Imperfect vaccine and hysteresis

Chen, Xingru; Fu, Feng

doi:10.1098/rspb.2018.2406

Cited by 74 publications

(63 citation statements)

References 71 publications

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“…When , , and the model describes a well-mixed society. Although the model is still essentially based on pairwise interactions, dyadic games nonetheless are widely applicable to understanding societal level issues including but not limited to political crises 39 , vaccine compliance 40 , antibiotic resistance 41 , and cultural diversity 42 . Moreover, some argue that simple, transparent models from which insights are readily apparent may be of greater use than more complicated models 43 .…”

Section: Introductionmentioning

confidence: 99%

Decisions and disease: a mechanism for the evolution of cooperation

Karlsson

Rowlett

2020

Sci Rep

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In numerous contexts, individuals may decide whether they take actions to mitigate the spread of disease, or not. Mitigating the spread of disease requires an individual to change their routine behaviours to benefit others, resulting in a ‘disease dilemma’ similar to the seminal prisoner’s dilemma. In the classical prisoner’s dilemma, evolutionary game dynamics predict that all individuals evolve to ‘defect.’ We have discovered that when the rate of cooperation within a population is directly linked to the rate of spread of the disease, cooperation evolves under certain conditions. For diseases which do not confer immunity to recovered individuals, if the time scale at which individuals receive accurate information regarding the disease is sufficiently rapid compared to the time scale at which the disease spreads, then cooperation emerges. Moreover, in the limit as mitigation measures become increasingly effective, the disease can be controlled; the number of infections tends to zero. It has been suggested that disease spreading models may also describe social and group dynamics, indicating that this mechanism for the evolution of cooperation may also apply in those contexts.

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Section: Introductionmentioning

confidence: 99%

Decisions and disease: a mechanism for the evolution of cooperation

Karlsson

Rowlett

2020

Sci Rep

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“…The interest of the physics community was further spurred on by the discovery that scale-free networks provide a unifying framework for the evolution of cooperation [9], and over the past almost two decades, methods of physics have been applied to many contemporary societal challenges [10]. Examples include traffic [11], crime [12], epidemic processes [13], vaccination [14,15], cooperation [16], climate inaction [17], as well as antibiotic overuse [18] and moral behavior [19], to name just some examples.…”

Section: Introductionmentioning

confidence: 99%

Assortativity provides a narrow margin for enhanced cooperation on multilayer networks

et al. 2019

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Research at the interface of statistical physics, evolutionary game theory, and network science has in the past two decades significantly improved our understanding of cooperation in structured populations. We know that networks with broad-scale degree distributions favor the emergence of robust cooperative clusters, and that temporal networks might preclude defectors to exploit cooperators, provided the later can sever their bad ties soon enough. In recent years, however, research has shifted from single and isolated networks to multilayer and interdependent networks. This has revealed new paths to cooperation, but also opened up new questions that remain to be answered. We here study how assortativity in connections between two different network layers affects public cooperation. The connections between the two layers determine to what extent payoffs in one network influence the payoffs in the other network. We show that assortative linking between the layersconnecting hubs of one network with the hubs in the other-does enhance cooperation under adverse conditions, but does so with a relatively modest margin in comparison to random matching or disassortative matching between the two layers. We also confirm previous results, showing that the bias in the payoffs in terms of contributions from different layers can help public cooperation to prevail, and in fact more so than the assortativity between layers. These results are robust to variations in the network structure and average degree, and they can be explained well by the distribution of strategies across the networks and by the suppression of individual success levels that is due to the payoff interdependence.

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“…reduction of overusage of antibiotics, which causes worsening medical cost and mortality especially for life-threatening bacteria infections [72]. Chen et al already showed that hysteresis can appear as an unprecedented roadblock for the recovery of vaccination uptake [73]. Our perceived study may be also useful to understand this puzzling phenomenon too.…”

Section: Discussionmentioning

confidence: 76%

Synchronization to extreme events in moving agents

et al. 2019

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Interactions amongst agents frequently exist only at particular moments in time, depending on their closeness in space and movement parameters. Here we propose a minimal model of moving agents where the network of contacts changes over time due to their motion. In particular, agents interact based on their proximity in a two-dimensional space, but only if they belong to the same fixed interaction zones. Our research reveals the emergence of global synchronization if all the interaction zones are attractive. However, if some of the interaction zones are repulsive, they deflect synchrony and lead to short-lasting but recurrent deviations that constitute extreme events in the network. We use two paradigmatic oscillators for the description of the agent dynamics to demonstrate our findings numerically, and we also provide an analytical formulation to describe the emergence of complete synchrony and the thresholds that distinguish extreme events from other intermittent states based on the peak-over-threshold approach. IntroductionResearch to understand the interplay between complex networks and the dynamical properties of coupled oscillators has been a hotspot for the last few decades and the developing phenomenon of synchronization [1-4] is one of the most important dynamical processes that has been in the center of these researches. Cooperation [5,6] and time series analysis [7,8] in complex network have been studied in the past few years. From the perspective of synchronization among coupled oscillators placed into a complex network [9,10], the correlation between the network's topology and local dynamics is quite decisive. Here, synchronization signifies a process of adaptation to a common collective behavior of oscillators due to their interaction. In most of the previous studies of such systems, the network topology is assumed to be invariant over time and thus the system is controlled by a deterministic static formation for all the course of time. But such a crude assumption regarding the network connectivity inhibits one to model and study most of the practical instances.Recently, time-varying networks have grabbed the attention of the researchers due to their enormous applications in various fields like functional brain network [11], epidemic modeling [12], communication systems [13,14] and many more. Time-varying networks, also known as temporal networks [15] indicate those networks in which links get activated for a certain course of time. On the assumption of time-invariant nodes which are static over time, many network architecture is studied, e.g. power transmission elements are considered as such nodes among which haphazard links are treated as the coupling between elements of the power transmission system [16]. Even for functional brain networks [11], these types of nodes are considered to characterize the dynamical evolution. Particularly, the scenario of time-varying networks owing to the mobility in the nodes is really a significant platform to study several dynamical processes over them in which no...

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Imperfect vaccine and hysteresis

Cited by 74 publications

References 71 publications

Decisions and disease: a mechanism for the evolution of cooperation

Decisions and disease: a mechanism for the evolution of cooperation

Assortativity provides a narrow margin for enhanced cooperation on multilayer networks

Synchronization to extreme events in moving agents

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