Evidence map›Paper›PMID 35512611›Full record

ArticleSocial science & medicine (1982)2022

Understanding the spatial diffusion dynamics of the COVID-19 pandemic in the city system in China.

Lijuan Gu, Linsheng Yang, Li Wang, Yanan Guo, Binggan Wei, Hairong Li

Open access · greenAbstract read
In one paragraph

Article in Social science & medicine (1982), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
1.2field-weighted citation impact, top 24% 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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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

4 citing papers in PubMed, 9 citations in OpenAlex.

  1. Article
  2. Article
  3. COVID-19 modeling based on real geographic and population data.Turkish journal of medical sciences · 2023
    Article
  4. Review
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

6 authors at 2 institutions in 1 country.

Lijuan GuKey Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China. Electronic address: gulj@igsnrr.ac.cn.
Linsheng YangKey Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China; College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, China. Electronic address: yangls@igsnrr.ac.cn.
Li WangKey Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China. Electronic address: wangl@igsnrr.ac.cn.
Yanan GuoKey Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China. Electronic address: guoyn.14s@igsnrr.ac.cn.
Binggan WeiKey Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China. Electronic address: Weibg@igsnrr.ac.cn.
Hairong LiKey Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China. Electronic address: lihr@igsnrr.ac.cn.
Chinese Academy of Sciences · CNInstitute of Geographic Sciences and Natural Resources Research · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Investigating the spatial epidemic dynamics of COVID-19 is crucial in understanding the routine of spatial diffusion and in surveillance, prediction, identification and prevention of another potential outbreak. However, previous studies attempting to evaluate these spatial diffusion dynamics are limited. Using city as the research unit and spatial association analysis as the primary strategy, this study explored the changing primary risk factors impacting the spatial spread of COVID-19 across Chinese cities under various diffusion assumptions and throughout the epidemic stage. Moreover, this study investigated the characteristics and geographical distributions of high-risk areas in different epidemic stages. The results empirically indicated rapid intercity diffusion at the early stage and primarily intracity diffusion thereafter. Before countermeasures took effect, proximity, GDP per capita, medical resources, outflows from Wuhan and intercity mobility significantly affected early diffusion. With speedily effective countermeasures, outflows from the epicenter, proximity, and intracity outflows played an important role. At the early stage, high-risk areas were mainly cities adjacent to the epicenter, with higher GDP per capita, or a combination of higher GDP per capita and better medical resources, with more outflow from the epicenter, or more intercity mobility. After countermeasures were effected, cities adjacent to the epicenter, or with more outflow from the epicenter or more intracity mobility became high-risk areas. This study provides an insightful understanding of the spatial diffusion of COVID-19 across cities. The findings are informative for effectively handling the potential recurrence of COVID-19 in various settings.

Indexed as

COVID-19ChinaCitiesDisease OutbreaksHumansPandemicsChinaCityCOVID-19DiffusionSpatial association

Identifiers

PMID35512611
PMCPMC9046135
OpenAlexW4224999607

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.