Evidence map›Paper›PMID 32630363›Full record

ArticleInternational journal of environmental research and public health2020

Predicting the Epidemiological Outbreak of the Coronavirus Disease 2019 (COVID-19) in Saudi Arabia.

Dabiah Alboaneen, Bernardi Pranggono, Dhahi Alshammari, Nourah Alqahtani, Raja Alyaffer

Abstract read
In one paragraph

Article in International journal of environmental research and public health, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing 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

15 citing papers in PubMed.

  1. Article
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  11. Article
  12. Article
  13. COVID-19 Spread in Saudi Arabia: Modeling, Simulation and Analysis.International journal of environmental research and public health · 2020
    Article
  14. Data Analytics for Predicting COVID-19 Cases in Top Affected Countries: Observations and Recommendations.International journal of environmental research and public health · 2020
    Article
  15. 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.

Dabiah AlboaneenComputer Science Department, College of Science and Humanities in Jubail, Imam Abdulrahman Bin Faisal University, Jubail P.O. Box 31961, Saudi Arabia.ORCID 0000-0003-2215-9963
Bernardi PranggonoDepartment of Engineering and Mathematics, Sheffield Hallam University, Sheffield S1 1WB, UK.ORCID 0000-0002-2992-697X
Dhahi AlshammariComputer Science and Information Department, College of Computer Science and Engineering, University of Ha'il, Hail 8145, Saudi Arabia.
Nourah AlqahtaniComputer Science Department, College of Science and Humanities in Jubail, Imam Abdulrahman Bin Faisal University, Jubail P.O. Box 31961, Saudi Arabia.
Raja AlyafferComputer Science Department, College of Science and Humanities in Jubail, Imam Abdulrahman Bin Faisal University, Jubail P.O. Box 31961, Saudi Arabia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The coronavirus diseases 2019 (COVID-19) outbreak continues to spread rapidly across the world and has been declared as pandemic by World Health Organization (WHO). Saudi Arabia was among the countries that was affected by the deadly and contagious virus. Using a real-time data from 2 March 2020 to 15 May 2020 collected from Saudi Ministry of Health, we aimed to give a local prediction of the epidemic in Saudi Arabia. We used two models: the Logistic Growth and the Susceptible-Infected-Recovered for real-time forecasting the confirmed cases of COVID-19 across Saudi Arabia. Our models predicted that the epidemics of COVID-19 will have total cases of 69,000 to 79,000 cases. The simulations also predicted that the outbreak will entering the final-phase by end of June 2020.

Indexed as

ForecastingPandemicsBetacoronavirusCoronavirus InfectionsCOVID-19Health SurveysHumansModels, BiologicalPneumonia, ViralSARS-CoV-2Saudi Arabia2019 novel coronavirusCOVID-19logistic growth modelSaudi ArabiaSIR model

Identifiers

PMID32630363
PMCPMC7344859

What OpenQuestion holds

Textmetadata
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.