Cascading crashes induced by the individual heterogeneity in complex networks

Wearing a face mask has been a key approach to contain or slow down the spread of COVID-19 in the ongoing pandemic. However, there is huge heterogeneity among individuals in their willingness to wear face masks during an epidemic. This research aims to investigate the individual heterogeneity to wear face masks and its associated predictors during the COVID-19 pandemic when mask-wearing was not mandatory but individual choices. Based on a survey of 708 Malaysian adults and a multivariate least-squares fitting analysis, the results reveal a significant variance among individuals in wearing masks, as 34% of the individual adults did not always wear masks in public places. Female, individuals who wash their hands more frequently, and those who reported more availability of personal protective equipment were more likely to practice mask-wearing. The identification of less compliant groups of mask-wearing has critical implications by enabling more specific health communication campaigns.

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Impact of Immunization Strategies on the Dynamics of Social Contagions

Discrete Dynamics in Nature and Society ◽

10.1155/2020/5284348 ◽

2020 ◽

Vol 2020 ◽

pp. 1-9

Author(s):

Hao Peng ◽

Wangxin Peng ◽

Dandan Zhao ◽

Zhaolong Hu ◽

Jianmin Han ◽

...

Keyword(s):

Complex Networks ◽

Threshold Model ◽

Network Heterogeneity ◽

Immunization Strategies ◽

Spreading Dynamics ◽

Theoretical Predictions ◽

Underlying Network ◽

Edge Based ◽

The Individual ◽

The Impact

Immunization strategies on complex networks are effective methods to control the spreading dynamics on complex networks, which change the topology and connectivity of the underlying network, thereby affecting the dynamics process of propagation. Here, we use a non-Markovian threshold model to study the impact of immunization strategies on social contagions, in which the immune index greater than (or equal to) 0 corresponds to targeted (random) immunization, and when the immune index is less than 0, the probability of an individual being immunized is inversely related to the degree of the individual. A generalized edge-based compartmental theory is developed to analyze the dynamics of social contagions under immunization, and theoretical predictions are very consistent with simulation results. We find that increasing the immune index or increasing the immune ratio will reduce the final adoption size and increase the outbreak threshold, in other words, make the residual network after immunization not conducive to social contagions. Interestingly, enhancing the network heterogeneity is proved to help improve the immune efficiency of targeted immunization. Besides, the dependence of the outbreak threshold on the network heterogeneity is correlated with the immune ratio and immune index.

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A Revised Framework to Evaluate the Consistency Assumption Globally in a Network of Interventions

Medical Decision Making ◽

10.1177/0272989x211068005 ◽

2021 ◽

pp. 0272989X2110680

Author(s):

Loukia M. Spineli

Keyword(s):

Complex Networks ◽

Meta Analysis ◽

Model Fit ◽

Individual Data ◽

Posterior Mean ◽

Data Points ◽

The Individual

Background The unrelated mean effects (UME) model has been proposed for evaluating the consistency assumption globally in the network of interventions. However, the UME model does not accommodate multiarm trials properly and omits comparisons between nonbaseline interventions in the multiarm trials not investigated in 2-arm trials. Methods We proposed a refinement of the UME model that tackles the limitations mentioned above. We also accompanied the scatterplots on the posterior mean deviance contributions of the trial arms under the network meta-analysis (NMA) and UME models with Bland-Altman plots to detect outlying trials contributing to poor model fit. We applied the refined and original UME models to 2 networks with multiarm trials. Results The original UME model omitted more than 20% of the observed comparisons in both networks. The thorough inspection of the individual data points’ deviance contribution using complementary plots in conjunction with the measures of model fit and the estimated between-trial variance indicated that the refined and original UME models revealed possible inconsistency in both examples. Conclusions The refined UME model allows proper accommodation of the multiarm trials and visualization of all observed evidence in complex networks of interventions. Furthermore, considering several complementary plots to investigate deviance helps draw informed conclusions on the possibility of global inconsistency in the network. Highlights We have refined the unrelated mean effects (UME) model to incorporate multiarm trials properly and to estimate all observed comparisons in complex networks of interventions. Forest plots with posterior summaries of all observed comparisons under the network meta-analysis and refined UME model can uncover the consequences of potential inconsistency in the network. Using complementary plots to investigate the individual data points’ deviance contribution in conjunction with model fit measures and estimated heterogeneity aid in detecting possible inconsistency.

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Extensive brain structural heterogeneity in individuals with schizophrenia and bipolar disorder

10.1101/2020.05.08.20095091 ◽

2020 ◽

Author(s):

Thomas Wolfers ◽

Jaroslav Rokicki ◽

Dag Alnæs ◽

Ingrid Agartz ◽

Seyed Mostafa Kia ◽

...

Keyword(s):

Bipolar Disorder ◽

Gaussian Process Regression ◽

Structural Heterogeneity ◽

Brain Regions ◽

Individual Heterogeneity ◽

Group Level ◽

Healthy Individuals ◽

Voxel Based Morphometry ◽

Structural Aberrations ◽

The Individual

ABSTRACTIdentifying brain processes involved in the risk and development of mental disorders is a major aim. We recently reported substantial inter-individual heterogeneity in brain structural aberrations among patients with schizophrenia and bipolar disorder. Estimating the normative range of voxel-based morphometry (VBM) data among healthy individuals using a Gaussian Process Regression (GPR) enables us to map individual deviations from the healthy range in unseen datasets. Here we aim to replicate our previous results in an independent sample of patients with schizophrenia (n=166), bipolar disorder (n=135) and healthy individuals (n=687). In line with previous findings, our results revealed robust group level differences between patients and healthy controls, yet only a small proportion of patients with schizophrenia or bipolar disorder exhibited extreme negative deviations from the norm in the same brain regions. These direct replications support that group level-differences in brain structure disguise considerable individual differences in brain aberrations, with important implications for the interpretation and generalization of group-level brain imaging findings to the individual with a mental disorder.

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Consume Now or Later? Time Inconsistency, Collective Choice, and Revealed Preference

The American Economic Review ◽

10.1257/aer.104.12.4147 ◽

2014 ◽

Vol 104 (12) ◽

pp. 4147-4183 ◽

Cited By ~ 23

Author(s):

Abi Adams ◽

Laurens Cherchye ◽

Bram De Rock ◽

Ewout Verriest

Keyword(s):

Individual Preference ◽

Revealed Preference ◽

Time Inconsistency ◽

Individual Heterogeneity ◽

Empirical Application ◽

Household Level ◽

Individual Level ◽

Explicit Recognition ◽

The Individual ◽

Household Choice

We develop a revealed preference methodology that allows us to explore whether time inconsistencies in household choice are the product of individual preference nonstationarities or the result of individual heterogeneity and renegotiation within the household. An empirical application to household-level microdata highlights that an explicit recognition of the collective nature of household choice enables the observed behavior to be rationalized by a theory that assumes preference stationarity at the individual level. The methodology created in this paper also facilitates the recovery of theory-consistent discount rates for each individual within particular household under study. (JEL E24, F13, F16)

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Celebrity

World Authorship ◽

10.1093/oxfordhb/9780198819653.013.3 ◽

2020 ◽

pp. 30-44

Author(s):

Rebecca Braun

Keyword(s):

Complex Networks ◽

Early Modern ◽

Popular Fiction ◽

Early Modern Period ◽

Individual Author ◽

Modern Period ◽

The World ◽

Practical Analysis ◽

The Individual ◽

Model Approach

This chapter shows how the methods and approaches of Celebrity Studies throw fresh light on what authors and literature can do in the world. In particular, the divide between elite and popular fiction turns out to be illusory once we start paying attention to the way authors and their works actually move around. Combining celebrity theory with a practical analysis of the networks sustaining literature allows us to examine afresh the ways and degrees to which authors accrue ‘attention capital’ and from which social groupings, and why. Working through examples taken from the early modern period to the present day, this chapter provides a model approach not only for seeing beyond the individual author to witness the complex networks of agents involved in the process of authorship—from editors to translators, agents, and readers, and so on—but also for placing the question of agency once again at the heart of that process.

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Previous success and current body condition determine breeding propensity in Lesser Scaup: evidence for the individual heterogeneity hypothesis

The Auk ◽

10.1642/auk-13-236.1 ◽

2014 ◽

Vol 131 (3) ◽

pp. 287-297 ◽

Cited By ~ 13

Author(s):

Jeffrey M. Warren ◽

Kyle A. Cutting ◽

John Y. Takekawa ◽

Susan E. De La Cruz ◽

Tony D. Williams ◽

...

Keyword(s):

Body Condition ◽

Individual Heterogeneity ◽

Lesser Scaup ◽

The Individual ◽

Breeding Propensity

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Dynamically characterizing individual clinical change by the steady state of disease-associated pathway

BMC Bioinformatics ◽

10.1186/s12859-019-3271-x ◽

2019 ◽

Vol 20 (S25) ◽

Author(s):

Shaoyan Sun ◽

Xiangtian Yu ◽

Fengnan Sun ◽

Ying Tang ◽

Juan Zhao ◽

...

Keyword(s):

Virus Infection ◽

Disease Classification ◽

Individual Heterogeneity ◽

Dynamical Process ◽

Virus Infections ◽

Temporal Gene Expression ◽

Disease States ◽

The Individual ◽

Consistent Functions ◽

Key Pathways

Abstract Background Along with the development of precision medicine, individual heterogeneity is attracting more and more attentions in clinical research and application. Although the biomolecular reaction seems to be some various when different individuals suffer a same disease (e.g. virus infection), the final pathogen outcomes of individuals always can be mainly described by two categories in clinics, i.e. symptomatic and asymptomatic. Thus, it is still a great challenge to characterize the individual specific intrinsic regulatory convergence during dynamic gene regulation and expression. Except for individual heterogeneity, the sampling time also increase the expression diversity, so that, the capture of similar steady biological state is a key to characterize individual dynamic biological processes. Results Assuming the similar biological functions (e.g. pathways) should be suitable to detect consistent functions rather than chaotic genes, we design and implement a new computational framework (ABP: Attractor analysis of Boolean network of Pathway). ABP aims to identify the dynamic phenotype associated pathways in a state-transition manner, using the network attractor to model and quantify the steady pathway states characterizing the final steady biological sate of individuals (e.g. normal or disease). By analyzing multiple temporal gene expression datasets of virus infections, ABP has shown its effectiveness on identifying key pathways associated with phenotype change; inferring the consensus functional cascade among key pathways; and grouping pathway activity states corresponding to disease states. Conclusions Collectively, ABP can detect key pathways and infer their consensus functional cascade during dynamical process (e.g. virus infection), and can also categorize individuals with disease state well, which is helpful for disease classification and prediction.

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Better the devil you know: common terns stay with a previous partner although pair bond duration does not affect breeding output

Proceedings of The Royal Society B Biological Sciences ◽

10.1098/rspb.2016.1424 ◽

2017 ◽

Vol 284 (1846) ◽

pp. 20161424 ◽

Cited By ~ 5

Author(s):

Maren Rebke ◽

Peter H. Becker ◽

Fernando Colchero

Keyword(s):

Bond Length ◽

Reproductive Performance ◽

Pair Bond ◽

Common Tern ◽

Individual Heterogeneity ◽

Monogamous Species ◽

The Common ◽

The Individual ◽

The Devil ◽

Pair Level

In a monogamous species two partners contribute to the breeding process. We study pair formation as well as the effect of pair bond length and age on breeding performance, incorporating individual heterogeneity, based on a high-quality dataset of a long-lived seabird, the common tern ( Sterna hirundo ). To handle missing information and model the complicated processes driving reproduction, we use a hierarchical Bayesian model of the steps that lead to the number of fledglings, including processes at the individual and the pair level. The results show that the age of both partners is important for reproductive performance, with similar patterns for both sexes and individual heterogeneity in reproductive performance, but pair bond length is not. The terns are more likely to choose a former partner independent of the previous breeding outcome with that partner, which suggests a tendency to retain the partner chosen at the beginning of the breeding career.

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