Evidence map›Paper›PMID 36703133›Full record

ArticleBMC medical informatics and decision making2023

Big data- and artificial intelligence-based hot-spot analysis of COVID-19: Gauteng, South Africa, as a case study.

Benjamin Lieberman, Jude Dzevela Kong, Roy Gusinow, Ali Asgary, Nicola Luigi Bragazzi, Joshua Choma, Salah-Eddine Dahbi, Kentaro Hayashi, Deepak Kar, Mary Kawonga and 7 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC medical informatics and decision making, 2023. 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. Review
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

17 authors.

Benjamin LiebermanSchool of Physics and Institute for Collider Particle Physics, University of the Witwatersrand, Johannesburg, South Africa. benjamin.lieberman@cern.ch.
Jude Dzevela KongDepartment of Mathematics and Statistics, York University, Toronto, Canada.
Roy GusinowSchool of Physics and Institute for Collider Particle Physics, University of the Witwatersrand, Johannesburg, South Africa.
Ali AsgaryDisaster and Emergency Management, School of Administrative Studies and Advanced Disaster, Emergency and Rapid-response Simulation, York University, Toronto, Canada.
Nicola Luigi BragazziDepartment of Mathematics and Statistics, York University, Toronto, Canada.
Joshua ChomaSchool of Physics and Institute for Collider Particle Physics, University of the Witwatersrand, Johannesburg, South Africa.
Salah-Eddine DahbiSchool of Physics and Institute for Collider Particle Physics, University of the Witwatersrand, Johannesburg, South Africa.
Kentaro HayashiSchool of Computer Science and Applied Mathematics, University of the Witwatersrand, Johannesburg, South Africa.
Deepak KarSchool of Physics and Institute for Collider Particle Physics, University of the Witwatersrand, Johannesburg, South Africa.
Mary KawongaSchool of Public Health, University of the Witwatersrand, Johannesburg, South Africa.
Mduduzi MbadaAfrica-Canada Artificial Intelligence and Data Innovation Consortium (ACADIC), Toronto, Canada.
Kgomotso MonnakgotlaSchool of Physics and Institute for Collider Particle Physics, University of the Witwatersrand, Johannesburg, South Africa.
James OrbinskiAfrica-Canada Artificial Intelligence and Data Innovation Consortium (ACADIC), Toronto, Canada.
Xifeng RuanSchool of Physics and Institute for Collider Particle Physics, University of the Witwatersrand, Johannesburg, South Africa.
Finn StevensonSchool of Physics and Institute for Collider Particle Physics, University of the Witwatersrand, Johannesburg, South Africa.
Jianhong WuDepartment of Mathematics and Statistics, York University, Toronto, Canada.
Bruce MelladoSchool of Physics and Institute for Collider Particle Physics, University of the Witwatersrand, Johannesburg, South Africa.

Funding

International Development Research Centre 109559-001
6 · The paper itself

Abstract

The coronavirus disease 2019 (COVID-19) has developed into a pandemic. Data-driven techniques can be used to inform and guide public health decision- and policy-makers. In generalizing the spread of a virus over a large area, such as a province, it must be assumed that the transmission occurs as a stochastic process. It is therefore very difficult for policy and decision makers to understand and visualize the location specific dynamics of the virus on a more granular level. A primary concern is exposing local virus hot-spots, in order to inform and implement non-pharmaceutical interventions. A hot-spot is defined as an area experiencing exponential growth relative to the generalised growth of the pandemic. This paper uses the first and second waves of the COVID-19 epidemic in Gauteng Province, South Africa, as a case study. The study aims provide a data-driven methodology and comprehensive case study to expose location specific virus dynamics within a given area. The methodology uses an unsupervised Gaussian Mixture model to cluster cases at a desired granularity. This is combined with an epidemiological analysis to quantify each cluster's severity, progression and whether it can be defined as a hot-spot.

Indexed as

COVID-19Artificial IntelligenceBig DataHumansPandemicsSouth AfricaArtificial intelligenceBig dataControl interventionCOVID-19Gauteng department of healthHot-spotRisk adjusted strategySouth Africa

Identifiers

PMID36703133
PMCPMC9879257

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.