Decentralized resource allocation to control an epidemic: A game theoretic approach

Wang, Shouqiang; Véricourt, Francis de; Sun, Peng

doi:10.1016/j.mbs.2009.08.002

Cited by 21 publications

(23 citation statements)

References 9 publications

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“…In terms of the resource strategy game, Wang et al [311] examined how two countries would allocate resources at the onset of an epidemic when they seek to protect their own populations. They build a two-region SIR model in which infected individuals can transmit between the two regions (countries).…”

Section: Effects Of Global Resourcesmentioning

confidence: 99%

Coevolution spreading in complex networks

et al. 2019

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a b s t r a c tThe propagations of diseases, behaviors and information in real systems are rarely independent of each other, but they are coevolving with strong interactions. To uncover the dynamical mechanisms, the evolving spatiotemporal patterns and critical phenomena of networked coevolution spreading are extremely important, which provide theoretical foundations for us to control epidemic spreading, predict collective behaviors in social systems, and so on. The coevolution spreading dynamics in complex networks has thus attracted much attention in many disciplines. In this review, we introduce recent progress in the study of coevolution spreading dynamics, emphasizing the contributions from the perspectives of statistical mechanics and network science. The theoretical methods, critical phenomena, phase transitions, interacting mechanisms, and effects of network topology for four representative types of coevolution spreading mechanisms, including the coevolution of biological contagions, social contagions, epidemic-awareness, and epidemic-resources, are presented in detail, and the challenges in this field as well as open issues for future studies are also discussed.

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Section: Effects Of Global Resourcesmentioning

confidence: 99%

Coevolution spreading in complex networks

et al. 2019

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“…Duijzer, Van Jaarsveld, Wallinga, & Dekker, 2016;Keeling & Shattock, 2012;. To evaluate the effects of different vaccination strategies we focus on minimizing the final size, i.e., the proportion of people infected during the outbreak (e.g., Keeling & Shattock, 2012;Lee, Yuan, Pietz, Benecke, & Burel, 2015;Wang, de Véricourt, & Sun, 2009;Wu et al, 2007). An alternative performance criterion is the reproduction ratio R, which is related to the initial growth of infections (Diekmann, Heesterbeek, & Britton, 2013).…”

Section: Timing Of Vaccinationmentioning

confidence: 99%

The benefits of combining early aspecific vaccination with later specific vaccination

Duijzer

Jaarsveld

Dekker

2018

European Journal of Operational Research

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Timing is of crucial importance for successful vaccination. To avoid a large outbreak, vaccines are administered preferably as quickly as possible. However, in the early stages of an outbreak the information on the disease is limited and waiting with the intervention allows to design a more tailored vaccination strategy. In this paper we study the resulting tradeoff between timing of vaccination and the effectiveness of the response.We model disease progression using the seminal SIR model, and consider a decision maker who allocates her budget over two vaccine types: an early aspecific vaccine and a later specific vaccine. We analytically characterize the switching curve separating the parameter space region where the late specific vaccine is preferred from the region where the early aspecific type is preferred. More importantly, weshow that the decision maker should not only consider pure strategies, i.e., strategies which spend the entire budget on one of the types. Instead, she should suitably invest in both vaccine types to benefit both from the early response and from the good vaccine. We prove that at the switching curve, such a hybrid strategy is strictly better than either of the pure strategies due to the non-linear dynamics of epidemics. Numerical experiments show that the associated benefit of hybrid strategies over pure strategies in terms of reduction of the number of infections may be more than 50%. Such experiments also substantiate our restriction to two vaccine types.

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“…They combine an epidemic transmission model and a game-theoretic setup to examine drug allocations among participating countries. Wang et al (2009) study a game between selfish countries that each allocate resources at the onset of an epidemic to minimize the total number of infected citizens. They show that selfish countries will allocate resources to lower the disease reproductive ratio below a threshold in order to avoid a major outbreak.…”

Section: Relevant Literaturementioning

confidence: 99%

Hospital stockpiling for disaster planning

Adida

DeLaurentis

Lawley³

2011

IIE Transactions

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In response to the increasing threat of terrorist attacks and natural disasters, governmental and private organizations worldwide have invested significant resources in disaster planning activities. This article addresses joint inventory stockpiling of medical supplies for groups of hospitals prior to a disaster. Specifically, the problem of determining the stockpile quantity of a medical item at several hospitals is considered. It is assumed that demand is uncertain and driven by the characteristics of a variety of disaster scenarios. Furthermore, it is assumed that hospitals have mutual aid agreements for inventory sharing in the event of a disaster. Each hospital's desire to minimize its stockpiling cost together with the potential to borrow from other stockpiles creates individual incentives well represented in a game-theoretic framework. This problem is modeled as a non-cooperative strategic game, the existence of a Nash equilibrium is proved, and the equilibrium solutions are analyzed. A centralized model of stockpile decision making where a central decision maker optimizes the entire system is also examined and the solutions obtained using this model are compared to those of the decentralized (game) model. The comparison provides some managerial insights and public health policy implications valuable for disaster planning.

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Decentralized resource allocation to control an epidemic: A game theoretic approach

Cited by 21 publications

References 9 publications

Coevolution spreading in complex networks

Coevolution spreading in complex networks

The benefits of combining early aspecific vaccination with later specific vaccination

Hospital stockpiling for disaster planning

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