Contagion Dynamics Research Articles

Pathways to discontinuous transitions in interacting contagion dynamics

Yet often neglected, dynamical interdependencies between concomitant contagion processes can alter their intrinsic equilibria and bifurcations. A particular case of interest for disease control is the emergence of discontinuous transitions in epidemic dynamics coming from their interactions with other simultaneous processes. To address this problem, here we propose a framework coupling a standard epidemic dynamics with another contagion process, presenting a tunable parameter shaping the nature of its transitions. Our model retrieves well-known results in the literature, such as the existence of first-order transitions arising from the mutual cooperation of epidemics or the onset of abrupt transitions when social contagions unidirectionally drive epidemics. We also reveal that negative feedback loops between simultaneous dynamical processes might suppress abrupt phenomena, thus increasing systems robustness against external perturbations. Our results render a general perspective toward finding different pathways to abrupt phenomena from the interaction of contagion processes.

Higher-Order Components Dictate Higher-Order Contagion Dynamics in Hypergraphs.

The presence of the giant component is a necessary condition for the emergence of collective behavior in complex networked systems. Unlike networks, hypergraphs have an important native feature that components of hypergraphs might be of higher order, which could be defined in terms of the number of common nodes shared between hyperedges. Although the extensive higher-order component (HOC) could be witnessed ubiquitously in real-world hypergraphs, the role of the giant HOC in collective behavior on hypergraphs has yet to be elucidated. In this Letter, we demonstrate that the presence of the giant HOC fundamentally alters the outbreak patterns of higher-order contagion dynamics on real-world hypergraphs. Most crucially, the giant HOC is required for the higher-order contagion to invade globally from a single seed. We confirm it by using synthetic random hypergraphs containing adjustable and analytically calculable giant HOC.

Chaos, Fractionality, Nonlinear Contagion, and Causality Dynamics of the Metaverse, Energy Consumption, and Environmental Pollution: Markov-Switching Generalized Autoregressive Conditional Heteroskedasticity Copula and Causality Methods

Metaverse (MV) technology introduces new tools for users each day. MV companies have a significant share in the total stock markets today, and their size is increasing. However, MV technologies are questioned as to whether they contribute to environmental pollution with their increasing energy consumption (EC). This study explores complex nonlinear contagion with tail dependence and causality between MV stocks, EC, and environmental pollution proxied with carbon dioxide emissions (CO2) with a decade-long daily dataset covering 18 May 2012–16 March 2023. The Mandelbrot–Wallis and Lo’s rescaled range (R/S) tests confirm long-term dependence and fractionality, and the largest Lyapunov exponents, Shannon and Havrda, Charvât, and Tsallis (HCT) entropy tests followed by the Kolmogorov–Sinai (KS) complexity measure confirm chaos, entropy, and complexity. The Brock, Dechert, and Scheinkman (BDS) test of independence test confirms nonlinearity, and White‘s test of heteroskedasticity of nonlinear forms and Engle’s autoregressive conditional heteroskedasticity test confirm heteroskedasticity, in addition to fractionality and chaos. In modeling, the marginal distributions are modeled with Markov-Switching Generalized Autoregressive Conditional Heteroskedasticity Copula (MS-GARCH–Copula) processes with two regimes for low and high volatility and asymmetric tail dependence between MV, EC, and CO2 in all regimes. The findings indicate relatively higher contagion with larger copula parameters in high-volatility regimes. Nonlinear causality is modeled under regime-switching heteroskedasticity, and the results indicate unidirectional causality from MV to EC, from MV to CO2, and from EC to CO2, in addition to bidirectional causality among MV and EC, which amplifies the effects on air pollution. The findings of this paper offer vital insights into the MV, EC, and CO2 nexus under chaos, fractionality, and nonlinearity. Important policy recommendations are generated.

Quantitative Modeling of Financial Contagion: Unraveling Market Dynamics and Bubble Detection Mechanisms

This study explored the complex interplay and potential risk of financial contagion across major financial indices, focusing on the Bucharest Exchange Trading Investment Funds Index (BET-FI), along with global indices like the S&P 500, Nasdaq Composite (IXIC), and Dow Jones Industrial Average (DJIA). Our analysis covered an extensive period from 2012 to 2023, with a particular emphasis on Romania’s financial market. We employed Autoregressive Distributed Lag (ARDL) modeling to examine the interrelations among these indices, treating the BET-FI index as our primary variable. Our research also integrated Exponential Curve Fitting (EXCF) and Generalized Supremum Augmented Dickey–Fuller (GSADF) models to identify and scrutinize potential price bubbles in these indices. We analyzed moments of high volatility and deviations from typical market trends, influenced by diverse factors like government policies, presidential elections, tech sector performance, the COVID-19 pandemic, and geopolitical tensions, specifically the Russia–Ukraine conflict. The ARDL model revealed a stable long-term relationship among the variables, indicating their interconnectedness. Our study also highlights the significance of short-term market shifts leading to long-term equilibrium, as shown in the Error Correction Model (ECM). This suggests the existence of contagion effects, where small, short-term incidents can trigger long-term, domino-like impacts on the financial markets. Furthermore, our variance decomposition examined the evolving contributions of different factors over time, shedding light on their changing interactions and impact. The Cholesky factors demonstrated the interdependence between indices, essential for understanding financial contagion effects. Our research thus uncovered the nuanced dynamics of financial contagion, offering insights into market variations, the effectiveness of our models, and strategies for detecting financial bubbles. This study contributes valuable knowledge to the academic field and offers practical insights for investors in turbulent financial environments.

Probabilistic activity driven model of temporal simplicial networks and its application on higher-order dynamics.

Network modeling characterizes the underlying principles of structural properties and is of vital significance for simulating dynamical processes in real world. However, bridging structure and dynamics is always challenging due to the multiple complexities in real systems. Here, through introducing the individual's activity rate and the possibility of group interaction, we propose a probabilistic activity-driven (PAD) model that could generate temporal higher-order networks with both power-law and high-clustering characteristics, which successfully links the two most critical structural features and a basic dynamical pattern in extensive complex systems. Surprisingly, the power-law exponents and the clustering coefficients of the aggregated PAD network could be tuned in a wide range by altering a set of model parameters. We further provide an approximation algorithm to select the proper parameters that can generate networks with given structural properties, the effectiveness of which is verified by fitting various real-world networks. Finally, we construct the co-evolution framework of the PAD model and higher-order contagion dynamics and derive the critical conditions for phase transition and bistable phenomenon using theoretical and numerical methods. Results show that tendency of participating in higher-order interactions can promote the emergence of bistability but delay the outbreak under heterogeneous activity rates. Our model provides a basic tool to reproduce complex structural properties and to study the widespread higher-order dynamics, which has great potential for applications across fields.

Analysis of tail risk contagion among industry sectors in the Chinese stock market during the COVID-19 pandemic

The COVID-19 pandemic has inflicted substantial impacts on global financial markets and the economy. This study investigates the impact of two pandemic outbreaks in China on industry sectors in its stock market. The Conditional Autoregressive Value at Risk (CAViaR) model is applied to compute tail risks across 16 selected industry sectors. In Addition, risk correlation networks are constructed to illustrate the risk correlations among industry sectors during different stages of the two outbreaks. Furthermore, risk contagion networks are built based on the Granger causality test to examine the similarities and differences in the contagion mechanisms between the two outbreaks. The findings of this study show that (i) the two pandemic outbreaks have resulted in tail risks across most industries in the Chinese stock market. (ii) The risk correlation network exhibited increased compactness during both outbreaks, with the impact of the second outbreak on the network being less severe than that of the first outbreak. (iii) During the first outbreak, the financial industry was the primary source of risk output. In contrast, during the second outbreak, the localized nature of the outbreak in Shanghai resulted in the industries closely linked to the city's economy and trade becoming the most significant risk contributors. These empirical findings have practical implications for both researchers and decision-makers, offering insight into the dynamics of risk contagion among stock market industries during major public emergencies.

Effects of experts on the coupling dynamics of complex contagion of awareness and epidemic spreading

Effects of experts on the coupling dynamics of complex contagion of awareness and epidemic spreading

Assessing the “Systemic Barriers” Affecting Lean Implementation: A Comparison Between Manufacturing and Lean Software Development Contexts

This study attempts to understand the systemic nature of barriers affecting lean implementation in two highly contrasting contexts, i.e., manufacturing and software development. The impact of barriers and their interrelationships is explained using the theoretical lens of organizational change, including dynamics of motivational contagion, resistance, and readiness to change. Furthermore, the varying impact of barriers in manufacturing and service contexts is posited using the service-dominant logic, which is subsequently examined by analyzing barrier impacts in a manufacturing firm and a software development firm in India. Lean implementation barriers common to multiple industry sectors were identified from extant literature and then subjected to graph theory (GT) methodology to quantify the causal interactions among these common barriers. Relevant data were collected from team leaders in Indian manufacturing and software development firms tasked with lean implementation in their organizations. A network of relationships was obtained using the GT approach for major barrier groups and associated subbarriers. The findings highlighted the varying effect of these barriers in the two highly contrasting contexts. This study extends the understanding of the systemic effect of lean implementation barriers, which is a relatively novel perspective.

Neural parameter calibration and uncertainty quantification for epidemic forecasting.

The recent COVID-19 pandemic has thrown the importance of accurately forecasting contagion dynamics and learning infection parameters into sharp focus. At the same time, effective policy-making requires knowledge of the uncertainty on such predictions, in order, for instance, to be able to ready hospitals and intensive care units for a worst-case scenario without needlessly wasting resources. In this work, we apply a novel and powerful computational method to the problem of learning probability densities on contagion parameters and providing uncertainty quantification for pandemic projections. Using a neural network, we calibrate an ODE model to data of the spread of COVID-19 in Berlin in 2020, achieving both a significantly more accurate calibration and prediction than Markov-Chain Monte Carlo (MCMC)-based sampling schemes. The uncertainties on our predictions provide meaningful confidence intervals e.g. on infection figures and hospitalisation rates, while training and running the neural scheme takes minutes where MCMC takes hours. We show convergence of our method to the true posterior on a simplified SIR model of epidemics, and also demonstrate our method's learning capabilities on a reduced dataset, where a complex model is learned from a small number of compartments for which data is available.

On the role of diffusion dynamics on community-aware centrality measures.

Theoretical and empirical studies on diffusion models have revealed their versatile applicability across different fields, spanning from sociology and finance to biology and ecology. The presence of a community structure within real-world networks has a substantial impact on how diffusion processes unfold. Key nodes located both within and between these communities play a crucial role in initiating diffusion, and community-aware centrality measures effectively identify these nodes. While numerous diffusion models have been proposed in literature, very few studies investigate the relationship between the diffusive ability of key nodes selected by community-aware centrality measures, the distinct dynamical conditions of various models, and the diverse network topologies. By conducting a comparative evaluation across four diffusion models, utilizing both synthetic and real-world networks, along with employing two different community detection techniques, our study aims to gain deeper insights into the effectiveness and applicability of the community-aware centrality measures. Results suggest that the diffusive power of the selected nodes is affected by three main factors: the strength of the network's community structure, the internal dynamics of each diffusion model, and the budget availability. Specifically, within the category of simple contagion models, such as SI, SIR, and IC, we observe similar diffusion patterns when the network's community structure strength and budget remain constant. In contrast, the LT model, which falls under the category of complex contagion dynamics, exhibits divergent behavior under the same conditions.

Socioeconomic conditions and contagion dynamics of the COVID-19 pandemic with and without mitigation measures: Evidence from 185 countries

It is well documented that COVID-19 contagion and mortality rates differed systematically across countries. Less is known, however, on whether these differences could be explained by socioeconomic conditions that may determine both the extent to which individuals voluntarily take protection measures in the absence of Non-Pharmaceutical Interventions (NPIs) or comply with imposed NPIs, when these are in place. Using data from 185 countries, we examine associations of COVID-19 infection and mortality dynamics with socioeconomic conditions, as measured by poverty rates, in periods before and after NPIs have been imposed. We find that, in the initial period of the pandemic, when no NPIs were in place, daily growth of COVID-19 cases and deaths are positively associated with the share of the population living in poverty, whereas, in the following period, when NPIs were implemented, these associations turn negative. We argue that these results could be explained by the fact that NPIs are expected to be more effective in countries with high poverty rates where voluntary physical distancing is low and physical distancing practices are more responsive to imposed measures.

Insights from exact social contagion dynamics on networks with higher-order structures

Abstract Recently, there has been an increasing interest in studying dynamical processes on networks exhibiting higher-order structures, such as simplicial complexes, where the dynamics acts above and beyond dyadic interactions. Using simulations or heuristically derived epidemic spreading models, it was shown that new phenomena can emerge, such as bi-stability/multistability. Here, we show that such new emerging phenomena do not require complex contact patterns, such as community structures, but naturally result from the higher-order contagion mechanisms. We show this by deriving an exact higher-order Susceptible-Infected-Susceptible model and its limiting mean-field equivalent for fully connected simplicial complexes. Going beyond previous results, we also give the global bifurcation picture for networks with 3- and 4-body interactions, with the latter allowing for two non-trivial stable endemic steady states. Differently from previous approaches, we are able to study systems featuring interactions of arbitrary order. In addition, we characterize the contributions from higher-order infections to the endemic equilibrium as perturbations of the pairwise baseline, finding that these diminish as the pairwise rate of infection increases. Our approach represents a first step towards a principled understanding of higher-order contagion processes beyond triads and opens up further directions for analytical investigations.

Dimensional reduction of solvency contagion dynamics on financial networks

Modelling systems with networks has been a powerful approach to tame the complexity of several phenomena. Unfortunately, the large number of variables to take into consideration often makes concrete problems difficult to handle. Methods of dimensional reduction are useful tools to rescale a complex network down to a low-dimensional effective system and thus to capture its global dynamical features. Here we study the application of the degree-weighted and spectral reduction methods to an important class of dynamical processes on networks: the propagation of credit shocks within an interbank network, modelled according to the DebtRank algorithm. We introduce an effective version of the dynamics, characterized by functions with continuous derivatives that can be handled by the dimensional reduction. We test the reduction methods against the full dynamical system in different interbank market settings: homogeneous and heterogeneous networks generated from state-of-the-art reconstruction methods as well as networks derived from empirical e-MID data. Our results indicate that, for proper choices of the bank default probability, reduction methods can provide reliable estimates of systemic risk in the market, with the spectral reduction better handling heterogeneous networks. Finally, we provide new insights on the nature and working principles of dimensional reduction methods.

Contagion dynamics on hypergraphs with nested hyperedges.

In complex social systems encoded as hypergraphs, higher-order (i.e., group) interactions taking place among more than two individuals are represented by hyperedges. One of the higher-order correlation structures native to hypergraphs is the nestedness: Some hyperedges can be entirely contained (that is, nested) within another larger hyperedge, which itself can also be nested further in a hierarchical manner. Yet the effect of such hierarchical structure of hyperedges on the dynamics has remained unexplored. In this context, here we propose a random nested-hypergraph model with a tunable level of nestedness and investigate the effects of nestedness on a higher-order susceptible-infected-susceptible process. By developing an analytic framework called the facet approximation, we obtain the steady-state fraction of infected nodes on the random nested-hypergraph model more accurately than existing methods. Our results show that the hyperedge-nestedness affects the phase diagram significantly. Monte Carlo simulations support the analytical results.

Virus dynamics with behavioral responses

Motivated by epidemics such as COVID-19, we study the spread of a contagious disease when behavior responds to the disease's prevalence. We extend the SIR epidemiological model to include endogenous meeting rates. Individuals benefit from economic activity, but activity involves interactions with potentially infected individuals. The main focus is a theoretical analysis of contagion dynamics and behavioral responses to changes in risk. We obtain a simple condition for when public-health interventions or variants of a disease will have paradoxical effects on infection rates due to risk compensation. Behavioral responses are most likely to undermine public-health interventions near the peak of severe diseases.

Overcoming vaccine hesitancy by multiplex social network targeting: an analysis of targeting algorithms and implications

Incorporating social factors into disease prevention and control efforts is an important undertaking of behavioral epidemiology. The interplay between disease transmission and human health behaviors, such as vaccine uptake, results in complex dynamics of biological and social contagions. Maximizing intervention adoptions via network-based targeting algorithms by harnessing the power of social contagion for behavior and attitude changes largely remains a challenge. Here we address this issue by considering a multiplex network setting. Individuals are situated on two layers of networks: the disease transmission network layer and the peer influence network layer. The disease spreads through direct close contacts while vaccine views and uptake behaviors spread interpersonally within a potentially virtual network. The results of our comprehensive simulations show that network-based targeting with pro-vaccine supporters as initial seeds significantly influences vaccine adoption rates and reduces the extent of an epidemic outbreak. Network targeting interventions are much more effective by selecting individuals with a central position in the opinion network as compared to those grouped in a community or connected professionally. Our findings provide insight into network-based interventions to increase vaccine confidence and demand during an ongoing epidemic.

Sustainable development in brand loyalty: Exploring the dynamics of corporate social responsibility, customer attitudes, and emotional contagion

AbstractCorporate social responsibility (CSR) significantly bolsters brand loyalty by demonstrating a company's commitment to societal and environmental well‐being. This article investigates the link between brand loyalty based on consumer perception and CSR. The study looks at the effects of unfavorable perceptions of CSR initiatives and how emotional contagion affects brand loyalty. The results emphasize the importance of CSR and how it influences consumer attitudes toward brands. To this end, this study particularly targeted young consumers for adopting a sample of this study. The findings reveal that brand disloyalty can be caused by negative attitudes toward a company's CSR initiatives, and brand perception can amplify this effect. The study emphasizes how crucial it for businesses is to demonstrate dedication to sustainability and environmental stewardship to sustainable environmentally conscious clients. However, the geographical sampling size and sampling methods are the research's limitations, which should be considered by the researchers in the future for additional validation and contribution.

Calming the Storm: How Non-Negative Messages From Fellow Consumers Can Dispel Negativity in a Social Media Firestorm

This research explores the potential of non-negative consumer messages to counteract negativity in social media firestorms through emotional contagion. 1,186 tweets were examined in response to a McDonald’s Japan service issue, revealing that non-negative messages tend to align emotionally with preceding messages. This suggests a temporary mitigation of negativity. Investigating emotional contagion within social media firestorms challenged the prevailing notion of negativity bias, indicating a focus on maintaining a positive affective state. Practical implications suggest organizations should monitor and acknowledge non-negative messages during crises to identify advocates and gain insights into subnetwork impact. Incorporating elements from contagious non-negative posts in responses can help mitigate reputational damage. This research contributes to a deeper understanding of emotional contagion dynamics in social media firestorms, aiding organizations in managing their online reputation during crises.

Contagion dynamics on a compound model

In the real world, a single homogeneous or heterogeneous network is very rare, so we consider a compound model which contains two different types of subnetworks. The compound model may be applied to analyze the contagion dynamics of two different communities where they have a probability to communicate. The global infection threshold is obtained, and many factors affecting spread of epidemics are analyzed, including subnetwork density, subnetwork size, connecting probability and the maximum degree. An interesting phenomenon is that we find the transition points of the global threshold from increasing to decreasing, which was not mentioned in previous work. Therefore, we give many measures to improve infection threshold and prevent the spread of epidemics. The experimental results confirm the conclusions of epidemic analysis.

Causal interactions and financial contagion among the BRICS stock markets under rare events: a Liang causality analysis

PurposeThe purpose of this paper is to investigate dynamics of causal interactions and financial risk contagion among BRICS stock markets under rare events.Design/methodology/approachTwo methods are adopted: The new causal inference technique, namely, the Liang causality analysis based on information flow theory and the dynamic causal index (DCI) are used to measure the financial risk contagion.FindingsThe causal relationships among the BRICS stock markets estimated by the Liang causality analysis are significantly stronger in the mid-periods of rare events than in the pre- and post-periods. Moreover, different rare events have heterogeneous effects on the causal relationships. Notably, under rare events, there is almost no significant Liang's causality between the Chinese and other four stock markets, except for a few moments, indicating that the former can provide a relatively safe haven within the BRICS. According to the DCIs, the causal linkages have significantly increased during rare events, implying that their connectivity becomes stronger under extreme conditions.Practical implicationsThe obtained results not only provide important implications for investors to reasonably allocate regional financial assets, but also yield some suggestions for policymakers and financial regulators in effective supervision, especially in extreme environments.Originality/valueThis paper uses the Liang causality analysis to construct the causal networks among BRICS stock indices and characterize their causal linkages. Furthermore, the DCI derived from the causal networks is applied to measure the financial risk contagion of the BRICS countries under three rare events.