers  (2020-12-08T21:21:47Z)
• Inter-Series Attention Model for COVID-19 Forecasting [23.58411106491221]
  We develop a new neural forecasting model, called Attention Crossing Time Series (textbfACTS), that makes forecasts via comparing patterns across time series obtained from multiple regions. Among 13 out of 18 testings including forecasting newly confirmed cases, hospitalizations and deaths, textbfACTS outperforms all the leading COVID-19 forecasters highlighted by CDC.
  arXiv  Detail & Related papers  (2020-10-25T00:11:49Z)
• Steering a Historical Disease Forecasting Model Under a Pandemic: Case of Flu and COVID-19 [75.99038202534628]
  We propose CALI-Net, a neural transfer learning architecture which allows us to'steer' a historical disease forecasting model to new scenarios where flu and COVID co-exist. Our experiments demonstrate that our approach is successful in adapting a historical forecasting model to the current pandemic.
  arXiv  Detail & Related papers  (2020-09-23T22:35:43Z)
• Transfer Graph Neural Networks for Pandemic Forecasting [32.0506180195988]
  We study the impact of population movement on the spread of COVID-19. We employ graph neural networks to predict the number of future cases.
  arXiv  Detail & Related papers  (2020-09-10T13:23:52Z)
• Cross-lingual Transfer Learning for COVID-19 Outbreak Alignment [90.12602012910465]
  We train on Italy's early COVID-19 outbreak through Twitter and transfer to several other countries. Our experiments show strong results with up to 0.85 Spearman correlation in cross-country predictions.
  arXiv  Detail & Related papers  (2020-06-05T02:04:25Z)
• Learning to Forecast and Forecasting to Learn from the COVID-19 Pandemic [10.796851110372593]
  We propose a heterogeneous infection rate model with human mobility for epidemic modeling. By linearizing the model and using weighted least squares, our model is able to quickly adapt to changing trends. We show that during the earlier part of the epidemic, using travel data increases the predictions.
  arXiv  Detail & Related papers  (2020-04-23T07:25:46Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.