Mobility Changes in Response to COVID-19

• URL: http://arxiv.org/abs/2003.14228v1
• Date: Tue, 31 Mar 2020 14:03:30 GMT
• Title: Mobility Changes in Response to COVID-19
• Authors: Michael S. Warren and Samuel W. Skillman
• Abstract summary: We use anonymized and/or de-identified mobile device locations to measure mobility. Results indicate that a large reduction in mobility has taken place, both in the US and globally.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In response to the COVID-19 pandemic, both voluntary changes in behavior and administrative restrictions on human interactions have occurred. These actions are intended to reduce the transmission rate of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). We use anonymized and/or de-identified mobile device locations to measure mobility, a statistic representing the distance a typical member of a given population moves in a day. Results indicate that a large reduction in mobility has taken place, both in the US and globally. In the United States, large mobility reductions have been detected associated with the onset of the COVID-19 threat and specific government directives. Mobility data at the US admin1 (state) and admin2 (county) level have been made freely available under a Creative Commons Attribution (CC BY 4.0) license via the GitHub repository https://github.com/descarteslabs/DL-COVID-19/

Related papers

• "COVID-19 was a FIFA conspiracy #curropt": An Investigation into the Viral Spread of COVID-19 Misinformation [60.268682953952506]
  We estimate the extent to which misinformation has influenced the course of the COVID-19 pandemic using natural language processing models. We provide a strategy to combat social media posts that are likely to cause widespread harm.
  arXiv  Detail & Related papers  (2022-06-12T19:41:01Z)
• Exploring the spatiotemporal heterogeneity in the relationship between human mobility and COVID-19 prevalence using dynamic time warping [0.0]
  Previous studies have revealed the correlation between human mobility and COVID-19 cases. This study aims to identify heterogeneities in the relationship between human mobility and COVID-19 cases in U.S. counties.
  arXiv  Detail & Related papers  (2021-09-28T14:38:29Z)
• 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)
• 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)
• Transportation Density Reduction Caused by City Lockdowns Across the World during the COVID-19 Epidemic: From the View of High-resolution Remote Sensing Imagery [48.52477000522933]
  The COVID-19 epidemic began to worsen in the first months of 2020. stringent lockdown policies were implemented in numerous cities throughout the world to control human transmission and mitigate its spread. We provide a quantitative investigation of the transportation density reduction before and after lockdown was implemented in six epicenter cities.
  arXiv  Detail & Related papers  (2021-03-02T13:45:16Z)
• 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)
• Digital Ariadne: Citizen Empowerment for Epidemic Control [55.41644538483948]
  The COVID-19 crisis represents the most dangerous threat to public health since the H1N1 pandemic of 1918. Technology-assisted location and contact tracing, if broadly adopted, may help limit the spread of infectious diseases. We present a tool, called 'diAry' or 'digital Ariadne', based on voluntary location and Bluetooth tracking on personal devices.
  arXiv  Detail & Related papers  (2020-04-16T15:53:42Z)

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.