Your browser doesn't support javascript.
Show: 20 | 50 | 100
Results 1 - 8 de 8
Filter
1.
Cambridge Journal of Regions Economy and Society ; : 21, 2022.
Article in English | Web of Science | ID: covidwho-1868266

ABSTRACT

The expectation of a mass movement out of cities due to the rise of remote work associated with the Covid-19 pandemic, is counter to longstanding theories of the benefits of agglomeration economies. It suggests centrifugal shifts of economic activity which could boost neighbourhood economies at the expense of the downtown core. Using mobile phone data from SafeGraph, we track migration and daily mobility patterns throughout the New York metropolitan area between July 2019 and June 2021. We find that diverse suburban centres and exurban areas have bounced back more quickly than the dense specialised commercial districts in and around Manhattan.

2.
2021 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, WI-IAT 2021 ; : 320-326, 2021.
Article in English | Scopus | ID: covidwho-1832583

ABSTRACT

Various measures have been taken to prevent the spread of COVID-19. Even with widespread vaccination, the control of the epidemic is still difficult due to the mutation of the virus. When an epidemic breaks out, the simplest and most efficient method of control is still social isolation, which greatly affects daily lives and mobility patterns. To study mobility patterns, we leveraged mobile base station data in Shulan, China, during the epidemic. Our main discoveries are as follows: (1) With the development of COVID-19, travel volumes and the scopes of trips were gradually reduced. (2) In addition to the government's prevention policy, media coverage of COVID-19 had a huge impact on mobility patterns. (3) Previous studies focused on morning and evening rush hours. However, our results show that humans tend to intensively travel at noon. (4) The travel network was significantly more active in the early stages of the COVID-19 outbreak;hence, the possibility of disease transmission was greater. (5) With the development of the epidemic, travel intervals became increasingly longer, and the number of contacts between base stations decreased. (6) By analyzing the temporal path length, we found that some nodes were still active during the epidemic. © 2021 ACM.

3.
Sensors (Basel) ; 22(3)2022 Feb 01.
Article in English | MEDLINE | ID: covidwho-1667289

ABSTRACT

As an inevitable process, the number of older adults is increasing in many countries worldwide. Two of the main problems that society is being confronted with more and more, in this respect, are the inter-related aspects of feelings of loneliness and social isolation among older adults. In particular, the ongoing COVID-19 crisis and its associated restrictions have exacerbated the loneliness and social-isolation problems. This paper is first and foremost a comprehensive survey of loneliness monitoring and management solutions, from the multidisciplinary perspective of technology, gerontology, socio-psychology, and urban built environment. In addition, our paper also investigates machine learning-based technological solutions with wearable-sensor data, suitable to measure, monitor, manage, and/or diminish the levels of loneliness and social isolation, when one also considers the constraints and characteristics coming from social science, gerontology, and architecture/urban built environments points of view. Compared to the existing state of the art, our work is unique from the cross-disciplinary point of view, because our authors' team combines the expertise from four distinct domains, i.e., gerontology, social psychology, architecture, and wireless technology in addressing the two inter-related problems of loneliness and social isolation in older adults. This work combines a cross-disciplinary survey of the literature in the four aforementioned domains with a proposed wearable-based technological solution, introduced first as a generic framework and, then, exemplified through a simple proof of concept with dummy data. As the main findings, we provide a comprehensive view on challenges and solutions in utilizing various technologies, particularly those carried by users, also known as wearables, to measure, manage, and/or diminish the social isolation and the perceived loneliness among older adults. In addition, we also summarize the identified solutions which can be used for measuring and monitoring various loneliness- and social isolation-related metrics, and we present and validate, through a simple proof-of-concept mechanism, an approach based on machine learning for predicting and estimating loneliness levels. Open research issues in this field are also discussed.


Subject(s)
COVID-19 , Wearable Electronic Devices , Aged , Humans , Loneliness , SARS-CoV-2 , Social Isolation
4.
International Journal of Geographical Information Science ; : 32, 2021.
Article in English | Web of Science | ID: covidwho-1585416

ABSTRACT

The COVID-19 pandemic resulted in profound changes in mobility patterns and altered travel behaviors locally and globally. As a result, movement metrics have widely been used by researchers and policy makers as indicators to study, model, and mitigate the impacts of the COVID-19 pandemic. However, the veracity and variability of these mobility metrics have not been studied. This paper provides a systematic review of mobility and social distancing metrics available to researchers during the pandemic in 2020 in the United States. Twenty-six indices across nine different sources are analyzed and assessed with respect to their spatial and temporal coverage as well as sample representativeness at the county-level. Finally global and local indicators of spatial association are computed to explore spatial and temporal heterogeneity in mobility patterns. The structure of underlying changes in mobility and social distancing is examined in different US counties and across different data sets. We argue that a single measure might not describe all aspects of mobility perfectly.

5.
R Soc Open Sci ; 8(12): 210865, 2021 Dec.
Article in English | MEDLINE | ID: covidwho-1583912

ABSTRACT

During the COVID-19 pandemic, governments have attempted to control infections within their territories by implementing border controls and lockdowns. While large-scale quarantine has been the most successful short-term policy, the enormous costs exerted by lockdowns over long periods are unsustainable. As such, developing more flexible policies that limit transmission without requiring large-scale quarantine is an urgent priority. Here, the dynamics of dismantled community mobility structures within US society during the COVID-19 outbreak are analysed by applying the Louvain method with modularity optimization to weekly datasets of mobile device locations. Our networks are built based on individuals' movements from February to May 2020. In a multi-scale community detection process using the locations of confirmed cases, natural break points from mobility patterns as well as high risk areas for contagion are identified at three scales. Deviations from administrative boundaries were observed in detected communities, indicating that policies informed by assumptions of disease containment within administrative boundaries do not account for high risk patterns of movement across and through these boundaries. We have designed a multi-level quarantine process that takes these deviations into account based on the heterogeneity in mobility patterns. For communities with high numbers of confirmed cases, contact tracing and associated quarantine policies informed by underlying dismantled community mobility structures is of increasing importance.

6.
J Transp Geogr ; 90: 102906, 2021 Jan.
Article in English | MEDLINE | ID: covidwho-919596

ABSTRACT

Background: This paper looks into the impact of the recent COVID-19 epidemic on the daily mobility of people. Existing research into the epidemic travel patterns points at transport as a channel for disease spreading with especially long-distance travel in the centre of interest. We adopt a different approach looking into the effects that epidemic has on the transport system and specifically in relation to short-distance daily mobility activities. We go beyond simple travel avoidance behaviours and look into factors influencing change in travel times and in modal split under epidemic. This leads to the research problems we posit in this paper. We look into the overall reduction of daily travel and into the factors impacting peoples' decisions to refrain from daily traveling. This paper focuses on modes affected and explores differences between various societal groups. Methods: We use a CATI survey with a representative sample size of 1069 respondents from Poland. The survey was carried out between March, 24th and April, 6th2020, with a start date one week after the Polish government introduced administrative measures aimed at slowing down the COVID-19 epidemic. For data analysis, we propose using the GLM (general linear model), allowing us to include all the qualitative and quantitative variables which depict our sample. Results: We observe significant drops in travel times under epidemic conditions. Those drops are similar regardless of the age group and gender. The time decrease depended on the purpose of travels, means of transport, traveller's household size, fear of coronavirus, main occupation, and change in it caused by the epidemic. The more the respondent was afraid of coronavirus, the more she or he shortened the travel time.

7.
PeerJ ; 8: e9879, 2020.
Article in English | MEDLINE | ID: covidwho-800809

ABSTRACT

BACKGROUND: As governments across Europe have issued non-pharmaceutical interventions (NPIs) such as social distancing and school closing, the mobility patterns in these countries have changed. Most states have implemented similar NPIs at similar time points. However, it is likely different countries and populations respond differently to the NPIs and that these differences cause mobility patterns and thereby the epidemic development to change. METHODS: We build a Bayesian model that estimates the number of deaths on a given day dependent on changes in the basic reproductive number, R 0, due to differences in mobility patterns. We utilise mobility data from Google mobility reports using five different categories: retail and recreation, grocery and pharmacy, transit stations, workplace and residential. The importance of each mobility category for predicting changes in R 0 is estimated through the model. FINDINGS: The changes in mobility have a considerable overlap with the introduction of governmental NPIs, highlighting the importance of government action for population behavioural change. The shift in mobility in all categories shows high correlations with the death rates 1 month later. Reduction of movement within the grocery and pharmacy sector is estimated to account for most of the decrease in R 0. INTERPRETATION: Our model predicts 3-week epidemic forecasts, using real-time observations of changes in mobility patterns, which can provide governments with direct feedback on the effects of their NPIs. The model predicts the changes in a majority of the countries accurately but overestimates the impact of NPIs in Sweden and Denmark and underestimates them in France and Belgium. We also note that the exponential nature of all epidemiological models based on the basic reproductive number, R 0 cause small errors to have extensive effects on the predicted outcome.

8.
J Travel Med ; 28(2)2021 02 23.
Article in English | MEDLINE | ID: covidwho-745783

ABSTRACT

BACKGROUND: Low-wage dormitory-dwelling migrant workers in Singapore were disproportionately affected by coronavirus disease 2019 (COVID-19) infection. This was attributed to communal living in high-density and unhygienic dormitory settings and a lack of inclusive protection systems. However, little is known about the roles of social and geospatial networks in COVID-19 transmission. The study examined the networks of non-work-related activities among migrant workers to inform the development of lockdown exit strategies and future pandemic preparedness. METHODS: A population-based survey was conducted with 509 migrant workers across the nation, and it assessed dormitory attributes, social ties, physical and mental health status, COVID-19-related variables and mobility patterns using a grid-based network questionnaire. Mobility paths from dormitories were presented based on purposes of visit. Two-mode social networks examined the structures and positions of networks between workers and visit areas with individual attributes. RESULTS: COVID-19 risk exposure was associated with the density of dormitory, social ties and visit areas. The migrant worker hub in the city centre was the most frequently visited for essential services of grocery shopping and remittance, followed by south central areas mainly for social gathering. The hub was positioned as the core with the highest degree of centrality with a cluster of workers exposed to COVID-19. CONCLUSIONS: Social and geospatial networks of migrant workers should be considered in the implementation of lockdown exit strategies while addressing the improvement of living conditions and monitoring systems. Essential services, like remittance and grocery shopping at affordable prices, need to be provided near to dormitories to minimize excess gatherings.


Subject(s)
COVID-19/epidemiology , Health Equity/standards , Transients and Migrants/statistics & numerical data , Adult , Built Environment/standards , COVID-19/transmission , Female , Humans , Male , Pandemics , Population Density , Prevalence , Risk Assessment , SARS-CoV-2 , Singapore/epidemiology , Social Network Analysis , Spatial Analysis , Surveys and Questionnaires , Young Adult
SELECTION OF CITATIONS
SEARCH DETAIL