Evidence map›Paper›PMID 34367890›Full record

ArticleResults in physics2021

COVID-19 deterministic and stochastic modelling with optimized daily vaccinations in Saudi Arabia.

Othman A M Omar, Yousef Alnafisah, Reda A Elbarkouky, Hamdy M Ahmed

Open access · goldAbstract read
In one paragraph

Article in Results in physics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed, 1 pooled it
1.8field-weighted citation impact, top 15% of its field
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

5 citing papers in PubMed, 1 synthesis or guideline pooled it, 23 citations in OpenAlex.

  1. Pooled it
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  3. Article
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  5. 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

4 authors at 4 institutions in 2 countries.

Othman A M OmarPhysics and Engineering Mathematics Department, Faculty of Engineering, Ain Shams University, Cairo 11517, Egypt.
Yousef AlnafisahMathematics Department, College of Science, Qassim University, P.O.Box 6644, Buraydah 51452, Saudi Arabia.
Reda A ElbarkoukyEgyptian Chinese University, Cairo 11725, Egypt.
Hamdy M AhmedHigher Institute of Engineering, El-Shorouk Academy, P.O. 3 El-Shorouk City, Cairo, Egypt.
Ain Shams University · EGEgyptian Russian University · EGHigher Institute of Engineering · EGQassim University · SA

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In this paper, we investigate the stochastic nature of the COVID-19 temporal dynamics by generating a fractional-order dynamic model and a fractional-order-stochastic model. Initially, we considered the first and second vaccination doses as multiple vaccinations were initiated worldwide. The concerned models are then tested for the Saudi Arabia second virus wave, which is assumed to start on 1st March 2021. Four daily vaccination scenarios for the first and second dose are assumed for 100 days from the wave beginning. One of these scenarios is based on function optimization using the invasive weed optimization algorithm (IWO). After that, we numerically solve the established models using the fractional Euler method and the Euler-Murayama method. Finally, the obtained virus dynamics using the assumed scenarios and the real one started by the government are compared. The optimized scenario using the IWO effectively minimizes the predicted cumulative wave infections with a 4.4 % lower number of used vaccination doses.

Indexed as

COVID-19Epidemiological modellingEuler-Murayama methodFractional stochastic dynamic modellingNumerical optimization algorithms

Identifiers

PMID34367890
PMCPMC8327613
OpenAlexW3187585064

What OpenQuestion holds

Textmetadata
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.