Evidence map›Paper›PMID 41917107›Full record

ArticleScientific reports2026

Bang-bang control optimization in infectious disease model with incorporating breakthrough and reinfection.

Ya Chen, Wenjun Jing, Juping Zhang, Peng Qin

Abstract read
In one paragraph

Article in Scientific reports, 2026. 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

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

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3 · Its place in the literature

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0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Ya ChenSchool of Mathematics, Xi'an University of Finance and Economics, Xi'an, 710100, Shaanxi, China.
Wenjun JingSchool of Statistics, Shanxi University of Finance and Economics, Taiyuan, 030006, Shanxi, China.
Juping ZhangComplex Systems Research Center, Shanxi University, Taiyuan, 030006, Shanxi, China.
Peng QinSchool of Electrical and Control Engineering, North University of China, Taiyuan, 030006, Shanxi, China. qinpeng@nuc.edu.cn.

Funding

Fundamental Research Program of Shanxi Province 202403021211154National Sciences Foundation of China 12101373National Sciences Foundation of China 12171291National Social Science Fund of China 12471462
6 · The paper itself

Abstract

Breakthrough infections and reinfection are key factors leading to recurrent epidemic waves. However, sustained control strategies can lead to unnecessary resource wastage when tacking these issues. There is an urgent need to establish dynamic intervention systems capable of rapid response and efficient resource utilization. To address the question of how breakthrough infections and reinfections affect the dynamics of the pandemic, this study develops an infectious disease model that incorporates both breakthrough infections and reinfections, and while introduces bang-bang optimal control as an efficient public health intervention strategy to provide a new perspective and solutions. In theoretical analysis, we derive basic reproduction number via next-generation matrix method, prove the global stability of the disease-free equilibrium, and establish sufficient conditions for the existence of multiple endemic equilibria and the occurrence of backward bifurcation. Numerical simulations further confirm the critical role of breakthrough infections and reinfection in disease persistence and recurrent outbreaks. In control strategy research, we prove the existence of bang-bang optimal solutions based on optimal control theory and demonstrate their distinct advantages in rapidly outbreaks while minimizing operational costs. Simulation results show that a combined strategy implemented under the bang-bang control-reducing transmission rates, expanding vaccine coverage, and enhancing vaccine protection-most effectively contains disease spread. This results provide both theoretical foundation and practical guidance for developing efficient control strategies against recurrent infectious disease outbreaks.

Indexed as

Communicable Disease ControlCommunicable DiseasesReinfectionBasic Reproduction NumberBreakthrough InfectionsComputer SimulationDisease OutbreaksHumansModels, TheoreticalPandemicsBackward bifurcationsBang-bang controlBasic reproduction numberBreakthrough infectionReinfection

Identifiers

PMID41917107
PMCPMC13180989

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LicenceCC BY-NC-ND
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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.