Evidence map›Paper›PMID 32331787›Full record

SynthesisArchives of medical research2020

Comparative Global Epidemiological Investigation of SARS-CoV-2 and SARS-CoV Diseases Using Meta-MUMS Tool Through Incidence, Mortality, and Recovery Rates.

Massoud Sokouti, Ramin Sadeghi, Saeid Pashazadeh, Saeid Eslami, Mohsen Sokouti, Morteza Ghojazadeh, Babak Sokouti

Open access · greenAbstract readComparative StudyMeta-Analysis
In one paragraph

Synthesis in Archives of medical research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed, 2 pooled it
0.5field-weighted citation impact, top 28% 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

9 citing papers in PubMed, 2 syntheses or guidelines pooled it, 15 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. Article
  4. TargetingPulmonary circulation · 2023
    Article
  5. A review onExploration of targeted anti-tumor therapy · 2023
    Review
  6. Strategies to DAMPen COVID-19-mediated lung and systemic inflammation and vascular injury.Translational research : the journal of laboratory and clinical medicine · 2021
    Review
  7. Review
  8. IoMT amid COVID-19 pandemic: Application, architecture, technology, and security.Journal of network and computer applications (Online) · 2021
    Review
  9. 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

7 authors at 3 institutions in 1 country.

Massoud SokoutiNuclear Medicine Research Center, Mashhad University of Medical Sciences, Mashhad, Iran.
Ramin SadeghiNuclear Medicine Research Center, Mashhad University of Medical Sciences, Mashhad, Iran. Electronic address: raminsadeghi1355@yahoo.com.
Saeid PashazadehFaculty of Computer and Electrical Engineering, University of Tabriz, Tabriz, Iran.
Saeid EslamiDepartment of Medical Informatics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran.
Mohsen SokoutiDepartment of Cardiothoracic Surgery, Tabriz University of Medical Sciences, Tabriz, Iran.
Morteza GhojazadehResearch Center of Evidence-Based Medicine, Tabriz University of Medical Sciences, Tabriz, Iran.
Babak SokoutiBiotechnology Research Center, Tabriz University of Medical Sciences, Tabriz, Iran. Electronic address: b.sokouti@gmail.com.
Mashhad University of Medical Sciences · IRTabriz University of Medical Sciences · IRUniversity of Tabriz · IR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

COVID-19 is a novel coronavirus that was reported by the world health organization in late December 2019. As an unexplained respiratory disease epidemic, which is similar to respiratory syndrome coronavirus SARS-CoV, it rapidly spread all over the world. The study aims to compare several parameters of COVID-19 and SARS-CoV infectious diseases in terms of incidence, mortality, and recovery rates. The publicly available dataset Worldometer (extracted on April 5, 2020) confirmed by WHO report was available for meta-analysis purposes using the Meta-MUMS tool. And, the reported outcomes of the analysis used a random-effects model to evaluate the event rate, and risk ratios thorough subgroup analysis forest plots. Seventeen countries for COVID-19 and eight countries of SARS infections, including COVID-19 group n = 1124243, and SARS-CoV group n = 8346, were analyzed. In this meta-analysis, a random effect model of relations of incidence, mortality, and recovery rates of COVID-19 and SARS world infections were determined. The meta-analysis and forest plots of two viral world infections showed that the incidence rate of COVID-19 infection is more than SARS infections, while recovery and mortality event rates of SARS-CoV are more than COVID-19 infection. And subgroup analysis showed that the mortality and recovery rates were higher in both SARS-CoV wand COVID-19 in comparison to incidence and mortality rates, respectively. In conclusion, the meta-analysis approach on the abovementioned dataset revealed the epidemiological and statistical analyses for comparing COVID-19 and SARS-CoV outbreaks.

Indexed as

Coronavirus InfectionsCOVID-19Datasets as TopicHumansIncidencePandemicsPneumonia, ViralSevere Acute Respiratory SyndromeSurvival RateCOVID-19EpidemiologyMeta-analysisMeta-MUMSSARS-CoV

Identifiers

PMID32331787
PMCPMC7158781
OpenAlexW3017233227

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.