Evidence map›Paper›PMID 34131453›Full record

ArticleProcedia computer science2021

AI in Fighting Covid-19: Pandemic Management.

Abhishek Tripathi, Parmeet Kaur, Shwetha Suresh

Abstract read
In one paragraph

Article in Procedia computer science, 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.

Abhishek TripathiThe College of New Jersey, 2000 Pennington Road, Ewing Township and 08618, USA.
Parmeet KaurThe College of New Jersey, 2000 Pennington Road, Ewing Township and 08618, USA.
Shwetha SureshThe College of New Jersey, 2000 Pennington Road, Ewing Township and 08618, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Coronaviruses are a family of viruses found in several animal species, such as bats, cattle, cats, camels, and humans. With more than 1.6 million people dead worldwide, as of December 2020, the Covid-19 pandemic has brought about a unified need to address global health crises more aggressively. There is great urgency in decreasing the impact of a potential future outbreak, which can be done by gathering information about the disease and its effects on humans. Various artificial intelligence (AI) techniques can be utilized for the pandemic, such as COVID (CoV) management, a vast scientific field involving computers performing tasks capable of only human brains. Among the subsets of AI, there are Machine Learning (ML) techniques, which can learn from historical data examples without programming. While no prior data regarding the virus exists, the growing cases make for more data. In this research, we employ a literature review method to understand pandemic management's current state and how it can benefit by utilizing AI capabilities.

Indexed as

Artificial IntelligenceCoronavirusCovid-19Intelligent CitiesMachine LearningPandemic ManagementSmart Cities

Identifiers

PMID34131453
PMCPMC8191524

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.