ArticleEuropean physical journal plus2021
Application of reinforcement learning for effective vaccination strategies of coronavirus disease 2019 (COVID-19).
Article in European physical journal plus, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Numerical modeling and simulation of stochastic fractional order model for COVID-19 infection in Mittag-Leffler kernel.Scientific reports · 2025Article
- Integrating artificial intelligence with mechanistic epidemiological modeling: a scoping review of opportunities and challenges.Nature communications · 2025Article
- HRL4EC: Hierarchical reinforcement learning for multi-mode epidemic control.Information sciences · 2023Article
- A fractional-order model with different strains of COVID-19.Physica A · 2022Article
- Optimal control and cost-effectiveness analysis for a COVID-19 model with individual protection awareness.Physica A · 2022Article
- Modeling and optimal control of mutated COVID-19 (Delta strain) with imperfect vaccination.Chaos, solitons, and fractals · 2022Article
- Effect of an antiviral drug control and its variable order fractional network in host COVID-19 kinetics.The European physical journal. Special topics · 2022Article
- Mathematical assessment of constant and time-dependent control measures on the dynamics of the novel coronavirus: An application of optimal control theory.Results in physics · 2021Article
- Mathematical assessment of the dynamics of novel coronavirus infection with treatment: A fractional study.Chaos, solitons, and fractals · 2021Article
- A dynamical study of SARS-COV-2: A study of third wave.Results in physics · 2021Article
- Impact of environmental transmission and contact rates on Covid-19 dynamics: A simulation study.Informatics in medicine unlocked · 2021Article
- Qualitative analysis of a discrete-time phytoplankton-zooplankton model with Holling type-II response and toxicity.Advances in difference equations · 2021Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Since December 2019, the new coronavirus has raged in China and subsequently all over the world. From the first days, researchers have tried to discover vaccines to combat the epidemic. Several vaccines are now available as a result of the contributions of those researchers. As a matter of fact, the available vaccines should be used in effective and efficient manners to put the pandemic to an end. Hence, a major problem now is how to efficiently distribute these available vaccines among various components of the population. Using mathematical modeling and reinforcement learning control approaches, the present article aims to address this issue. To this end, a deterministic Susceptible-Exposed-Infectious-Recovered-type model with additional vaccine components is proposed. The proposed mathematical model can be used to simulate the consequences of vaccination policies. Then, the suppression of the outbreak is taken to account. The main objective is to reduce the effects of Covid-19 and its domino effects which stem from its spreading and progression. Therefore, to reach optimal policies, reinforcement learning optimal control is implemented, and four different optimal strategies are extracted. Demonstrating the efficacy of the proposed methods, finally, numerical simulations are presented.
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Registered trials
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