Interplay between the local information based behavioral responses and the epidemic spreading in complex networks

We review behavioural change models (BCMs) for infectious disease transmission in humans. Following the Cochrane collaboration guidelines and the PRISMA statement, our systematic search and selection yielded 178 papers covering the period 2010–2015. We observe an increasing trend in published BCMs, frequently coupled to (re)emergence events, and propose a categorization by distinguishing how information translates into preventive actions. Behaviour is usually captured by introducing information as a dynamic parameter (76/178) or by introducing an economic objective function, either with (26/178) or without (37/178) imitation. Approaches using information thresholds (29/178) and exogenous behaviour formation (16/178) are also popular. We further classify according to disease, prevention measure, transmission model (with 81/178 population, 6/178 metapopulation and 91/178 individual-level models) and the way prevention impacts transmission. We highlight the minority (15%) of studies that use any real-life data for parametrization or validation and note that BCMs increasingly use social media data and generally incorporate multiple sources of information (16/178), multiple types of information (17/178) or both (9/178). We conclude that individual-level models are increasingly used and useful to model behaviour changes. Despite recent advancements, we remain concerned that most models are purely theoretical and lack representative data and a validation process.

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“…[181], Liu et al . [182], Chen et al [183]Goyal & Vigier [179], Kitchovitch and Lio [184,185], Ni et al . [186], Rutherford et al .…”

Section: Resultsmentioning

confidence: 99%

“…We retrieved 29 BCMs in which behaviour change is modelled conditional on exceeding a predefined information threshold [12,42,57,58,61–63,70,78,81,88,114,127,132,133,135,136,138–144,162,163,180–182]. The information the individual assesses can be obtained in a direct way (e.g.…”

Section: Resultsmentioning

confidence: 99%

Behavioural change models for infectious disease transmission: a systematic review (2010–2015)

Verelst

Willem

Beutels

2016

J. R. Soc. Interface.

340

311

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“…As the same with BA model, this partial differential equation has the initial and boundary conditions (7). Solving (16), and substituting (7) into the general solution, we get If we ignore the initial m 0 nodes, the degree distribution is…”

Section: 2mentioning

confidence: 99%

“…Regarding the spread of epidemic, for example, different network topologies often have different threshold and propagation behavior. The main existing epidemic models are nodebased model [22,26,16], pairwise models [11,12], effective degree models [15], and edge-based models [20,19,18,24]. Although these models have different styles, they all divide the nodes and edges into different classes according to the node degree.…”

mentioning

confidence: 99%

Structural calculations and propagation modeling of growing networks based on continuous degree

Jia

Jin

Chang

et al. 2017

MBE

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When a network reaches a certain size, its node degree can be considered as a continuous variable, which we will call continuous degree. Using continuous degree method (CDM), we analytically calculate certain structure of the network and study the spread of epidemics on a growing network. Firstly, using CDM we calculate the degree distributions of three different growing models, which are the BA growing model, the preferential attachment accelerating growing model and the random attachment growing model. We obtain the evolution equation for the cumulative distribution function F(k,t), and then obtain analytical results about F(k,t) and the degree distribution p(k,t). Secondly, we calculate the joint degree distribution p(k1,k2,t) of the BA model by using the same method, thereby obtain the conditional degree distribution p(k1|k2). We find that the BA model has no degree correlations. Finally, we consider the different states, susceptible and infected, according to the node health status. We establish the continuous degree SIS model on a static network and a growing network, respectively. We find that, in the case of growth, the new added health nodes can slightly reduce the ratio of infected nodes, but the final infected ratio will gradually tend to the final infected ratio of SIS model on static networks.

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“…Different factors [ 13 – 15 ], such as the frequency of contact between individuals, the disease duration, and the immunity of particular individuals, have been considered in these models to provide a realistic and comprehensive understanding of the spreading process. In particular, as an important representation of the spread of information about epidemics, awareness spreading [ 16 – 20 ] has attracted increasing interest. A pioneering step in this direction was taken by Funk et al [ 16 ], who studied the spreading of epidemics while accounting for the spread of awareness.…”

Section: Introductionmentioning

confidence: 99%

The Role of Node Heterogeneity in the Coupled Spreading of Epidemics and Awareness

et al. 2016

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Exploring the interplay between information spreading and epidemic spreading is a topic that has been receiving increasing attention. As an efficient means of depicting the spreading of information, which manifests as a cascade phenomenon, awareness cascading is utilized to investigate this coupled transmission. Because in reality, different individuals facing the same epidemic will exhibit distinct behaviors according to their own experiences and attributes, it is important for us to consider the heterogeneity of individuals. Consequently, we propose a heterogeneous spreading model. To describe the heterogeneity, two of the most important but radically different methods for this purpose, the degree and k-core measures, are studied in this paper through three models based on different assumptions. Adopting a Markov chain approach, we succeed in predicting the epidemic threshold trend. Furthermore, we find that when the k-core measure is used to classify individuals, the spreading process is robust to these models, meaning that regardless of the model used, the spreading process is nearly identical at the macroscopic level. In addition, the k-core measure leads to a much larger final epidemic size than the degree measure. These results are cross-checked through numerous simulations, not only of a synthetic network but also of a real multiplex network. The presented findings provide a better understanding of k-core individuals and reveal the importance of considering network structure when investigating various dynamic processes.

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Interplay between the local information based behavioral responses and the epidemic spreading in complex networks

Cited by 19 publications

References 37 publications

Behavioural change models for infectious disease transmission: a systematic review (2010–2015)

Behavioural change models for infectious disease transmission: a systematic review (2010–2015)

Structural calculations and propagation modeling of growing networks based on continuous degree

The Role of Node Heterogeneity in the Coupled Spreading of Epidemics and Awareness

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