EoN (Epidemics on Networks): a fast, flexible Python package for simulation, analytic approximation, and analysis of epidemics on networks

Miller, Joel C.; TIng, Tony

doi:10.21105/joss.01731

Cited by 48 publications

(40 citation statements)

References 19 publications

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“…For each network, a fraction f = 0.1, 0.2, and 0.3 of the nodes identified with the FN and JN strategies are removed from the original network. SIR simulations are performed on the six reduced networks using the EoN (EpidemicsOnNetworks) model 34,35 . The simulations use β = 1.5, γ = 1, and p = 0.01, where β is the transmission rate per edge, γ is the recovery rate per node and p is the ratio of the number of initially infected nodes to all of the nodes in the network.…”

Section: Sir Simulation Resultsmentioning

confidence: 99%

Hunting for Influential Nodes in Complex Networks Using Local Information

Dong

Chen

Tricco

et al. 2021

Preprint

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Complex networks in the real world are often with heterogeneous degree distributions. The structure and function of nodes can vary significantly, with influential nodes playing a crucial role in information spread and other spreading phenomena. Identifying high-degree nodes enables change to the network’s structure and function. Previous work either redefines metrics used to measure the nodes’ importance or focus on developing algorithms to efficiently find influential nodes. These approaches typically rely on global knowledge of the network and assume that the structure of the network does not change over time, both of which are difficult to achieve in the real world. In this paper, we propose a decentralized strategy that can find influential nodes without global knowledge of the network. Our Joint Nomination (JN) strategy selects a random set of nodes along with a set of nodes connected to those nodes, and together they nominate the influential node set. Experiments are conducted on 12 network datasets, including both synthetic and real-world networks, both undirected and directed networks. Results show that average degree of the identified node set is about 3–8 times higher than that of the full node set, and the degree distribution skews toward higher-degree nodes. Removal of influential nodes increase the average shortest path length by 20–70% over the original network, or about 8–15% longer than the other decentralized strategies. Immunization based on JN is more efficient than other strategies, consuming around 12–40% less immunization resources to raise the epidemic threshold to 𝜏 ~ 0:1. Susceptible-Infected-Recovered (SIR) simulations on networks with 30% influential nodes removed using JN delays the arrival time of infection peak significantly and reduce the total infection scale to 15%.

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Section: Sir Simulation Resultsmentioning

confidence: 99%

Hunting for Influential Nodes in Complex Networks Using Local Information

Dong

Chen

Tricco

et al. 2021

Preprint

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“…To compare and contrast information flow in the quoter model, we also simulate traditional models of information flow, specifically simple and complex contagion. For simple contagion we simulate a stochastic SIR model on different networks (1000-node Erdős-Rényi and Barabási-Albert networks, as well as a sample of real-world networks) using [ 31 ]. For the simulations here we set the transmission rate 20 and recovery rate 1.…”

Section: Methodsmentioning

confidence: 99%

Complex Contagion Features without Social Reinforcement in a Model of Social Information Flow

Pond

Magsarjav

South

et al. 2020

Entropy

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Contagion models are a primary lens through which we understand the spread of information over social networks. However, simple contagion models cannot reproduce the complex features observed in real-world data, leading to research on more complicated complex contagion models. A noted feature of complex contagion is social reinforcement that individuals require multiple exposures to information before they begin to spread it themselves. Here we show that the quoter model, a model of the social flow of written information over a network, displays features of complex contagion, including the weakness of long ties and that increased density inhibits rather than promotes information flow. Interestingly, the quoter model exhibits these features despite having no explicit social reinforcement mechanism, unlike complex contagion models. Our results highlight the need to complement contagion models with an information-theoretic view of information spreading to better understand how network properties affect information flow and what are the most necessary ingredients when modeling social behavior.

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“…The main focus of this paper is the effectiveness of disease spreaders on contact networks. The performance of spreaders is analysed using susceptible-infected (SI) simulations, see Miller and Ting (2020), where edge weight affects the probability of transmission, with the time to infection exponentially distributed as described by Kiss et al (2017). This paper is primarily concerned with the influence of a node on the spread of disease, therefore susceptible-infected-susceptible (SIS) and susceptibleinfected-recovered (SIR) models are not considered to reduce the effect of stochastic resusceptibility and recovery events.…”

Section: Susceptible-infected (Si) Simulationsmentioning

confidence: 99%

“…For simulating the spread of disease through a network, exponentially distributed times to infection [as in Miller and Ting (2020)] are commonly assumed but contact network analysis frequently uses contact times to weight the adjacency matrix, such as in Salathé et al (2010). In this work, the adjacency matrix is altered to reflect the exponential relationship between contact time and risk of disease spread, with the aim of improving the identification of effective spreaders of disease.…”

Section: Exponentially Distributed Contact Networkmentioning

confidence: 99%

“…where s ∈ S . For each value of s, a spreader selection is generated and all unique selections tested with SI simulations described in Miller and Ting (2020). While the set of S provides effective selections for most of the topologies tested, the results shall highlight certain artificial topologies for which a smaller range of values, such as S = {0.001, 0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, 0.1} , produces superior selections.…”

Section: Optimising Spreader Selectionmentioning

confidence: 99%

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Identification of effective spreaders in contact networks using dynamical influence

Clark

Macdonald

2021

Appl Netw Sci

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Contact networks provide insights on disease spread due to the duration of close proximity interactions. For systems governed by consensus dynamics, network structure is key to optimising the spread of information. For disease spread over contact networks, the structure would be expected to be similarly influential. However, metrics that are essentially agnostic to the network’s structure, such as weighted degree (strength) centrality and its variants, perform near-optimally in selecting effective spreaders. These degree-based metrics outperform eigenvector centrality, despite disease spread over a network being a random walk process. This paper improves eigenvector-based spreader selection by introducing the non-linear relationship between contact time and the probability of disease transmission into the assessment of network dynamics. This approximation of disease spread dynamics is achieved by altering the Laplacian matrix, which in turn highlights why nodes with a high degree are such influential disease spreaders. From this approach, a trichotomy emerges on the definition of an effective spreader where, for susceptible-infected simulations, eigenvector-based selections can either optimise the initial rate of infection, the average rate of infection, or produce the fastest time to full infection of the network. Simulated and real-world human contact networks are examined, with insights also drawn on the effective adaptation of ant colony contact networks to reduce pathogen spread and protect the queen ant.

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EoN (Epidemics on Networks): a fast, flexible Python package for simulation, analytic approximation, and analysis of epidemics on networks

Cited by 48 publications

References 19 publications

Hunting for Influential Nodes in Complex Networks Using Local Information

Hunting for Influential Nodes in Complex Networks Using Local Information

Complex Contagion Features without Social Reinforcement in a Model of Social Information Flow

Identification of effective spreaders in contact networks using dynamical influence

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