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1.
Sustain Cities Soc ; 81: 103869, 2022 Jun.
Article in English | MEDLINE | ID: covidwho-1763976

ABSTRACT

The notion of social segregation refers to the degrees of separation between socially different population groups. Many studies have examined spatial and residential separations among different socioeconomic or racial populations. However, with the advancement of transportation and communication technologies, people's activities and social interactions are no longer limited to their residential areas. Therefore, there is a growing necessity to investigate social segregation from a mobility perspective by analyzing people's mobility patterns. Taking advantage of crowdsourced mobility data derived from 45 million mobile devices, we innovatively quantify social segregation for the twelve most populated U.S. metropolitan statistical areas (MSAs). We analyze the mobility patterns between different communities within each MSA to assess their separations for two years. Meanwhile, we particularly explore the dynamics of social segregation impacted by the COVID-19 pandemic. The results demonstrate that New York and Washington D.C. are the most and least segregated MSA respectively among the twelve MSAs. Since the COVID-19 began, six of the twelve MSAs experienced a statistically significant increase in segregation. This study also shows that, within each MSA, the most and least vulnerable groups of communities are prone to interacting with their similar communities, indicating a higher degree of social segregation.

2.
Cardiovasc Res ; 117(8): 1814-1822, 2021 07 07.
Article in English | MEDLINE | ID: covidwho-1301347

ABSTRACT

2020 has been an extraordinary year. The emergence of COVID-19 has driven urgent research in pulmonary and cardiovascular science and other fields. It has also shaped the way that we work with many experimental laboratories shutting down for several months, while bioinformatics approaches and other large data projects have gained prominence. Despite these setbacks, vascular biology research is stronger than ever. On behalf of the European Society of Cardiology Council for Basic Cardiovascular Science (ESC CBCS), here we review some of the vascular biology research highlights for 2020. This review is not exhaustive and there are many outstanding vascular biology publications that we were unable to cite due to page limits. Notwithstanding this, we have provided a snapshot of vascular biology research excellence in 2020 and identify topics that are in the ascendency and likely to gain prominence in coming years.


Subject(s)
COVID-19/diagnosis , Extracellular Traps/physiology , Neutrophils/cytology , Smartphone , Computational Biology , Humans , SARS-CoV-2/pathogenicity
3.
Proc Natl Acad Sci U S A ; 118(1)2021 01 07.
Article in English | MEDLINE | ID: covidwho-1066037

ABSTRACT

Nursing homes and other long-term care facilities account for a disproportionate share of COVID-19 cases and fatalities worldwide. Outbreaks in US nursing homes have persisted despite nationwide visitor restrictions beginning in mid-March. An early report issued by the Centers for Disease Control and Prevention identified staff members working in multiple nursing homes as a likely source of spread from the Life Care Center in Kirkland, WA, to other skilled nursing facilities. The full extent of staff connections between nursing homes-and the role these connections serve in spreading a highly contagious respiratory infection-is currently unknown given the lack of centralized data on cross-facility employment. We perform a large-scale analysis of nursing home connections via shared staff and contractors using device-level geolocation data from 50 million smartphones, and find that 5.1% of smartphone users who visited a nursing home for at least 1 h also visited another facility during our 11-wk study period-even after visitor restrictions were imposed. We construct network measures of connectedness and estimate that nursing homes, on average, share connections with 7.1 other facilities. Traditional federal regulatory metrics of nursing home quality are unimportant in predicting outbreaks, consistent with recent research. Controlling for demographic and other factors, a home's staff network connections and its centrality within the greater network strongly predict COVID-19 cases.


Subject(s)
COVID-19/epidemiology , Nursing Homes , Pandemics , SARS-CoV-2/pathogenicity , COVID-19/prevention & control , COVID-19/virology , Disease Outbreaks , Female , Humans , Male , Skilled Nursing Facilities , Smartphone , Social Network Analysis , Social Networking
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