Evidence map›Paper›PMID 33828346›Full record

ArticleApplied mathematics and computation2021

Short-term predictions and prevention strategies for COVID-19: A model-based study.

Sk Shahid Nadim, Indrajit Ghosh, Joydev Chattopadhyay

Open access · greenAbstract read
In one paragraph

Article in Applied mathematics and computation, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 48 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
48citing papers in PubMed, 1 pooled it
7.8field-weighted citation impact, top 2% 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

48 citing papers in PubMed, 1 synthesis or guideline pooled it, 129 citations in OpenAlex.

  1. Pooled it
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  7. Review
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  11. Uncertainty Quantification for Epidemic Risk Management: Case of SARS-CoV-2 in Morocco.International journal of environmental research and public health · 2023
    Article
  12. Article
  13. Article
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  18. Global Analysis and Optimal Control Model of COVID-19.Computational and mathematical methods in medicine · 2022
    Article
  19. Article
  20. Mathematical modelling for decision making of lockdown during COVID-19.Applied intelligence (Dordrecht, Netherlands) · 2022
    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 at 1 institution in 1 country.

Sk Shahid NadimAgricultural and Ecological Research Unit, Indian Statistical Institute, Kolkata 700108, West Bengal, India.
Indrajit GhoshDepartment of Computational and Data Sciences, Indian Institute of Science, Bengalore 560012, Karnataka, India.
Joydev ChattopadhyayAgricultural and Ecological Research Unit, Indian Statistical Institute, Kolkata 700108, West Bengal, India.
Indian Statistical Institute · IN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

An outbreak of respiratory disease caused by a novel coronavirus is ongoing from December 2019. As of December 14, 2020, it has caused an epidemic outbreak with more than 73 million confirmed infections and above 1.5 million reported deaths worldwide. During this period of an epidemic when human-to-human transmission is established and reported cases of coronavirus disease 2019 (COVID-19) are rising worldwide, investigation of control strategies and forecasting are necessary for health care planning. In this study, we propose and analyze a compartmental epidemic model of COVID-19 to predict and control the outbreak. The basic reproduction number and the control reproduction number are calculated analytically. A detailed stability analysis of the model is performed to observe the dynamics of the system. We calibrated the proposed model to fit daily data from the United Kingdom (UK) where the situation is still alarming. Our findings suggest that independent self-sustaining human-to-human spread (

Indexed as

Basic reproduction numberControl strategiesCoronavirus diseaseMathematical modelModel calibration and predictionUnited Kingdom

Identifiers

PMID33828346
PMCPMC8015415
OpenAlexW3010697270

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.