Toward COVID-19 Contact Tracing though Wi-Fi Probes.

Xu Yang, Chenqi Shi, Peihao Li, Yuqing Yin, Qiang Niu

Abstract read

In one paragraph

Article in Sensors (Basel, Switzerland), 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.

Risk assessment of communicable respiratory diseases transmission based on social contact networks: a primary school contact data survey conducted with portable high-precision devices.BMC public health · 2025
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.

Xu YangSchool of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221000, China.ORCID 0000-0002-2651-3432

Chenqi ShiSchool of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221000, China.ORCID 0000-0001-5589-1582

Peihao LiSchool of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221000, China.

Yuqing YinSchool of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221000, China.ORCID 0000-0002-3296-4256

Qiang NiuSchool of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221000, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

COVID-19 is currently the biggest threat that challenges all of humankind's health and property. One promising and effective way to control the rapid spreading of this infection is searching for primary close contacts of the confirmed cases. In response, we propose COVID-19 Tracer, a low-cost passive searching system to find COVID-19 patients' close contacts. The main idea is utilizing ubiquitous WiFi probe requests to describe the location similarity, which is then achieved by two designed range-free judgment indicators: location similarity coefficient and close contact distance. We have carried out extensive experiments in a school office building, and the experimental results show an average accuracy of more than 98%, demonstrating our system's effectiveness in judging close contacts. Last but not least, we have developed a prototype system for a school building to find potential close contacts.

Indexed as

Contact TracingCOVID-19Data CollectionHumansSchoolsclose contactCOVID-19WiFi probe requests

Identifiers

PMID35336425

DOI10.3390/s22062255

PMCPMC8949136

What OpenQuestion holds

Textmetadata

LicenceCC BY

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.