Evidence map›Paper›PMID 32963585›Full record

ArticleComputational and mathematical methods in medicine2020

Controlling the Spread of COVID-19: Optimal Control Analysis.

Chinwendu E Madubueze, Sambo Dachollom, Isaac Obiajulu Onwubuya

Abstract read
In one paragraph

Article in Computational and mathematical methods in medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
24citing papers in PubMed, 1 pooled it
–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

24 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  12. Modeling and Analysis of COVID-19 Spread: The Impacts of Nonpharmaceutical Protocols.Computational and mathematical methods in medicine · 2022
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  17. Optimal control-based vaccination and testing strategies for COVID-19.Computer methods and programs in biomedicine · 2021
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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.

Chinwendu E MadubuezeDepartment of Mathematics/Statistics/Computer Science, University of Agriculture Makurdi, P.M.B. 2373, Markurdi, Nigeria.ORCID https://orcid.org/0000-0003-3391-3896
Sambo DachollomDepartment of Mathematics/Statistics, Akanu Ibiam Federal Polytechnic, Unwana, P.M.B. 1007 Afikpo, Ebonyi State, Nigeria.
Isaac Obiajulu OnwubuyaDepartment of Mathematics, Airforce Institute of Technology, Kaduna, Nigeria.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Coronavirus disease 2019 (COVID-19) is a disease caused by severe acute respiratory syndrome coronavirus 2 (SARS CoV-2). It was declared on March 11, 2020, by the World Health Organization as pandemic disease. The disease has neither approved medicine nor vaccine and has made governments and scholars search for drastic measures in combating the pandemic. Regrettably, the spread of the virus and mortality due to COVID-19 has continued to increase daily. Hence, it is imperative to control the spread of the disease particularly using nonpharmacological strategies such as quarantine, isolation, and public health education. This work studied the effect of these different control strategies as time-dependent interventions using mathematical modeling and optimal control approach to ascertain their contributions in the dynamic transmission of COVID-19. The model was proven to have an invariant region and was well-posed. The basic reproduction number and effective reproduction numbers were computed with and without interventions, respectively, and were used to carry out the sensitivity analysis that identified the critical parameters contributing to the spread of COVID-19. The optimal control analysis was carried out using the Pontryagin's maximum principle to figure out the optimal strategy necessary to curtail the disease. The findings of the optimal control analysis and numerical simulations revealed that time-dependent interventions reduced the number of exposed and infected individuals compared to time-independent interventions. These interventions were time-bound and best implemented within the first 100 days of the outbreak. Again, the combined implementation of only two of these interventions produced a good result in reducing infection in the population. While, the combined implementation of all three interventions performed better, even though zero infection was not achieved in the population. This implied that multiple interventions need to be deployed early in order to reduce the virus to the barest minimum.

Indexed as

BetacoronavirusModels, BiologicalBasic Reproduction NumberComputer SimulationCoronavirus InfectionsCOVID-19Health EducationHumansMathematical ConceptsPandemicsPneumonia, ViralPublic HealthQuarantineSARS-CoV-2

Identifiers

PMID32963585
PMCPMC7499329

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Registered trials

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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.