Evidence map›Paper›PMID 40239175›Full record

ArticlePloS one2025

Optimal control analysis for the transmission of Nipah infection with imperfect vaccination.

Mengqi Xie, Muhammad Younas Khan, Saif Ullah, Muhammad Farooq, Muhammad Bilal Riaz, Basem Al Alwan

Abstract read
In one paragraph

Article in PloS one, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

6 authors.

Mengqi XieDepartment of ElectronicInformation Engineering, Xi'an Technological University, Xi'an, China.
Muhammad Younas KhanDepartmentof Mathematics, University of Peshawar, Khyber Pakhtunkhwa, Pakistan.
Saif UllahDepartmentof Mathematics, University of Peshawar, Khyber Pakhtunkhwa, Pakistan.ORCID 0000-0002-4338-6850
Muhammad FarooqDepartmentof Mathematics, University of Peshawar, Khyber Pakhtunkhwa, Pakistan.
Muhammad Bilal RiazIT4Innovations, VSB-Technical University of Ostrava, Ostrava, Czech Republic.
Basem Al AlwanChemicalEngineering Department, College of Engineering, King Khalid University, Abha,Saudi Arabia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This paper presents an innovative mathematical model for assessing the dynamics and optimal control of Nipah virus (NiV) with imperfect vaccination. The model formulation considers transmissions through contaminated food and human-to-human contacts. It also incorporates the potential virus transmission through contact with a deceased body infected with NiV. Initially, the NiV model is assessed theoretically, identifying three distinct equilibrium states: the NiV-endemic equilibrium state, the NiV-free equilibrium state, and the equilibrium state involving infected flying foxes. Furthermore, the stability results of the model in the case of constant controls are thoroughly analyzed at the NiV-free equilibrium. Some of the parameters of the model are estimated based on the infected cases documented in Bangladesh from 2001 to 2017. We further perform sensitivity analysis to determine the most influential parameters and formulate effective time-dependent controls. Numerical simulations indicate the optimal course of action for eradicating the disease and provide a comparative analysis of controlling the infection under constant and time-varying interventions. The simulation confirms that the implementation of time-varying interventions is effective in minimizing disease incidence.

Indexed as

Henipavirus InfectionsNipah VirusVaccinationAnimalsBangladeshComputer SimulationHumansModels, Theoretical

Identifiers

PMID40239175
PMCPMC12002807

What OpenQuestion holds

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LicenceCC BY
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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.