Evidence map›Paper›PMID 36481667›Full record

ArticleScientific reports2022

An LBS and agent-based simulator for Covid-19 research.

Hang Du, Zhenming Yuan, Yingfei Wu, Kai Yu, Xiaoyan Sun

Abstract read
In one paragraph

Article in Scientific reports, 2022. 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

5 authors.

Hang DuSchool of Information Science and Technology, Hangzhou Normal University, Hangzhou, 310016, China.
Zhenming YuanSchool of Information Science and Technology, Hangzhou Normal University, Hangzhou, 310016, China.
Yingfei WuSchool of Information Science and Technology, Hangzhou Normal University, Hangzhou, 310016, China.
Kai YuSchool of Information Science and Technology, Hangzhou Normal University, Hangzhou, 310016, China.
Xiaoyan SunSchool of Information Science and Technology, Hangzhou Normal University, Hangzhou, 310016, China. xsunx003@gmail.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The mobility data of citizens provide important information on the epidemic spread including Covid-19. However, the privacy versus security dilemma hinders the utilization of such data. This paper proposed a method to generate pseudo mobility data on a per-agent basis, utilizing the actual geographical environment data provided by LBS to generate the agent-specific mobility trajectories and export them as GPS-like data. Demographic characteristics such as behavior patterns, gender, age, vaccination, and mask-wearing status are also assigned to the agents. A web-based data generator was implemented, enabling users to make detailed settings to meet different research needs. The simulated data indicated the usability of the proposed methods.

Indexed as

COVID-19HumansPrivacy

Identifiers

PMID36481667
PMCPMC9731980

What OpenQuestion 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 OpenQuestion graph.