Evidence map›Paper›PMID 35087601›Full record

ArticleComputational and mathematical methods in medicine2022

VSHR: A Mathematical Model for the Prediction of Second-Wave COVID-19 Epidemics in Malaysia.

Xiang Yu, Lihua Lu, Jiangfan Guo, Haihuan Qin, Chunlei Ji

RetractedOpen access · hybridAbstract readRetracted Publication
In one paragraph

Article in Computational and mathematical methods in medicine, 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 It has been retracted, and should not be counted. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.4field-weighted citation impact, top 48% 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

1 citing paper in PubMed, 3 citations in OpenAlex.

  1. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

5 authors at 1 institution in 1 country.

Xiang YuSchool of Electronics and Information, Shanghai Dianji University, Shanghai 201306, China.ORCID https://orcid.org/0000-0001-7120-1668
Lihua LuSchool of Electronics and Information, Shanghai Dianji University, Shanghai 201306, China.ORCID https://orcid.org/0000-0001-6509-5301
Jiangfan GuoSchool of Electronics and Information, Shanghai Dianji University, Shanghai 201306, China.ORCID https://orcid.org/0000-0002-4826-9872
Haihuan QinSchool of Electronics and Information, Shanghai Dianji University, Shanghai 201306, China.ORCID https://orcid.org/0000-0002-7592-9100
Chunlei JiSchool of Electronics and Information, Shanghai Dianji University, Shanghai 201306, China.
Shanghai Dianji University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Since December 2019, a novel coronavirus (COVID-19) has spread all over the world, causing unpredictable economic losses and public fear. Although vaccines against this virus have been developed and administered for months, many countries still suffer from secondary COVID-19 infections, including the United Kingdom, France, and Malaysia. Observations of COVID-19 infections in the United Kingdom and France and their governance measures showed a certain number of similarities. A further investigation of these countries' COVID-19 transmission patterns suggested that when a turning point appeared, the values of their stringency indices per population density (PSI) were nearly proportional to their absolute infection rate (AIR). To justify our assumptions, we developed a mathematical model named VSHR to predict the COVID-19 turning point for Malaysia. VSHR was first trained on 30-day infection records prior to the United Kingdom, Germany, France, and Belgium's known turning points. It was then transferred to Malaysian COVID-19 data to predict this nation's turning point. Given the estimated AIR parameter values in 5 days, we were now able to locate the turning point's appearance on June 2

Indexed as

EpidemicsEpidemiological ModelsSARS-CoV-2AlgorithmsBelgiumComputational BiologyComputer SimulationCOVID-19FranceGermanyHumansMalaysiaUnited Kingdom

Identifiers

PMID35087601
PMCPMC8789418
OpenAlexW4205447107

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.