Observed mobility behavior data reveal social distancing inertia

• URL: http://arxiv.org/abs/2004.14748v1
• Date: Thu, 30 Apr 2020 13:12:37 GMT
• Title: Observed mobility behavior data reveal social distancing inertia
• Authors: Sepehr Ghader, Jun Zhao, Minha Lee, Weiyi Zhou, Guangchen Zhao, Lei Zhang
• Abstract summary: The study revealed that statistics related to social distancing, namely trip rate, miles traveled per person, and percentage of population staying at home have all showed an unexpected trend. The trends showed that as soon as COVID-19 cases were observed, the statistics started improving, regardless of government actions. The study suggests that there is a natural behavior inertia toward social distancing, which puts a limit on the extent of improvement in the social-distancing-related statistics.
• Score: 16.89576620375838
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The research team has utilized an integrated dataset, consisting of anonymized location data, COVID-19 case data, and census population information, to study the impact of COVID-19 on human mobility. The study revealed that statistics related to social distancing, namely trip rate, miles traveled per person, and percentage of population staying at home have all showed an unexpected trend, which we named social distancing inertia. The trends showed that as soon as COVID-19 cases were observed, the statistics started improving, regardless of government actions. This suggests that a portion of population who could and were willing to practice social distancing voluntarily and naturally reacted to the emergence of COVID-19 cases. However, after about two weeks, the statistics saturated and stopped improving, despite the continuous rise in COVID-19 cases. The study suggests that there is a natural behavior inertia toward social distancing, which puts a limit on the extent of improvement in the social-distancing-related statistics. The national data showed that the inertia phenomenon is universal, happening in all the U.S. states and for all the studied statistics. The U.S. states showed a synchronized trend, regardless of the timeline of their statewide COVID-19 case spreads or government orders.

Related papers

• Exploring the impact of social stress on the adaptive dynamics of COVID-19: Typing the behavior of na\"ive populations faced with epidemics [43.50312332512221]
  The COVID-19 pandemic has brought to light profound variations among different countries in terms of their adaptive dynamics. This emphasizes the crucial role of cultural characteristics in natural disaster analysis.
  arXiv  Detail & Related papers  (2023-11-23T11:05:54Z)
• Coronavirus statistics causes emotional bias: a social media text mining perspective [4.042350304426975]
  This paper proposes a deep learning model which classifies texts related to the pandemic from text data with place labels. Next, it conducts a sentiment analysis based on multi-task learning. Finally, it carries out a fixed-effect panel regression with outputs of the sentiment analysis.
  arXiv  Detail & Related papers  (2022-11-16T03:36:13Z)
• Causal Analysis and Prediction of Human Mobility in the U.S. during the COVID-19 Pandemic [0.0]
  Since the increasing outspread of COVID-19 in the U.S., most states have enforced travel restrictions resulting in sharp reductions in mobility. This study develops an analytical framework that determines and analyzes the most dominant factors impacting human mobility and travel in the U.S. during this pandemic.
  arXiv  Detail & Related papers  (2021-11-24T05:15:12Z)
• Twitter Subjective Well-Being Indicator During COVID-19 Pandemic: A Cross-Country Comparative Study [0.0]
  This study analyzes the impact of the COVID-19 pandemic on the subjective well-being as measured through Twitter data indicators for Japan and Italy. Overall, the subjective well-being dropped by 11.7% for Italy and 8.3% for Japan in the first nine months of 2020 compared to the last two months of 2019.
  arXiv  Detail & Related papers  (2021-01-19T15:51:53Z)
• COVID-19 Outbreak Prediction and Analysis using Self Reported Symptoms [12.864257751458712]
  We use self-reported symptoms survey data to understand trends in the spread of COVID-19. From our studies, we try to predict the likely % of the population that tested positive for COVID-19 based on self-reported symptoms. We forecast that % of the population having COVID-19-like illness (CLI) and those tested positive as 0.15% and 1.14% absolute error respectively.
  arXiv  Detail & Related papers  (2020-12-21T00:37:24Z)
• Controlling the Outbreak of COVID-19: A Noncooperative Game Perspective [61.558752620308134]
  Isolation and social distancing seem to be effective preventive measures to control this pandemic. We propose a noncooperative game that can provide an incentive for maintaining social distancing to prevent the spread of COVID-19. Numerical results show that the individual incentive increases more than 85% with an increasing percentage of home isolation.
  arXiv  Detail & Related papers  (2020-07-27T04:28:32Z)
• Understanding the temporal evolution of COVID-19 research through machine learning and natural language processing [66.63200823918429]
  The outbreak of the novel coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been continuously affecting human lives and communities around the world. We used multiple data sources, i.e., PubMed and ArXiv, and built several machine learning models to characterize the landscape of current COVID-19 research. Our findings confirm the types of research available in PubMed and ArXiv differ significantly, with the former exhibiting greater diversity in terms of COVID-19 related issues.
  arXiv  Detail & Related papers  (2020-07-22T18:02:39Z)
• 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)
• 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)
• Quantifying human mobility behavior changes in response to non-pharmaceutical interventions during the COVID-19 outbreak in the United States [5.904534884761267]
  Social distancing is one of the major community mitigation strategies, also known as non-pharmaceutical interventions. We construct a Social Distancing Index () to evaluate people's mobility pattern changes along with the spread of COVID-19. The major findings are: 1) the declaration of the national emergency greatly encouraged social distancing and the mandatory stay-at-home orders in most states further strengthened the practice; 2) the states with more confirmed cases have taken more active and timely responses in practicing social distancing; and 3) people in the states with fewer confirmed cases did not pay much attention to maintaining social distancing.
  arXiv  Detail & Related papers  (2020-05-04T00:59:28Z)
• The Ivory Tower Lost: How College Students Respond Differently than the General Public to the COVID-19 Pandemic [66.80677233314002]
  Pandemic of the novel Coronavirus Disease 2019 (COVID-19) has presented governments with ultimate challenges. In the United States, the country with the highest confirmed COVID-19 infection cases, a nationwide social distancing protocol has been implemented by the President. This paper aims to discover the social implications of this unprecedented disruption in our interactive society by mining people's opinions on social media.
  arXiv  Detail & Related papers  (2020-04-21T13:02:38Z)

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.