A dynamic vaccination strategy to suppress the recurrent epidemic outbreaks

Chen, Dandan; Zheng, Muhua; Zhao, Ming

doi:10.1016/j.chaos.2018.04.026

Cited by 7 publications

(5 citation statements)

References 41 publications

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“…These studies consider various objectives such as single-objective optimization, access equity maximization, health-care cost minimization (G€ unes et al, 2014;Ndiaye and Alfares, 2008) and multi-objective optimization (Li et al, 2020;Mohammadi et al, 2014;Ng et al, 2018). Some prior research modeled epidemiological dynamics using population game theory (Li et al, 2017;Chen et al, 2018).…”

Section: Vaccine Supply Chainmentioning

confidence: 99%

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Optimal Covid-19 vaccine stations location and allocation strategies

Kumar

Ramane

et al. 2022

BIJ

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PurposeThis study proposes strategies for vaccine center allocation for coronavirus disease (COVID) vaccine by determining the number of vaccination stations required for the vaccination drive, location of vaccination station, assignment of demand group to vaccination station, allocation of the scarce medical professional teams to station and number of optimal days a vaccination station to be functional in a week.Design/methodology/approachThe authors propose a mixed-integer nonlinear programming model. However, to handle nonlinearity, the authors devise a heuristic and then propose a two-stage mixed-integer linear programming (MILP) formulation to optimize the allocation of vaccination centers or stations to demand groups in the first stage and the allocation of vaccination centers to cold storage links in the second stage. The first stage optimizes the cost and average distance traveled by people to reach the vaccination center, whereas the second stage optimizes the vaccine’s holding and storage and transportation cost by efficiently allocating cold storage links to the centers.Findings The model is studied for the real-world case of Chandigarh, India. The results obtained validate that the proposed approach can immensely help government agencies and policymaking body for a successful vaccination drive. The model tries to find a tradeoff between loss due to underutilized medical teams and the distance traveled by a demand group to get the vaccination.Originality/value To the best of our knowledge, there are hardly any studies on a vaccination program at such a scale due to sudden outbreaks such as Covid-19.

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Section: Vaccine Supply Chainmentioning

confidence: 99%

“…Some prior research modeled epidemiological dynamics using population game theory (Li et al. , 2017; Chen et al ., 2018).…”

Section: Literature Reviewmentioning

confidence: 99%

Optimal Covid-19 vaccine stations location and allocation strategies

Kumar

Ramane

et al. 2022

BIJ

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“…light blue) curve as reference (see Figs. 5,7,8). Other colors imply the sequential allocation of resources.…”

Section: B Comparing Online Strategiesmentioning

confidence: 99%

“…Dynamic models for allocating medical resources are subject to wide investigation [7], [17], for which [21] gives a convenient formalism with the introduction of the Dynamic Resource Allocation (DRA), a model for network control, originally developed for SIS-like processes [20] (a node is either infected, or healthy without permanent immunity), that distributes a limited budget of available treatment resources on infected nodes in order to speed-up their recovery. The score-based DRA Contact emails: {fekom, vayatis, kalogeratos}@cmla.ens-cachan.fr.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Epidemic Control via Sequential Resource Allocation

Fekom,

Vayatis,

Kalogeratos

2020

Preprint

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In the Dynamic Resource Allocation (DRA) problem, an administrator has to allocate a limited amount of resources to the nodes of a network in order to reduce a diffusion process (DP) (e.g. an epidemic). In this paper we propose a multiround dynamic control framework, which we realize through two derived models: the Restricted and the Sequential DRA (RDRA, SDRA), that allows for restricted information and access to the entire network, contrary to standard full-information and full-access DRA models. At each intervention round, the administrator has only access -simultaneous for the former, sequential for the latter-to a fraction of the network nodes. This sequential aspect in the decision process offers a completely new perspective to the dynamic DP control, making this work the first to cast the dynamic control problem as a series of sequential selection problems. Through in-depth SIS epidemic simulations we compare the performance of our multi-round approach with other resource allocation strategies and several sequential selection algorithms on both generated, and real-data networks. The results provide evidence about the efficiency and applicability of the proposed framework for real-life problems.

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“…In recent years, pandemics of infectious diseases have become a grim reality for mankind, such as SARS and H1N1 in the early days [1] , and COVID-19 in the latest epidemic The epidemic and outbreak of infectious diseases not only cause huge economic losses to human society, but also pose a great threat to people's life safety [2] .While significant progress has been made in combating various infectious diseases [3] , but most studies have only modeled infection with a single strain [4] .Compared with other social interventions, such as social distancing, vaccination is the most effective way to control the mass spread of disease [5]. These mutated strains may be more transmissible than the original strains, and the effectiveness of vaccines against the original strains may be compromised [6] [7] .…”

Section: Introductionmentioning

confidence: 99%

Study of disease transmission models considering disease mutation and vaccination on complex network

Shaorui,

Yunxiao,

Xiangyu

2024

Fourth International Conference on Applied Mathematics, Modelling, and Intelligent Computing (CAMMIC 2024)

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Vaccination is the most effective way to control the spread of disease. However, during the spread of the disease, gene mutations may occur due to external environmental stimuli, resulting in different mutated strains. These mutated strains may be more transmissible than the original strains, and the effectiveness of vaccines against the original strain may also be affected. At the same time, after a longer period of vaccination, the concentration of antibodies in the body will also decrease with the increase of time. The prevention effect of diseases that cannot be lifelong immunity will also be reduced over time. So even though vaccination has been shown to be the most effective way to curb the growth of the disease, this effectiveness has been challenged by issues including vaccine efficacy and strain variation. This paper considers the emergence of mutated strains during transmission and the variation of vaccine effectiveness over time. At the same time, people's vaccination decisions will be based on the comparison of vaccination cost and infection cost, their own risk of infection and vaccine effectiveness and other factors. Based on the above factors, this paper established an improved S 0 I 1 I 2 VS R disease transmission model based on SIS Model and carried out numerical simulation on the ER network. The study found that encouraging re-vaccination was more effective in controlling the spread of the disease than not vaccinating and vaccinating once. Reducing the cost of vaccination can also reduce the scale of infections.

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A dynamic vaccination strategy to suppress the recurrent epidemic outbreaks

Cited by 7 publications

References 41 publications

Optimal Covid-19 vaccine stations location and allocation strategies

Optimal Covid-19 vaccine stations location and allocation strategies

Dynamic Epidemic Control via Sequential Resource Allocation

Study of disease transmission models considering disease mutation and vaccination on complex network

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