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Quarantine Fatigue: first-ever decrease in social distancing measures after the COVID-19 outbreak before reopening United States
TL;DR: The analysis showed that despite the existence of state-to-state variations, most states started experiencing a quarantine fatigue phenomenon during the same period, raising the concern of a second wave of outbreak.
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Abstract: By the emergence of the novel coronavirus disease (COVID-19) in Wuhan, China, and its rapid outbreak worldwide, the infectious illness has changed our everyday travel patterns. In this research, our team investigated the changes in the daily mobility pattern of people during the pandemic by utilizing an integrated data panel. To incorporate various aspects of human mobility, the team focused on the Social Distancing Index (SDI) which was calculated based on five basic mobility measures. The SDI patterns showed a plateau stage in the beginning of April that lasted for about two weeks. This phenomenon then followed by a universal decline of SDI, increased number of trips and reduction in percentage of people staying at home. We called the observation Quarantine Fatigue. The Rate of Change (ROC) method was employed to trace back the start date of quarantine fatigue which was indicated to be April 15th. Our analysis showed that despite the existence of state-to-state variations, most states started experiencing a quarantine fatigue phenomenon during the same period. This observation became more important by knowing that none of the states had officially announced the reopening until late April showing that people decided to loosen up their social distancing practices before the official reopening announcement. Moreover, our analysis indicated that official reopening led to a rapid decline in SDI, raising the concern of a second wave of outbreak. The synchronized trend among states also emphasizes the importance of a more nationwide decision-making attitude for the future as the condition of each state depends on the nationwide behavior.
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Citations
The impact of the COVID-19 pandemic on people's mobility: A longitudinal study of the U.S. from march to september of 2020
Junghwan Kim,Mei Po Kwan +1 more
TL;DR: In this paper, the authors examined changes in people's mobility over a 7-month period (from March 1st to September 30th, 2020) during the COVID-19 pandemic in the U S using longitudinal models and county-level mobility data obtained from people's anonymized mobile phone signals.
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Using Machine Learning to Develop a Novel COVID-19 Vulnerability Index (C19VI)
TL;DR: A Random Forest machine learning-based vulnerability model using CDC’s sociodemographic and COVID-19-specific themes and the census data is proposed for identifying and mapping vulnerable counties to help public health officials and disaster management agencies develop effective mitigation strategies especially for the disproportionately impacted communities.
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What Are People Concerned About During the Pandemic? Detecting Evolving Topics about COVID-19 from Twitter.
Chia-Hsuan Chang,Michal Monselise,Christopher C. Yang +2 more
- 17 Jan 2021
TL;DR: This research will propose two topic mining techniques that can handle a large-scale dataset—rolling online non-negative matrix factorization (Rolling-ONMF) and sliding online Non-negative Matrix Factorization (Sliding-OnMF)—and compare the insights produced by both techniques.
Spatiotemporal Patterns of Human Mobility and Its Association with Land Use Types during COVID-19 in New York City
TL;DR: In this paper, the authors used geotagged tweets data to reveal the spatio-temporal human mobility patterns during this COVID-19 pandemic in New York City, where human mobility pattern changes were detected by different categories of land use, including residential, parks, transportation facilities, and workplaces.
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Mobility in pandemic times: Exploring changes and long-term effects of COVID-19 on urban mobility behavior
TL;DR: In this paper , the authors investigated COVID-19-induced mobility-behavioral transformations by analyzing travel patterns of Berlin residents during a 20-month pandemic period and comparing them to the pre-pandemic situation.
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Deducing mode and purpose from GPS data
Peter R. Stopher,Eoin Clifford,Jun Zhang,Camden FitzGerald +3 more
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An interactive covid-19 mobility impact and social distancing analysis platform
Lei Zhang,Sepehr Ghader,Michael L Pack,Chenfeng Xiong,Aref Darzi,Mofeng Yang,Qianqian Sun,Aliakbar Kabiri,Songhua Hu +8 more
TL;DR: A summary of the platform is presented and the methodology used to process data and produce the platform metrics are described, to continuously inform decision-makers about the impacts of COVID-19 on their communities using an interactive analytical tool.
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Does Social Distancing Matter
Michael Greenstone,Vishan Nigam +1 more
TL;DR: In this article, the authors developed and implemented a method to monetize the impact of moderate social distancing on deaths from OVID-19 using the Ferguson et al. (2020) simulation model.
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