COVID-19: Mathematical growth vs. precautionary measures in China, KSA, and the USA.

M A El-Shorbagy, Adel M El-Refaey

Abstract readReview

In one paragraph

Review in Informatics in medicine unlocked, 2022. 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.

A deep learning-based approach for predicting COVID-19 diagnosis.Heliyon · 2024
Article
HIV and COVID-19 co-infection: A mathematical model and optimal control.Informatics in medicine unlocked · 2022
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

2 authors.

M A El-ShorbagyDepartment of Mathematics, College of Science and Humanities in Al-Kharj, Prince Sattam bin Abdulaziz University, Al-Kharj, 11942, Saudi Arabia.

Adel M El-RefaeyBasic and Applied Science Department, College of Engineering and Technology, Arab Academy for Science, Technology and Maritime Transport, Smart Village Campus, Egypt.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This paper aims to study the relation between precautionary measures that were taken by countries to prevent the spread of COVID-19 and its impact on its mathematical growth. In this paper, we study the development and growth of the epidemic during the first fifty days since its appearance in three countries: China, the Kingdom of Saudi Arabia (KSA), and the United States of America (USA). An optimization process is used to determine the parameters of the closest model that simulates the data during the specified period by using one of the evolutionary computation techniques, the grasshopper optimization algorithm (GOA). The study reveals that the strict precautionary measures of applying isolation and quarantine, preventing all gatherings, and a total curfew are the only way to prevent the spread of the epidemic exponentially as China did. Also, without any measures to slow its growth, COVID-19 will continue to spread steadily for months.

Indexed as

COVID-19Epidemic growth mathematicallyNovel coronavirusOptimization processPrecautionary measures

Identifiers

PMID34977332

DOI10.1016/j.imu.2021.100834

PMCPMC8713421

What OpenQuestion holds

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Registered trials

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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.