Evidence map›Paper›PMID 39080433›Full record

ArticleScientific reports2024

Optimal control analysis for the Nipah infection with constant and time-varying vaccination and treatment under real data application.

Muhammad Younas Khan, Saif Ullah, Muhammad Farooq, Basem Al Alwan, Abdul Baseer Saqib

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In one paragraph

Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

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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

4 citing papers in PubMed.

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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

5 authors.

Muhammad Younas KhanDepartment of Mathematics, University of Peshawar, Peshawar, KP, 25000, Pakistan.
Saif UllahDepartment of Mathematics, University of Peshawar, Peshawar, KP, 25000, Pakistan.
Muhammad FarooqDepartment of Mathematics, University of Peshawar, Peshawar, KP, 25000, Pakistan.
Basem Al AlwanChemical Engineering Department, College of Engineering, King Khalid University, Abha, 62521, Saudi Arabia.
Abdul Baseer SaqibFaculty of Education, Department of Mathematics, Nangarhar University, Nangarhar, Afghanistan. ab_saqib@nu.edu.af.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In the last two decades, Nipah virus (NiV) has emerged as a significant paramyxovirus transmitted by bats, causing severe respiratory illness and encephalitis in humans. NiV has been included in the World Health Organization's Blueprint list of priority pathogens due its potential for human-to-human transmission and zoonotic characteristics. In this paper, a mathematical model is formulated to analyze the dynamics and optimal control of NiV. In formulation of the model we consider two modes of transmission: human-to-human and food-borne. Further, the impact of contact with an infected corpse as a potential route for virus transmission is also consider in the model. The analysis identifies the model with constant controls has three equilibrium states: the NiV-free equilibrium, the infected flying foxes-free equilibrium, and the NiV-endemic equilibrium state. Furthermore, a theoretical analysis is conducted to presents the stability of the model equilibria. The model fitting to the reported cases in Bangladesh from 2001 to 2015, and the estimation of parameters are performed using the standard least squares technique. Sensitivity analysis of the model-embedded parameters is provided to set the optimal time-dependent controls for the disease eradication. The necessary optimality conditions are derived using Pontryagin's maximum principle. Finally, numerical simulation is performed to determine the most effective strategy for disease eradication and to confirm the theoretical results.

Indexed as

Henipavirus InfectionsNipah VirusVaccinationAnimalsBangladeshChiropteraHumansModels, TheoreticalConstant and time-varying controlsNiV outbreakPontryagin’s maximum principleSensitivity analysisSimulation

Identifiers

PMID39080433
PMCPMC11289478

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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.