Evidence map›Paper›PMID 38404914›Full record

ArticleInfectious Disease Modelling2024

An AI-empowered indoor digital contact tracing system for COVID-19 outbreaks in residential care homes.

Jiahui Meng, Justina Yat Wa Liu, Lin Yang, Man Sing Wong, Hilda Tsang, Boyu Yu, Jincheng Yu, Freddy Man-Hin Lam, Daihai He, Lei Yang and 6 more

Abstract read
In one paragraph

Article in Infectious Disease Modelling, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Trial
  2. 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

16 authors.

Jiahui MengSchool of Nursing, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region, China.
Justina Yat Wa LiuSchool of Nursing, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region, China.
Lin YangSchool of Nursing, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region, China.
Man Sing WongDepartment of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region, China.
Hilda TsangSchool of Nursing, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region, China.
Boyu YuDepartment of Applied Mathematics, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region, China.
Jincheng YuThe Jockey Club School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China.
Freddy Man-Hin LamDepartment of Rehabilitation Sciences, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region, China.
Daihai HeDepartment of Applied Mathematics, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region, China.
Lei YangDepartment of Computing, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region, China.
Yan LiSchool of Nursing, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region, China.
Gilman Kit-Hang SiuDepartment of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region, China.
Stefanos TyrovolasSchool of Nursing, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region, China.
Yao Jie XieSchool of Nursing, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region, China.
David ManTung Wah College, Hong Kong Special Administrative Region, China.
David H K ShumDepartment of Rehabilitation Sciences, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

An AI-empowered indoor digital contact-tracing system was developed using a centralized architecture and advanced low-energy Bluetooth technologies for indoor positioning, with careful preservation of privacy and data security. We analyzed the contact pattern data from two RCHs and investigated a COVID-19 outbreak in one study site. To evaluate the effectiveness of the system in containing outbreaks with minimal contacts under quarantine, a simulation study was conducted to compare the impact of different quarantine strategies on outbreak containment within RCHs. The significant difference in contact hours between weekdays and weekends was observed for some pairs of RCH residents and staff during the two-week data collection period. No significant difference between secondary cases and uninfected contacts was observed in a COVID-19 outbreak in terms of their demographics and contact patterns. Simulation results based on the collected contact data indicated that a threshold of accumulative contact hours one or two days prior to diagnosis of the index case could dramatically increase the efficiency of outbreak containment within RCHs by targeted isolation of the close contacts. This study demonstrated the feasibility and efficiency of employing an AI-empowered system in indoor digital contact tracing of outbreaks in RCHs in the post-pandemic era.

Indexed as

Artificial intelligenceContact patternCOVID-19Indoor contact tracingOutbreak containment

Identifiers

PMID38404914
PMCPMC10885586

What Socratic 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 Socratic graph.