Evidence map›Paper›PMID 34220301›Full record

ArticleSoft computing2023

Hybrid harmony search algorithm for social network contact tracing of COVID-19.

Ala'a Al-Shaikh, Basel A Mahafzah, Mohammad Alshraideh

RetractedAbstract readRetracted Publication
In one paragraph

Article in Soft computing, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It has been retracted, and should not be counted. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Article
  3. The goat search algorithms.Artificial intelligence review · 2022
    Article
  4. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

3 authors.

Ala'a Al-ShaikhLearning and Teaching Technology Center, Al-Balqa Applied University, Al-Salt, 19117 Jordan.ORCID 0000-0002-1888-759X
Basel A MahafzahDepartment of Computer Science, King Abdulla II School of Information Technology, The University of Jordan, Amman, 11942 Jordan.ORCID 0000-0003-3979-1076
Mohammad AlshraidehDepartment of Computer Science, King Abdulla II School of Information Technology, The University of Jordan, Amman, 11942 Jordan.ORCID 0000-0002-2724-9290

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The coronavirus disease 2019 (COVID-19) was first reported in December 2019 in Wuhan, China, and then moved to almost every country showing an unprecedented outbreak. The world health organization declared COVID-19 a pandemic. Since then, millions of people were infected, and millions have lost their lives all around the globe. By the end of 2020, effective vaccines that could prevent the fast spread of the disease started to loom on the horizon. Nevertheless, isolation, social distancing, face masks, and quarantine are the best-known measures, in the time being, to fight the pandemic. On the other hand, contact tracing is an effective procedure in tracking infections and saving others' lives. In this paper, we devise a new approach using a hybrid harmony search (HHS) algorithm that casts the problem of finding strongly connected components (SCCs) to contact tracing. This new approach is named as hybrid harmony search contact tracing (HHS-CT) algorithm. The hybridization is achieved by integrating the stochastic hill climbing into the operators' design of the harmony search algorithm. The HHS-CT algorithm is compared to other existing algorithms of finding SCCs in directed graphs, where it showed its superiority over these algorithms. The devised approach provides a 77.18% enhancement in terms of run time and an exceptional average error rate of 1.7% compared to the other existing algorithms of finding SCCs.

Indexed as

Contact tracingCoronavirusCOVID-19Harmony search algorithmHill climbingMetaheuristic approachSocial networks

Identifiers

PMID34220301
PMCPMC8237257

What OpenQuestion holds

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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.