Nonmassive immunization to contain spreading on complex networks

Costa, Guilherme S.; Ferreira, Silvio C.

doi:10.1103/physreve.101.022311

Cited by 25 publications

(16 citation statements)

References 60 publications

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“…The aim was to reduce the socio-economic costs while taking into account constraints such as limited hospital capacity. The work in [32] constructs an optimal non-massive immunization strategy to contain SIS epidemic on complex networks, however, without employing the theory of optimal control.…”

Section: Related Workmentioning

confidence: 99%

Mitigating biological epidemic on heterogeneous social networks

Devendra

Kandhway

2022

Results in Control and Optimization

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Recent Covid-19 pandemic has demonstrated the need of efficient epidemic outbreak management. We study the optimal control problem of minimizing the fraction of infected population by applying vaccination and treatment control strategies, while at the same time minimizing the cost of implementing them. We model the epidemic using the degree based Susceptible-Infected-Recovered (SIR) compartmental model. We study the impact of varying network topologies on the optimal epidemic management strategies and present results for the Erdős-Rényi, scale free, and real world networks. For efficient computational modeling we form groups of groups of degree classes, and apply separate vaccination and treatment control signals to each group. This allows us to identify the degree classes that play a significant role in mitigating the epidemic for a given network topology. We compare the optimal control strategy with non optimal strategies (constant control and no control) and study the effect of various model parameters on the system. We identify which strategy (vaccination/treatment) plays a significant role in controlling the epidemic on different network topologies. We also study the effect of the cost of vaccination and treatment controls on the resource allocation. We find that the optimal strategy achieves significant improvements over the non optimal heuristics for all networks studied in this paper. Our results may be of interest to governments and healthcare authorities for devising effective vaccination and treatment campaigns during an epidemic outbreak.

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Section: Related Workmentioning

confidence: 99%

Mitigating biological epidemic on heterogeneous social networks

Devendra

Kandhway

2022

Results in Control and Optimization

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“…To identify an effective vaccination strategy, some studies have adopted a network science perspective, combining a network model of the connected community and an epidemic model [7,8,9,10], to provide a convenient framework to understand the impact of topological properties on the progression behavior of the epidemic. In particular, global and local network information can be incorporated in the derivation of effective vaccination strategies [11,12,13,14,15,16,17]. For global network information-based vaccination strategies, the network topologies are used by policymakers for developing vaccination strategies.…”

Section: Introductionmentioning

confidence: 99%

Who Should Get Vaccinated First? An Effective Network Information-Driven Priority Vaccination Strategy

et al. 2021

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Approval of emergency use of the Novel Coronavirus Disease 2019 vaccines in many countries has brought hope to ending the COVID-19 pandemic sooner. Considering the limited vaccine supply in the early stage of COVID-19 vaccination programs in most countries, a highly relevant question to ask is: who should get vaccinated first? In this article we propose a network informationdriven vaccination strategy where a small number of people in a network (population) are categorized, according to a few key network properties, into priority groups. Using a network-based SEIR model for simulating the pandemic progression, the network information-driven vaccination strategy is compared with a random vaccination strategy. Results for both large-scale synthesized networks and real social networks have demonstrated that the network information-driven vaccination strategy can significantly reduce the cumulative number of infected individuals and lead to a more rapid containment of the pandemic. The results provide insight for policymakers in designing an effective early-stage vaccination plan.✩ This work is the results of a research project funded by City University of Hong Kong (Project Number 9229031).

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Section: Introductionmentioning

confidence: 99%

Who Should Get Vaccinated First? An effective Network Information-driven Priority Vaccination Strategy

Liu

Tse

Chan

et al. 2021

Preprint

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Approval of emergency use of the Novel Coronavirus Disease 2019 (COVID-19) vaccines in many countries has brought hope to ending the COVID-19 pandemic sooner. Considering the limited vaccine supply in the early stage of COVID-19 vaccination programs in most countries, a highly relevant question to ask is: who should get vaccinated first? In this article we propose a network information- driven vaccination strategy where a small number of people in a network (population) are categorized, according to a few key network properties, into priority groups. Using a network-based SEIR model for simulating the pandemic progression, the network information-driven vaccination strategy is compared with a random vaccination strategy. Results for both large-scale synthesized networks and real social networks have demonstrated that the network information-driven vaccination strategy can significantly reduce the cumulative number of infected individuals and lead to a more rapid containment of the pandemic. The results provide insight for policymakers in designing an effective early-stage vaccination plan.

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Nonmassive immunization to contain spreading on complex networks

Cited by 25 publications

References 60 publications

Mitigating biological epidemic on heterogeneous social networks

Mitigating biological epidemic on heterogeneous social networks

Who Should Get Vaccinated First? An Effective Network Information-Driven Priority Vaccination Strategy

Who Should Get Vaccinated First? An effective Network Information-driven Priority Vaccination Strategy

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