Evidence map›Paper›PMID 35431642›Full record

ArticleSoft computing2022

RETRACTED ARTICLE: Predicting the spread of COVID-19 with a machine learning technique and multiplicative calculus.

Bülent Bilgehan, Ali Özyapıcı, Zakia Hammouch, Yusuf Gurefe

RetractedAbstract readRetracted Publication
PubMed Full text at PMC Publisher
In one paragraph

Article in Soft computing, 2022. 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 It has been retracted, and should not be counted. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

4 authors.

Bülent BilgehanDepartment of Electrical and Electronic Engineering, Near East University, Nicosia, Mersin 10 Turkey.
Ali ÖzyapıcıFaculty of Engineering, Cyprus International University, Nicosia, Mersin 10 Turkey.
Zakia HammouchDivision of Applied Mathematics, Thu Dau Mot University, Thu Dau Mot, Binh Duong Province Vietnam.
Yusuf GurefeDepartment of Mathematics, Mersin University, Mersin, Turkey.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This paper aims to generate a universal well-fitted mathematical model to aid global representation of the spread of the coronavirus (COVID-19) disease. The model aims to identify the importance of the measures to be taken in order to stop the spread of the virus. It describes the diffusion of the virus in normal life with and without precaution. It is a data-driven parametric dependent function, for which the parameters are extracted from the data and the exponential function derived using multiplicative calculus. The results of the proposed model are compared to real recorded data from different countries and the performance of this model is investigated using error analysis theory. We stress that all statistics, collected data, etc., included in this study were extracted from official website of the World Health Organization (WHO). Therefore, the obtained results demonstrate its applicability and efficiency.

Indexed as

COVID-19 modelMultiplicative data fittingMultiplicative least square methodSimulation

Identifiers

PMID35431642
PMCPMC8994092

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.