An SIS epidemic model with vaccination in a dynamical contact network of mobile individuals with heterogeneous spatial constraints

Peng, Xiao-Long; Zhang, Ze-Qiong; Yang, Junyuan; Jin, Zhen

doi:10.1016/j.cnsns.2019.02.004

Cited by 23 publications

(15 citation statements)

References 76 publications

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“…Equation 8 highlights a nontrivial relationship between the expected degree of an agent and its radius of interaction, which is due to the links passively received by the agent when it is located within the radii of interaction of other agents. This relationship is different from the case of directed interactions analyzed in Huang et al (2016) ; Peng et al (2019) , where E [ k i ] is proportional to .…”

Section: Temporal Network Of Contactsmentioning

confidence: 77%

“…However, those advantageous features are accompanied by some drawbacks, including the need of mobility data and models, the use of massive computational resources when the system size scales up, and the lack of analytical techniques for model characterizations. A viable approach to agent-based modeling is based on two-dimensional representations, where agents move and interact according to proximity criteria (Frasca et al 2006 ; Frasca et al 2008 ; Zhou and Liu 2009 ; Buscarino et al 2010 ; Yang et al 2012 ; Buscarino et al 2014 ; Huang et al 2016 ; Peng et al 2019 ). As a first approximation, the motion of the agents can be described according to a random walk with sporadic long range jumps ( Frasca et al 2006 ).…”

Section: Introductionmentioning

confidence: 99%

“…As a first approximation, the motion of the agents can be described according to a random walk with sporadic long range jumps ( Frasca et al 2006 ). Building on this approximation, it is possible to include realistic features such as nonhomogeneous infection rates ( Buscarino et al 2014 ) and heterogeneous radii of interaction ( Huang et al 2016 ; Peng et al 2019 ). Much work is needed, however, to fully capture and describe realistic patterns of human mobility, which are shaped by the complex structure of urban environments ( Alessandretti et al 2017 ).…”

Section: Introductionmentioning

confidence: 99%

“…Here, we contribute to the field of agent-based modeling by presenting a two-dimensional model that is capable of reproducing a spatially inhomogeneous urban-like environment, in which a heterogeneous population follows realistic rules of mobility. Inspired by previous theoretical studies ( Huang et al 2016 ; Peng et al 2019 ), we assume that agents have a heterogeneous radius of interaction, which accounts for variations among individuals in their involvement in social behavior and activities.…”

Section: Introductionmentioning

confidence: 99%

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A multi-agent model to study epidemic spreading and vaccination strategies in an urban-like environment

et al. 2020

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Worldwide urbanization calls for a deeper understanding of epidemic spreading within urban environments. Here, we tackle this problem through an agent-based model, in which agents move in a two-dimensional physical space and interact according to proximity criteria. The planar space comprises several locations, which represent bounded regions of the urban space. Based on empirical evidence, we consider locations of different density and place them in a core-periphery structure, with higher density in the central areas and lower density in the peripheral ones. Each agent is assigned to a base location, which represents where their home is. Through analytical tools and numerical techniques, we study the formation mechanism of the network of contacts, which is characterized by the emergence of heterogeneous interaction patterns. We put forward an extensive simulation campaign to analyze the onset and evolution of contagious diseases spreading in the urban environment. Interestingly, we find that, in the presence of a core-periphery structure, the diffusion of the disease is not affected by the time agents spend inside their base location before leaving it, but it is influenced by their motion outside their base location: a strong tendency to return to the base location favors the spreading of the disease. A simplified one-dimensional version of the model is examined to gain analytical insight into the spreading process and support our numerical findings. Finally, we investigate the effectiveness of vaccination campaigns, supporting the intuition that vaccination in central and dense areas should be prioritized.

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Section: Temporal Network Of Contactsmentioning

confidence: 77%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A multi-agent model to study epidemic spreading and vaccination strategies in an urban-like environment

et al. 2020

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“…We will follow the works of [30,35,36] in defining an epidemic model with spatially distributed agents. We consider a spatial network in which N individuals (nodes) are distributed uniformly and randomly in a square patch of length L with density ρ.…”

Section: Sir Dynamics On Evolving Random Geometric Graphsmentioning

confidence: 99%

Social adaptive behavior and oscillatory prevalence in an epidemic model on evolving random geometric graphs

Panicker¹,

Sasidevan²

2022

Preprint

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Our recent experience with COVID19 amply shows that spatial effects like mobility and average interpersonal distance are very important in deciding the outcome of an epidemic dynamics. Spatial connectivity structure of a population is usually modelled via Random Geometric Graphs which are networks generated from spatially distributed components where connections between components are made based on a distance-dependent probability measure. Structural and dynamical aspects of such graphs are important in describing processes with a spatial dependence such as the spread of an airborne disease. In this work, using a simple computational model of an epidemic, we investigate how spatial factors like average separation between individuals and their mobility affect the spread of a disease. We show that such spatial factors can give rise to oscillatory prevalence in a society of adaptive individuals. We also show that delays in executing non-pharmaceutical spatial mitigation strategies can accentuate oscillatory prevalence and can have non-monotonic effects on the peak prevalence. In both cases, we characterize the effects of different parameters on the prevalence of the disease and peak infection and obtain threshold values.

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Optimization methods for large-scale vaccine supply chains: a rapid review

et al. 2022

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Global vaccine revenues are projected at $59.2 billion, yet large-scale vaccine distribution remains challenging for many diseases in countries around the world. Poor management of the vaccine supply chain can lead to a disease outbreak, or at worst, a pandemic. Fortunately, a large number of those challenges, such as decision-making for optimal allocation of resources, vaccination strategy, inventory management, among others, can be improved through optimization approaches. This work aims to understand how optimization has been applied to vaccine supply chain and logistics. To achieve this, we conducted a rapid review and searched for peer-reviewed journal articles, published between 2009 and March 2020, in four scientific databases. The search resulted in 345 articles, of which 25 unique studies met our inclusion criteria. Our analysis focused on the identification of article characteristics such as research objectives, vaccine supply chain stage addressed, the optimization method used, whether outbreak scenarios were considered, among others. Approximately 64% of the studies dealt with vaccination strategy, and the remainder dealt with logistics and inventory management. Only one addressed market competition (4%). There were 14 different types of optimization methods used, but control theory, linear programming, mathematical model and mixed integer programming were the most common (12% each). Uncertainties were considered in the models of 44% of the studies. One resulting observation was the lack of studies using optimization for vaccine inventory management and logistics. The results provide an understanding of how optimization models have been used to address challenges in large-scale vaccine supply chains.

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An SIS epidemic model with vaccination in a dynamical contact network of mobile individuals with heterogeneous spatial constraints

Abstract: Network-based epidemic models have been extensively employed to understand the spread of infectious diseases, but have generally overlooked the fact that most realistic networks are dynamical rather than static. In this paper, we

Cited by 23 publications

References 76 publications

A multi-agent model to study epidemic spreading and vaccination strategies in an urban-like environment

A multi-agent model to study epidemic spreading and vaccination strategies in an urban-like environment

Social adaptive behavior and oscillatory prevalence in an epidemic model on evolving random geometric graphs

Optimization methods for large-scale vaccine supply chains: a rapid review

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