Evidence map›Paper›PMID 36612713›Full record

ArticleInternational journal of environmental research and public health2022

Examining the Human Activity-Intensity Change at Different Stages of the COVID-19 Pandemic across Chinese Working, Residential and Entertainment Areas.

Shuang Ma, Kang Cao, Shuangjin Li, Yaozhi Luo, Ke Wang, Wei Liu, Guohui Sun

Open access · goldAbstract read
In one paragraph

Article in International journal of environmental research and public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
0.1field-weighted citation impact, top 56% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed, 1 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors at 4 institutions in 2 countries.

Shuang MaCollege of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, China.
Kang CaoCollege of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, China.
Shuangjin LiGraduate School of Advanced Science and Engineering, Hiroshima University, Higashihiroshima 739-8529, Japan.
Yaozhi LuoCollege of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, China.
Ke WangCollege of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, China.
Wei LiuInstitute for Health and Environment, Chongqing University of Science and Technology, Chongqing 401331, China.
Guohui SunBeijing Key Laboratory of Environment and Viral Oncology, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, China.ORCID 0000-0003-2259-5431
Zhejiang University · CNBeijing University of Technology · CNChongqing University of Science and Technology · CNHiroshima University · JP

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The COVID-19 pandemic has already resulted in more than 6 million deaths worldwide as of December 2022. The COVID-19 has also been greatly affecting the activity of the human population in China and the world. It remains unclear how the human activity-intensity changes have been affected by the COVID-19 spread in China at its different stages along with the lockdown and relaxation policies. We used four days of Location-based services data from Tencent across China to capture the real-time changes in human activity intensity in three stages of COVID-19-namely, during the lockdown, at the first stage of work resuming and at the stage of total work resuming-and observed the changes in different land use categories. We applied the mean decrease Gini (MDG) approach in random forest to examine how these changes are influenced by land attributes, relying on the CART algorithm in Python. This approach was also compared with Geographically Weighted Regression (GWR). Our analysis revealed that the human activity intensity decreased by 22-35%, 9-16% and 6-15%, respectively, in relation to the normal conditions before the spread of COVID-19 during the three periods. The human activity intensity associated with commercial sites, sports facilities/gyms and tourism experienced the relatively largest contraction during the lockdown. During the relaxations of restrictions, government institutions showed a 13.89% rise in intensity at the first stage of work resuming, which was the highest rate among all the working sectors. Furthermore, the GDP and road junction density were more influenced by the change in human activity intensity for all land use categories. The bus stop density was importantly associated with mixed-use land recovery during the relaxing stages, while the coefficient of density of population in entertainment land were relatively higher at these two stages. This study aims to provide additional support to investigate the human activity changes due to the spread of COVID-19 at different stages across different sectors.

Indexed as

COVID-19Communicable Disease ControlEast Asian PeopleHuman ActivitiesHumansPandemicsSARS-CoV-2COVID-19location-based services (LBS) datamulti stagesnationwide

Identifiers

PMID36612713
PMCPMC9820041
OpenAlexW4313327512

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.