The effect of risk-taking behaviour in epidemic models

Sega, Laurentiu; Maxin, Daniel; Eaton, Lisa A.; Latham, Alfred; Moose, A.; Stenslie, S.

doi:10.1080/17513758.2015.1065351

Cited by 7 publications

(6 citation statements)

References 29 publications

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“…[170]Reluga [11], Liu & Stechlinski [171], Samanta & Chattopadhyay [172], Sega et al . [173]Hatzopoulos et al . [174], Sahneh & Scoglio [175], Sahneh et al .…”

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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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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“…[170]Reluga [11], Liu & Stechlinski [171], Samanta & Chattopadhyay [172], Sega et al . [173]Hatzopoulos et al . [174], Sahneh & Scoglio [175], Sahneh et al .…”

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

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“…For example, the fact that r 1 increases for q > q 2 suggests that the bi-stability interval would actually increase with higher rates of quarantine. Such an unexpected and counter-intuitive effect can happen under other circumstances such as when risk-taking behavior in response to treatment and/or vaccination is taken into account (see Sega et al, 2015;Maxin et al, 2016). However, this is not the case for the model we analyze here.…”

Section: The Stability Of the Endemic Equilibriamentioning

confidence: 89%

The Influence of Quarantine Before Obtaining a Partially Effective Preventive Measure

2023

LiB

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We study an epidemic model for a generic infectious disease with an ongoing spread in a closed community. The disease is assumed to not cause additional mortality and without providing immunity. We also assume the availability of a preventive measure that is both scarce and only partially eective in reducing the infection risk. We analyze the model focusing on the eect of a class of susceptibles that chooses to quarantine itself from the epidemic while waiting for the preventive measure to be available. Of particular interest is the case when the model exhibits bi-stability between the disease-free equilibrium and an endemic state which indicates that the disease may persist even if the epidemic reproductive number is less than one. We investigate the conditions whereby increasing the quarantine rate eliminates the bistability scenario thereby improving the predictive value of the model when assessing whether the disease evolves toward an endemic state or not.

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“…The existing research on the factors of epidemic prevention and control mainly includes epidemic publicity, isolation, social distance and wearing masks. Some scholars pointed out that publicizing knowledge about epidemic prevention and control can improve public awareness of prevention and control and strengthen public prevention and control behaviors (Sega et al, 2015;Du et al, 2021). Other scholars pointed out that isolation and social distancing are effective means of epidemic prevention and control during the epidemic.…”

Section: Literature Reviewmentioning

confidence: 99%

The stochastic evolutionary game analysis of public prevention and control strategies in public health emergencies

Jia

Wang

2022

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PurposeThe COVID-19 epidemic is still spreading globally and will not be completely over in a short time. Wearing a mask is an effective means to combat the spread of COVID-19. However, whether the public wear a mask for epidemic prevention and control will be affected by stochastic factors such as vaccination, cultural differences and irrational emotions, which bring a high degree of uncertainty to the prevention and control of the epidemic. The purpose of this study is to explore and analyze the epidemic prevention and control strategies of the public in an uncertain environment.Design/methodology/approachBased on the stochastic evolutionary game model of the Moran process, the study discusses the epidemic prevention and control strategies of the public under the conditions of the dominance of stochastic factors, expected benefits and super-expected benefits.FindingsThe research shows that the strategic evolution of the public mainly depends on stochastic factors, cost-benefit and the number of the public. When the stochastic factors are dominant, the greater the perceived benefit, the lower the cost and the greater the penalty for not wearing masks, the public will choose to wear a mask. Under the dominance of expected benefits and super-expected benefits, when the number of the public is greater than a certain threshold, the mask-wearing strategy will become an evolutionary stable strategy. From the evolutionary process, the government’s punishment measures will slow down the speed of the public choosing the strategy of not wearing masks. The speed of the public evolving to the stable strategy under the dominance of super-expected benefits is faster than that under the dominance of expected benefits.Practical implicationsThe study considers the impact of stochastic factors on public prevention and control strategies and provides decision-making support and theoretical guidance for the scientific prevention of the normalized public.Originality/valueTo the best of the authors’ knowledge, no research has considered the impact of different stochastic interference intensities on public prevention and control strategies. Therefore, this paper can be seen as a valuable resource in this field.

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The effect of risk-taking behaviour in epidemic models

Cited by 7 publications

References 29 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)

The Influence of Quarantine Before Obtaining a Partially Effective Preventive Measure

The stochastic evolutionary game analysis of public prevention and control strategies in public health emergencies

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