Evidence map›Paper›PMID 34485681›Full record

ArticleInformatics in medicine unlocked2021

Data interpretation and visualization of COVID-19 cases using R programming.

Yagyanath Rimal, Saikat Gochhait, Aakriti Bisht

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Informatics in medicine unlocked, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

3 authors.

Yagyanath RimalPokhara University, Nepal.
Saikat GochhaitSymbiosis Institute of Digital and Telecom Managment, constituent of Symbiosis International (Deemed University), Pune, India.
Aakriti BishtBirla Institute of Technology, Pilani, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundData analysis and visualization are essential for exploring and communicating medical research findings, especially when working with COVID records.

resultsData on COVID-19 diagnosed cases and deaths from December 2019 is collected automatically from www.statista.com, datahub.io, and the Multidisciplinary Digital Publishing Institute (MDPI). We have developed an application for data visualization and analysis of several indicators to follow the SARS-CoV-2 epidemic using Statista, Data Hub, and MDPI data from densely populated countries like the United States, Japan, and India using R programming.

conclusionsThe COVID19-World online web application systematically produces daily updated country-specific data visualization and analysis of the SARS-CoV-2 epidemic worldwide. The application will help with a better understanding of the SARS-CoV-2 epidemic worldwide.

Indexed as

CoronavirusCovid-19Data visualizationMachine learningOpen data map

Identifiers

PMID34485681
PMCPMC8404394

What OpenQuestion holds

Textmetadata
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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.