Spontaneous behavioural changes in response to epidemics

Poletti, Piero; Caprile, Bruno; Ajelli, Marco; Pugliese, Andrea; Merler, Stefano

doi:10.1016/j.jtbi.2009.04.029

Cited by 157 publications

(151 citation statements)

References 27 publications

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“…Extra compartments signify behavioral heterogeneities amongst members of a population, and the disease transmission rates associated with them also vary. Reduction in transmission due to adaptive behavior is either modelled as a quarantine of cases [64,65,63], or prophylactic behavior of susceptible individuals due to increased awareness of the disease [75,66,69,70,71,72,73,74,77]. These models agree that early activation of isolation measures and self-protective behavior can weaken an epidemic.…”

Section: Rule-based Modelsmentioning

confidence: 89%

“…These models often utilize additional compartments, which are populated according to specific rules. Examples of such rules are to construct the compartments to hold a constant amount of individuals associated with certain contact rates [61,62,67], or to add and remove individuals at a constant rate [64,65,63,68], a rate depending on prevalence [69,70,71,72,73,74], or according to a framework where behavior that is more successful is imitated by others [75,66,76]. Extra compartments signify behavioral heterogeneities amongst members of a population, and the disease transmission rates associated with them also vary.…”

Section: Rule-based Modelsmentioning

confidence: 99%

See 1 more Smart Citation

Coupled disease–behavior dynamics on complex networks: A review

Wang

Andrews

et al. 2015

Physics of Life Reviews

463

304

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It is increasingly recognized that a key component of successful infection control efforts is understanding the complex, two-way interaction between disease dynamics and human behavioral and social dynamics. Human behavior such as contact precautions and social distancing clearly influence disease prevalence, but disease prevalence can in turn alter human behavior, forming a coupled, nonlinear system. Moreover, in many cases, the spatial structure of the population cannot be ignored, such that social and behavioral processes and/or transmission of infection must be represented with complex networks. Research on studying coupled disease-behavior dynamics in complex networks in particular is growing rapidly, and frequently makes use of analysis methods and concepts from statistical physics. Here, we review some of the growing literature in this area. We contrast network-based approaches to homogeneous-mixing approaches, point out how their predictions differ, and describe the rich and often surprising behavior of disease-behavior dynamics on complex networks, and compare them to processes in statistical physics. We discuss how these models can capture the dynamics that characterize many real-world scenarios, thereby suggesting ways that policy makers can better design effective prevention strategies. We also describe the growing sources of digital data that are facilitating research in this area. Finally, we suggest pitfalls which might be faced by researchers in the field, and we suggest several ways in which the field could move forward in the coming years.

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Section: Rule-based Modelsmentioning

confidence: 89%

Section: Rule-based Modelsmentioning

confidence: 99%

Coupled disease–behavior dynamics on complex networks: A review

Wang

Andrews

et al. 2015

Physics of Life Reviews

463

304

View full text Add to dashboard Cite

show abstract

“…Recognizing the importance of behavioral responses in populations has altered the way epidemiological modelers consider transmission [52][53][54][55][56][57][58]. Such models have shown that social distancing can be effective at reducing the at-tack rate of an epidemic [59,60] and that social distancing behavior is a plausible explanation for certain phenomena arising in real epidemics, such as multiple outbreaks or waves of infection [60,61].…”

Section: Basic Concepts In Infectious Disease Epidemiologymentioning

confidence: 99%

Statistical physics of vaccination

et al. 2016

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Historically, infectious diseases caused considerable damage to human societies, and they continue to do so today. To help reduce their impact, mathematical models of disease transmission have been studied to help understand disease dynamics and inform prevention strategies. Vaccination-one of the most important preventive measures of modern times-is of great interest both theoretically and empirically. And in contrast to traditional approaches, recent research increasingly explores the pivotal implications of individual behavior and heterogeneous contact patterns in populations. Our report reviews the developmental arc of theoretical epidemiology with emphasis on vaccination, as it led from classical models assuming homogeneously mixing (mean-field) populations and ignoring human behavior, to recent models that account for behavioral feedback and/or population spatial/social structure. Many of the methods used originated in statistical physics, such as lattice and network models, and their associated analytical frameworks. Similarly, the feedback loop between vaccinating behavior and disease propagation forms a coupled nonlinear system with analogs in physics. We also review the new paradigm of digital epidemiology, wherein sources of digital data such as online social media are mined for high-resolution information on epidemiologically relevant individual behavior. Armed with the tools and concepts of statistical physics, and further assisted by new sources of digital data, models that capture nonlinear interactions between behavior and disease dynamics offer a novel way of modeling real-world phenomena, and can help improve health outcomes. We conclude the review by discussing open problems in the field and promising directions for future research.

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“…Several other studies model the behavioural change as constant rates (i.e. similar to weighted means that distinguish different contributions of various risk levels of individuals) such as Poletti et al [22,23] or Brauer [6]. Again, here the focus is on how sensitive the epidemic is with respect to behavioural changes.…”

Section: Introductionmentioning

confidence: 99%

The effect of risk-taking behaviour in epidemic models

Sega

Maxin

Eaton

et al. 2015

Journal of Biological Dynamics

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We study an epidemic model that incorporates risk-taking behaviour as a response to a perceived low prevalence of infection that follows from the administration of an effective treatment or vaccine. We assume that knowledge about the number of infected, recovered and vaccinated individuals has an effect in the contact rate between susceptible and infectious individuals. We show that, whenever optimism prevails in the risk behaviour response, the fate of an epidemic may change from disease clearance to disease persistence. Moreover, under certain conditions on the parameters, increasing the efficiency of vaccine and/or treatment has the unwanted effect of increasing the epidemic reproductive number, suggesting a wider range of diseases may become endemic due to risk-taking alone. These results indicate that the manner in which treatment/vaccine effectiveness is advertised can have an important influence on how the epidemic unfolds.

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Spontaneous behavioural changes in response to epidemics

Cited by 157 publications

References 27 publications

Coupled disease–behavior dynamics on complex networks: A review

Coupled disease–behavior dynamics on complex networks: A review

Statistical physics of vaccination

The effect of risk-taking behaviour in epidemic models

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