Evidence mapPaperPMID 39600802Full record

ArticlePNAS nexus2024

Strong long ties facilitate epidemic containment on mobility networks.

Jianhong Mou, Suoyi Tan, Juanjuan Zhang, Bin Sai, Mengning Wang, Bitao Dai, Bo-Wen Ming, Shan Liu, Zhen Jin, Guiquan Sun and 2 more

Abstract read
In one paragraph

Article in PNAS nexus, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
field-weighted citation impact
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

1 citing paper in PubMed.

  1. Article
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

12 authors.

Jianhong MouCollege of Systems Engineering, National University of Defense Technology, Changsha 410073, China.ORCID https://orcid.org/0009-0001-6654-7741
Suoyi TanCollege of Systems Engineering, National University of Defense Technology, Changsha 410073, China.ORCID https://orcid.org/0000-0002-9108-2229
Juanjuan ZhangDepartment of Epidemiology, School of Public Health, Key Laboratory of Public Health Safety, Ministry of Education, Fudan University, Shanghai 200032, China.ORCID https://orcid.org/0000-0002-8600-754X
Bin SaiCollege of Systems Engineering, National University of Defense Technology, Changsha 410073, China.ORCID https://orcid.org/0000-0002-2609-2236
Mengning WangCollege of Systems Engineering, National University of Defense Technology, Changsha 410073, China.
Bitao DaiCollege of Systems Engineering, National University of Defense Technology, Changsha 410073, China.ORCID https://orcid.org/0000-0002-5346-6352
Bo-Wen MingDepartment of Epidemiology, School of Public Health, Key Laboratory of Public Health Safety, Ministry of Education, Fudan University, Shanghai 200032, China.ORCID https://orcid.org/0000-0001-9564-1115
Shan LiuSchool of Management, Xi'an Jiaotong University, Xi'an 710049, China.ORCID https://orcid.org/0000-0003-1328-0331
Zhen JinComplex Systems Research Center, Shanxi University, Taiyuan 030006, Shanxi, China.
Guiquan SunComplex Systems Research Center, Shanxi University, Taiyuan 030006, Shanxi, China.ORCID https://orcid.org/0000-0001-5733-5299
Hongjie YuDepartment of Epidemiology, School of Public Health, Key Laboratory of Public Health Safety, Ministry of Education, Fudan University, Shanghai 200032, China.ORCID https://orcid.org/0000-0002-6335-5648
Xin LuCollege of Systems Engineering, National University of Defense Technology, Changsha 410073, China.ORCID https://orcid.org/0000-0002-3547-6493

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The analysis of connection strengths and distances in the mobility network is pivotal for delineating critical pathways, particularly in the context of epidemic propagation. Local connections that link proximate districts typically exhibit strong weights. However, ties that bridge distant regions with high levels of interaction intensity, termed strong long (SL) ties, warrant increased scrutiny due to their potential to foster satellite epidemic clusters and extend the duration of pandemics. In this study, SL ties are identified as outliers on the joint distribution of distance and flow in the mobility network of Shanghai constructed from 1 km × 1 km high-resolution mobility data. We propose a grid-joint isolation strategy alongside a reaction-diffusion transmission model to assess the impact of SL ties on epidemic propagation. The findings indicate that regions connected by SL ties exhibit a small spatial autocorrelation and display a temporal similarity pattern in disease transmission. Grid-joint isolation based on SL ties reduces cumulative infections by an average of 17.1% compared with other types of ties. This work highlights the necessity of identifying and targeting potentially infected remote areas for spatially focused interventions, thereby enriching our comprehension and management of epidemic dynamics.

Indexed as

epidemic containmentgrid-joint isolation strategymobility networksreaction–diffusion transmission modelstrong long ties

Identifiers

PMID39600802
PMCPMC11589786

What Socratic holds

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LicenceCC BY
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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 Socratic graph.