Country-wide mobility changes observed using mobile phone data during COVID-19 pandemic

• URL: http://arxiv.org/abs/2008.10064v1
• Date: Sun, 23 Aug 2020 16:00:57 GMT
• Title: Country-wide mobility changes observed using mobile phone data during COVID-19 pandemic
• Authors: Georg Heiler, Tobias Reisch, Jan Hurt, Mohammad Forghani, Aida Omani, Allan Hanbury, Farid Karimipour
• Abstract summary: In March 2020, the Austrian government introduced a widespread lock-down in response to the COVID-19 pandemic. Here we assess the effect of the lock-down quantitatively for all regions in Austria using near-real-time anonymized mobile phone data.
• Score: 5.402663611963239
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In March 2020, the Austrian government introduced a widespread lock-down in response to the COVID-19 pandemic. Based on subjective impressions and anecdotal evidence, Austrian public and private life came to a sudden halt. Here we assess the effect of the lock-down quantitatively for all regions in Austria and present an analysis of daily changes of human mobility throughout Austria using near-real-time anonymized mobile phone data. We describe an efficient data aggregation pipeline and analyze the mobility by quantifying mobile-phone traffic at specific point of interest (POI), analyzing individual trajectories and investigating the cluster structure of the origin-destination graph. We found a reduction of commuters at Viennese metro stations of over 80\% and the number of devices with a radius of gyration of less than 500 m almost doubled. The results of studying crowd-movement behavior highlight considerable changes in the structure of mobility networks, revealed by a higher modularity and an increase from 12 to 20 detected communities. We demonstrate the relevance of mobility data for epidemiological studies by showing a significant correlation of the outflow from the town of Ischgl (an early COVID-19 hotspot) and the reported COVID-19 cases with an 8-day time lag. This research indicates that mobile phone usage data permits the moment-by-moment quantification of mobility behavior for a whole country. We emphasize the need to improve the availability of such data in anonymized form to empower rapid response to combat COVID-19 and future pandemics.

Related papers

• Rethinking Urban Mobility Prediction: A Super-Multivariate Time Series Forecasting Approach [71.67506068703314]
  Long-term urban mobility predictions play a crucial role in the effective management of urban facilities and services. Traditionally, urban mobility data has been structured as videos, treating longitude and latitude as fundamental pixels. In our research, we introduce a fresh perspective on urban mobility prediction. Instead of oversimplifying urban mobility data as traditional video data, we regard it as a complex time series.
  arXiv  Detail & Related papers  (2023-12-04T07:39:05Z)
• Influence of Mobility Restrictions on Transmission of COVID-19 in the state of Maryland -- the USA [0.0]
  The novel coronavirus, COVID-19, was first detected in the United States in January 2020. To curb the spread of the disease in mid-March, different states issued mandatory stay-at-home (SAH) orders. We studied the impact of restrictions on mobility on reducing COVID-19 transmission.
  arXiv  Detail & Related papers  (2021-09-24T22:15:40Z)
• Predicting COVID-19 Spread from Large-Scale Mobility Data [22.55034017418318]
  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.
  arXiv  Detail & Related papers  (2021-06-01T10:05:02Z)
• 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)
• C-Watcher: A Framework for Early Detection of High-Risk Neighborhoods Ahead of COVID-19 Outbreak [54.39837683016444]
  C-Watcher aims at screening every neighborhood in a target city and predicting infection risks, prior to the spread of COVID-19 from epicenters to the city. C-Watcher collects large-scale long-term human mobility data from Baidu Maps, then characterizes every residential neighborhood in the city using a set of features based on urban mobility patterns. We carried out extensive experiments on C-Watcher using the real-data records in the early stage of COVID-19 outbreaks.
  arXiv  Detail & Related papers  (2020-12-22T17:02:54Z)
• Twitter, human mobility, and COVID-19 [11.143921916292726]
  We analyzed 587 million tweets worldwide to see how global collaborative efforts in reducing human mobility are reflected. To quantify the responsiveness in certain geographical regions, we propose a mobility-based responsive index (MRI) The results suggest that mobility patterns obtained from Twitter data are amendable to quantitatively reflect the mobility dynamics.
  arXiv  Detail & Related papers  (2020-06-24T23:21:03Z)
• Effectiveness and Compliance to Social Distancing During COVID-19 [72.94965109944707]
  We use a detailed set of mobility data to evaluate the impact that stay-at-home orders had on the spread of COVID-19 in the US. We show that there is a unidirectional Granger causality, from the median percentage of time spent daily at home to the daily number of COVID-19-related deaths with a lag of 2 weeks.
  arXiv  Detail & Related papers  (2020-06-23T03:36:19Z)
• COVID-19 Mobility Data Collection of Seoul, South Korea [0.46180371154032895]
  This paper presents two categories of mobility datasets that concern nearly 10 million citizens' movements during COVID-19 in the capital city of South Korea, Seoul. We curate hourly data of subway ridership, traffic volume and population present count at selected points of interests.
  arXiv  Detail & Related papers  (2020-06-11T07:04:31Z)
• 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)
• A Cross-Domain Approach to Analyzing the Short-Run Impact of COVID-19 on the U.S. Electricity Sector [1.2972684859455053]
  coronavirus disease (COVID-19) has rapidly spread around the globe in 2020, with the U.S. becoming the epicenter. We release a first-of-its-kind cross-domain open-access data hub, integrating data from across all existing U.S. wholesale electricity markets with COVID-19 case, weather, cellular location, and satellite imaging data. We uncover a significant reduction in electricity consumption across that is strongly correlated with the rise in the number of COVID-19 cases, degree of social distancing, and level of commercial activity.
  arXiv  Detail & Related papers  (2020-05-11T22:16:13Z)

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.