Spreading processes with mutations over multilayer networks.

A key scientific challenge during the outbreak of novel infectious diseases is to predict how the course of the epidemic changes under countermeasures that limit interaction in the population. Most epidemiological models do not consider the role of mutations and heterogeneity in the type of contact events. However, pathogens have the capacity to mutate in response to changing environments, especially caused by the increase in population immunity to existing strains, and the emergence of new pathogen strains poses a continued threat to public health. Further, in the light of differing transmission risks in different congregate settings (e.g., schools and offices), different mitigation strategies may need to be adopted to control the spread of infection. We analyze a multilayer multistrain model by simultaneously accounting for i) pathways for mutations in the pathogen leading to the emergence of new pathogen strains, and ii) differing transmission risks in different settings, modeled as network layers. Assuming complete cross-immunity among strains, namely, recovery from any infection prevents infection with any other (an assumption that will need to be relaxed to deal with COVID-19 or influenza), we derive the key epidemiological parameters for the multilayer multistrain framework. We demonstrate that reductions to existing models that discount heterogeneity in either the strain or the network layers may lead to incorrect predictions. Our results highlight that the impact of imposing/lifting mitigation measures concerning different contact network layers (e.g., school closures or work-from-home policies) should be evaluated in connection with their effect on the likelihood of the emergence of new strains.

Multilayer networks in the frequency domain: Measuring extreme risk connectedness of Chinese financial institutions

We propose multilayer networks in the frequency domain, including the short-term, medium-term, and long-term layers, to investigate the extreme risk connectedness among financial institutions. Using the conditional autoregressive value at risk (CAViaR) tool to measure the extreme risk of financial institutions, we construct extreme risk networks and inter-sector extreme risk networks of 36 Chinese financial institutions through the proposed approach. We observe that the extreme risk connectedness across financial institutions is heterogeneous in the short-, medium-, and long-term. In general, the long-term connectedness among financial institutions rises sharply during times of financial stress, such as the 2015 Chinese stock market turbulence and the 2020 COVID-19 pandemic. Moreover, we note that the insurers are key players in driving the inter-sector extreme risk networks, because the inter-sector systemic importance of insurance institutions is dominant. Finally, our conclusions provide valuable information for regulators to prevent systemic risk. © 2023 Elsevier B.V.

Flight from COVID-19: Multiscale and Multilayer Analyses of the Epidemic-Induced Network Adaptations

Pandemic waves are worldwide disasters that can create long-term disruptions in critical industries. Airline transportation is a crucial industry for the US economy. We empirically study how vital industries such as airlines adapt in response to massive disasters like COVID-19. This paper investigates the changes in the network of the US domestic flights caused by the start of the COVID-19 epidemic. Using a novel dataset, we examine the epidemic-induced network adaptations in the US airline industry and quantify the strength of the flight network's response to the epidemic network activation. We find that the overall disruption in the flight network is large in size. When considering a natural multilayer structure of the flight network represented by airlines, we find that the COVID-19 epidemic changes the multilayer structure, and some layers are more resilient than others. © 2023, This is a U.S. Government work and not under copyright protection in the US;foreign copyright protection may apply.

Influence of Information Blocking on the Spread of Virus in Multilayer Networks.

In this paper, we present the model of the interaction between the spread of disease and the spread of information about the disease in multilayer networks. Next, based on the characteristics of the SARS-CoV-2 virus pandemic, we evaluated the influence of information blocking on the virus spread. Our results show that blocking the spread of information affects the speed at which the epidemic peak appears in our society, and affects the number of infected individuals.

Multilayer networks with higher-order interaction reveal the impact of collective behavior on epidemic dynamics.

The ongoing COVID-19 pandemic has inflicted tremendous economic and societal losses. In the absence of pharmaceutical interventions, the population behavioral response, including situational awareness and adherence to non-pharmaceutical intervention policies, has a significant impact on contagion dynamics. Game-theoretic models have been used to reproduce the concurrent evolution of behavioral responses and disease contagion, and social networks are critical platforms on which behavior imitation between social contacts, even dispersed in distant communities, takes place. Such joint contagion dynamics has not been sufficiently explored, which poses a challenge for policies aimed at containing the infection. In this study, we present a multi-layer network model to study contagion dynamics and behavioral adaptation. It comprises two physical layers that mimic the two solitary communities, and one social layer that encapsulates the social influence of agents from these two communities. Moreover, we adopt high-order interactions in the form of simplicial complexes on the social influence layer to delineate the behavior imitation of individual agents. This model offers a novel platform to articulate the interaction between physically isolated communities and the ensuing coevolution of behavioral change and spreading dynamics. The analytical insights harnessed therefrom provide compelling guidelines on coordinated policy design to enhance the preparedness for future pandemics.

Modelling international sovereign risk information spillovers: A multilayer network approach

Applying the TVP-VAR model, we creatively construct multilayer information spillover networks containing return spillover layer, volatility spillover layer and extreme risk spillover layer among 23 countries in the G20 to explore international sovereign risk spillovers. From the perspective of system-level and country-level measures, this article explores the topological structures of static and dynamic multilayer networks. We observe that (i) at the system-level, multilayer measures containing uniqueness edge ratio and average edge overlap show each layer has unique network structures and spillover evolution behavior, especially for dynamic networks. Average connectedness strength shows volatility and extreme risk spillover layers are more sensitive to extreme events. Meanwhile, three layers have highly intertwined and interrelated relations. Notably, their spillovers all show a great upsurge during the crisis (financial and European debt crisis) and the COVID-19 pandemic period. (ii) At the country-level, average overlapping net-strength shows that countries’ roles are different during distinct periods. Multiplex participation coefficient on out-strength indicates we’ll focus on countries with highly heterogeneous connectedness among three layers during the stable period since their underestimated spillovers soar in extreme events or crises. Multilayer networks supply comprehensive information that cannot obtain by single-layer.

From base data to knowledge discovery – A life cycle approach – Using multilayer networks

Analysis of complex data sets to infer/discover meaningful information/knowledge involves (after data collection and cleaning): (i) Modeling the data – an approach for deriving a suitable representation of data for analysis, (ii) translating analysis objectives into computations on the generated model instance;these computations can be as simple as a query or a complex computation (e.g., community detection over multiple layers), (iii) computation of expressions generated – considering efficiency and scalability, and (iv) drill-down of results to understand them clearly. Beyond this, it is also useful to visualize results for easier understanding. Covid-19 visualization dashboard presented in this paper is an example of this. This paper covers the above steps of data analysis life cycle using a representation (or model) that is gaining importance. With complex data sets containing multiple entity types and relationships, an appropriate model to represent the data is important. For these data sets, we first establish the advantages of Multilayer Networks (or MLNs) as a data model. Then we use an entity-relationship based approach to convert the data set into MLNs for a precise representation of the data set. After that, we outline how expected analysis objectives can be translated using keyword-mapping to aggregate analysis expressions. Finally, we demonstrate, through a set of example data sets and objectives, how the expressions corresponding to objectives are evaluated using an efficient decoupling-based approach. Results are further drilled down to obtain actionable knowledge from the data set. Using the widely popular Enhanced Entity Relationship (EER) approach for requirements representation, we demonstrate how to generate EER diagrams for data sets and further generate, algorithmically, MLNs as well as Relational schema for analysis and drill down, respectively. Using communities and centrality for aggregate analysis, we demonstrate the flexibility of the chosen model to support diverse set of objectives. We also show that compared to current analysis approaches, a “decoupling-based” approach using MLNs is more appropriate as it preserves structure as well as semantics of the results and is very efficient. For this computation, we need to derive expressions for each analysis objective using the MLN model. We provide guidelines to translate English queries into analysis expressions based on keywords. Finally, we use several data sets to establish the effectiveness of modeling using MLNs and their analysis using the decoupling approach that has been proposed recently. For coverage, we use different types of MLNs for modeling, and community and centrality computations for analysis. The data sets used are from US commercial airlines, IMDb (a large international movie data set), the familiar DBLP (or bibliography database), and the Covid-19 data set. Our experimental analyses using the identified steps validate modeling, breadth of objectives that can be computed, and overall versatility of the life cycle approach. Correctness of results is verified, where possible, using independently available ground truth. Furthermore, we demonstrate drill-down that is afforded by this approach (due to structure and semantics preservation) for a better understanding and visualization of results.

ModViz: A Modular and Extensible Architecture for Drill-Down and Visualization of Complex Data

Analysis of data sets that may be changing often or in real-time, consists of at least three important synchronized components: i) figuring out what to infer (objectives), ii) analysis or computation of those objectives, and iii) understanding of the results which may require drill-down and/or visualization. There is considerable research on the first two of the above components whereas understanding actionable inferences through visualization has not been addressed properly. Visualization is an important step towards both understanding (especially by non-experts) and inferring the actions that need to be taken. As an example, for Covid-19, knowing regions (say, at the county or state level) that have seen a spike or are prone to a spike in the near future may warrant additional actions with respect to gatherings, business opening hours, etc. This paper focuses on a modular and extensible architecture for visualization of base as well as analyzed data. This paper proposes a modular architecture of a dashboard for user interaction, visualization management, and support for complex analysis of base data. The contributions of this paper are: i) extensibility of the architecture providing flexibility to add additional analysis, visualizations, and user interactions without changing the workflow, ii) decoupling of the functional modules to ease and speed up development by different groups, and iii) supporting concurrent users and addressing efficiency issues for display response time. This paper uses Multilayer Networks (or MLNs) for analysis. To showcase the above, we present the architecture of a visualization dashboard, termed CoWiz++ (for Covid Wizard), and elaborate on how web-based user interaction and display components are interfaced seamlessly with the back-end modules. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Understanding the coevolution of mask wearing and epidemics: A network perspective.

Nonpharmaceutical interventions (NPIs) such as mask wearing can be effective in mitigating the spread of infectious diseases. Therefore, understanding the behavioral dynamics of NPIs is critical for characterizing the dynamics of disease spread. Nevertheless, standard infection models tend to focus only on disease states, overlooking the dynamics of "beneficial contagions," e.g., compliance with NPIs. In this work, we investigate the concurrent spread of disease and mask-wearing behavior over multiplex networks. Our proposed framework captures both the competing and complementary relationships between the dueling contagion processes. Further, the model accounts for various behavioral mechanisms that influence mask wearing, such as peer pressure and fear of infection. Our results reveal that under the coupled disease-behavior dynamics, the attack rate of a disease-as a function of transition probability-exhibits a critical transition. Specifically, as the transmission probability exceeds a critical threshold, the attack rate decreases abruptly due to sustained mask-wearing responses. We empirically explore the causes of the critical transition and demonstrate the robustness of the observed phenomena. Our results highlight that without proper enforcement of NPIs, reductions in the disease transmission probability via other interventions may not be sufficient to reduce the final epidemic size.

Impact of co-evolution of negative vaccine-related information, vaccination behavior and epidemic spreading in multilayer networks

In this paper, we establish a three-layer coupled network model to analyze the co-evolution of negative vaccine-related information, vaccination behavior, and epidemic spread. The three layers are used to represent negative vaccine-related information dissemination, vaccination behavior diffusion and epidemic propagation, respectively. The comprehensive impact of vaccination costs and herd mentality are firstly considered in the diffusion of vaccination behavior. Then, we use the micro-Markov chain (MMC) method to derive the system dynamics equations and the epidemic threshold. The analytical results show that the proportion of efficaciously vaccinated individuals and the topology of the epidemic spread layer both determine the epidemic threshold. Finally, a large number of simulation experiments are conducted to investigate the impact of various factors on the epidemic spread, which include negative vaccine-related information diffusion, rational judgment and herd mentality during the process of vaccination decision, and cost of vaccine. The results demonstrate the role of negative vaccine-related information diffusion and vaccination behavior spread in epidemic control, and may provide some valuable clues for policymakers to formulate appropriate measures to prevent and control epidemics.

A multilayer network model of the coevolution of the spread of a disease and competing opinions

During the COVID-19 pandemic, conflicting opinions on physical distancing swept across social media, affecting both human behavior and the spread of COVID-19. Inspired by such phenomena, we construct a two-layer multiplex network for the coupled spread of a disease and conflicting opinions. We model each process as a contagion. On one layer, we consider the concurrent evolution of two opinions — pro-physical-distancing and anti-physical-distancing — that compete with each other and have mutual immunity to each other. The disease evolves on the other layer, and individuals are less likely (respectively, more likely) to become infected when they adopt the pro-physical-distancing (respectively, anti-physical-distancing) opinion. We develop approximations of mean-field type by generalizing monolayer pair approximations to multilayer networks;these approximations agree well with Monte Carlo simulations for a broad range of parameters and several network structures. Through numerical simulations, we illustrate the influence of opinion dynamics on the spread of the disease from complex interactions both between the two conflicting opinions and between the opinions and the disease. We find that lengthening the duration that individuals hold an opinion may help suppress disease transmission, and we demonstrate that increasing the cross-layer correlations or intra-layer correlations of node degrees may lead to fewer individuals becoming infected with the disease.