Changes in mobility patterns in Europe during the COVID-19 pandemic:
Novel insights using open source data
- URL: http://arxiv.org/abs/2008.10505v1
- Date: Mon, 24 Aug 2020 15:15:53 GMT
- Title: Changes in mobility patterns in Europe during the COVID-19 pandemic:
Novel insights using open source data
- Authors: Anna Sigridur Islind, Mar\'ia \'Oskarsd\'ottir, Harpa
Steingr\'imsd\'ottir
- Abstract summary: The COVID-19 pandemic triggered a worldwide health crisis that has been tackled using a variety of strategies across Europe.
We show that mobility patterns have changed in different counties depending on the strategies they adopted during the pandemic.
Our data shows that the majority of European citizens walked less during the lock-downs, and that, even though flights were less frequent, driving increased drastically.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The COVID-19 pandemic has changed the way we act, interact and move around in
the world. The pandemic triggered a worldwide health crisis that has been
tackled using a variety of strategies across Europe. Whereas some countries
have taken strict measures, others have avoided lock-downs altogether. In this
paper, we report on findings obtained by combining data from different publicly
available sources in order to shed light on the changes in mobility patterns in
Europe during the pandemic. Using that data, we show that mobility patterns
have changed in different counties depending on the strategies they adopted
during the pandemic. Our data shows that the majority of European citizens
walked less during the lock-downs, and that, even though flights were less
frequent, driving increased drastically. In this paper, we focus on data for a
number of countries, for which we have also developed a dashboard that can be
used by other researchers for further analyses. Our work shows the importance
of granularity in open source data and how such data can be used to shed light
on the effects of the pandemic.
Related papers
- SPEED++: A Multilingual Event Extraction Framework for Epidemic Prediction and Preparedness [73.73883111570458]
We introduce the first multilingual Event Extraction framework for extracting epidemic event information for a wide range of diseases and languages.
Annotating data in every language is infeasible; thus we develop zero-shot cross-lingual cross-disease models.
Our framework can provide epidemic warnings for COVID-19 in its earliest stages in Dec 2019 from Chinese Weibo posts without any training in Chinese.
arXiv Detail & Related papers (2024-10-24T03:03:54Z) - Human Behavior in the Time of COVID-19: Learning from Big Data [71.26355067309193]
Since March 2020, there have been over 600 million confirmed cases of COVID-19 and more than six million deaths.
The pandemic has impacted and even changed human behavior in almost every aspect.
Researchers have been employing big data techniques such as natural language processing, computer vision, audio signal processing, frequent pattern mining, and machine learning.
arXiv Detail & Related papers (2023-03-23T17:19:26Z) - Changes in mobility choices during the first wave of the COVID-19
pandemic: a comparison between Italy and Sweden [0.0]
The spread of COVID-19 disease affected people's lives worldwide, particularly their travel behaviours and how they performed daily activities.
During the first wave of the pandemic, spring 2020, countries adopted different strategies to contain the spread of the virus.
The aim of this paper is to analyse the changes in mobility behaviours caused by the pandemic in two countries with different containment policies in place: Italy and Sweden.
arXiv Detail & Related papers (2023-03-14T11:19:26Z) - "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) - Misinformation, Believability, and Vaccine Acceptance Over 40 Countries:
Takeaways From the Initial Phase of The COVID-19 Infodemic [11.737540072863405]
This paper presents findings from a global survey on the extent of worldwide exposure to the COVID-19 infodemic.
We find a strong association between perceived believability of misinformation and vaccination hesitancy.
We discuss implications of our findings on public campaigns that proactively spread accurate information to countries that are more susceptible to the infodemic.
arXiv Detail & Related papers (2021-04-22T05:09:25Z) - 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) - Surveillance of COVID-19 Pandemic using Hidden Markov Model [0.0]
We look at applying Hidden Markov Model to get a better assessment of extent of spread.
The data we have chosen to analyze pertains to Indian scenario.
arXiv Detail & Related papers (2020-08-14T05:45:34Z) - Pandemic Pulse: Unraveling and Modeling Social Signals during the
COVID-19 Pandemic [12.050597862123313]
We present and begin to explore a collection of social data that represents part of the COVID-19 pandemic's effects on the United States.
This data is collected from a range of sources and includes longitudinal trends of news topics, social distancing behaviors, community mobility changes, web searches, and more.
arXiv Detail & Related papers (2020-06-10T17:55:44Z) - 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) - When and How to Lift the Lockdown? Global COVID-19 Scenario Analysis and
Policy Assessment using Compartmental Gaussian Processes [111.69190108272133]
coronavirus disease 2019 (COVID-19) global pandemic has led many countries to impose unprecedented lockdown measures.
Data-driven models that predict COVID-19 fatalities under different lockdown policy scenarios are essential.
This paper develops a Bayesian model for predicting the effects of COVID-19 lockdown policies in a global context.
arXiv Detail & Related papers (2020-05-13T18:21:50Z) - The Pace and Pulse of the Fight against Coronavirus across the US, A
Google Trends Approach [0.0]
Google Trends can be used as a proxy for what people are thinking, needing, and planning.
We use it to provide both insights into, and potential indicators of, important changes in information-seeking patterns during pandemics like COVID-19.
arXiv Detail & Related papers (2020-05-05T20:55:45Z)
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.