Dynamical diversity induced by individual responsive immunization

Wu, Qingchu; Liu, Huaxiang; Small, Michael

doi:10.1016/j.physa.2013.02.014

Cited by 8 publications

(15 citation statements)

References 31 publications

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“…[37], Andrews & Bauch [41], Mao & Bian [62], Mao & Yang [63], Wu et al . [163], Miller [177], Guo et al . [180], Shang [189]globalBhattacharyya & Bauch [19,89], Bauch & Bhattacharyya [91], Johnson et al .…”

Section: Resultsmentioning

confidence: 99%

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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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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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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

View full text Add to dashboard Cite

show abstract

“…2(a) and (c), we illustrate the change of the infection density at the steady state I with respect to the infection rate β when k t = 30 and k t = 40. For a standard networked-SIS epidemic model with targeted immunization, there exists a threshold value of degree k c t related to the infection rate β for a given recovery rate [1,16]. Accordingly, we can derive an inverse relation-the epidemic threshold β c = ϕ(k t ) (where ϕ(·) is a function).…”

Section: The Targeted Immunization-based Contact Immunizationmentioning

confidence: 99%

“…Zhang et al [10] studied the impact of individual decision on the epidemic dynamics by using game theory and found that the heterogeneous network can help to control epidemic spread. In our work [16], when the number of infected neighbors of a susceptible node achieves a threshold value, it will become vaccinated. We focus on the impact of vaccination on the epidemic threshold and found that there exist two kinds of critical values of spreading rate to discriminate between dynamical behaviors.…”

Section: Introductionmentioning

confidence: 99%

“…(2) A susceptible node gets vaccinated when the epidemic information amount is larger than a threshold value [7,10,16], and we call this the threshold approach. Goldenberg et al [7] studied the distributed immunization on computer networks where the node informs other nodes in the same immunization cluster to vaccine once a vigilant node contacts one or more infected nodes.…”

Section: Introductionmentioning

confidence: 99%

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