Evidence map›Paper›PMID 35634031›Full record

ArticleAIMS public health2022

Analysis of COVID-19 epidemic model with sumudu transform.

Muhammad Farman, Muhammad Azeem, M O Ahmad

Abstract read
In one paragraph

Article in AIMS public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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0 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

3 authors.

Muhammad FarmanDepartment of Mathematics and Statistics, The University of Lahore, Lahore, Pakistan.
Muhammad AzeemDepartment of Mathematics and Statistics, The University of Lahore, Lahore, Pakistan.
M O AhmadDepartment of Mathematics and Statistics, The University of Lahore, Lahore, Pakistan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In this paper, we develop a time-fractional order COVID-19 model with effects of disease during quarantine which consists of the system of fractional differential equations. Fractional order COVID-19 model is investigated with ABC technique using sumudu transform. Also, the deterministic mathematical model for the quarantine effect is investigated with different fractional parameters. The existence and uniqueness of the fractional-order model are derived using fixed point theory. The sumudu transform can keep the unity of the function, the parity of the function, and has many other properties that are more valuable. Solutions are derived to investigate the influence of fractional operator which shows the impact of the disease during quarantine on society.

Indexed as

epidemic modelfractional derivativestabilitysumudu transformunique solution

Identifiers

PMID35634031
PMCPMC9114793

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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.