Study on Infection Probabilities of COVID-19 by SIR Network

Severe Acute Respiratory Syndrome Coronavirus 2 Related Diseases (COVID-19) is now one of the most challenging and concerning epidemics, which has been affecting the world so much. After that, countries around the world have been actively developing vaccines to deal with the sudden disease. How to carry out more efficient epidemic prevention has also become a problem of our concern. Unlike traditional SIR disease transmission models, network percolation has unique advantages in disease immune modelling, which makes it closer to reality in the simulation. This article introduces the study of SIR percolation network on infection probabilities of COVID-19, and proposes a method to preventing the spread of disease. © 2022 IEEE.

Superhighways and roads of multivariate time series shock transmission: Application to cryptocurrency, carbon emission and energy prices

Inferring the heterogeneous connection pattern of a networked system of multivariate time series observations is a key issue. In finance, the topological structure of financial connectedness in a network of assets can be a central tool for risk measurement. Against this, we propose a topological framework for variance decomposition analysis of multivariate time series in time and frequency domains. We build on the network representation of time–frequency generalized forecast error variance decomposition (GFEVD), and design a method to partition its maximal spanning tree into two components: (a) superhighways, i.e. the infinite incipient percolation cluster, for which nodes with high centrality dominate;(b) roads, for which low centrality nodes dominate. We apply our method to study the topology of shock transmission networks across cryptocurrency, carbon emission and energy prices. Results show that the topologies of short and long run shock transmission networks are starkly different, and that superhighways and roads considerably vary over time. We further document increased spillovers across the markets in the aftermath of the COVID-19 outbreak, as well as the absence of strong direct linkages between cryptocurrency and carbon markets. © 2023 Elsevier B.V.

The impact of COVID-19 on the international energy trade network centrality and community structures

Global trade including energy trade is expected to suffer a significant contraction as a result of the COVID-19 pandemic. In this paper, we try to measure the impact of the COVID-19 pandemic on the national status and international energy trade patterns. We apply the networks theory to quantify the dynamic process of the international energy trade network in the year 2018–2020, deriving the centrality to capture both national economic status and its topologic characteristics. Under the COVID-19, multilateral energy trade was blocked, thereby some resource-exporting countries show a downward rank of the centrality, and the opposite situation is in higher levels of economic development. By using the community detection method, we also found that new small communities detached from communities that formed before the COVID-19, but geographical related patterns of international energy trade network communities were not affected by the COVID-19 pandemic.

Optimal Bond Percolation in Networks by a Fast-Decycling Framework

Keeping a physical distance and creating social bubbles are popular measures that have been implemented to prevent infection and slow transmission of COVID-19. Such measures aim to reduce the risk of infection by decreasing the interactions among social networks. This, theoretically, corresponds to the optimal bond percolation (OBP) problem in networks, which is the problem of finding the minimum set of edges whose removal or deactivation from a network would dismantle it into isolated sub-components at most size C. To solve the OBP problem, we proposed a fast-decycling framework composed of three stages: (1) recursively removes influential edges from the 2-core of the network, (2) breaks large trees, and (3) reinserts the unnecessarily removed edges through an explosive percolation process. The proposed approaches perform better than existing OBP algorithms on real-world networks. Our results shed light on the faster design of a more practical social distancing and social bubble policy. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Percolation of temporal hierarchical mobility networks during COVID-19.

Percolation theory is essential for understanding disease transmission patterns on the temporal mobility networks. However, the traditional approach of the percolation process can be inefficient when analysing a large-scale, dynamic network for an extended period. Not only is it time-consuming but it is also hard to identify the connected components. Recent studies demonstrate that spatial containers restrict mobility behaviour, described by a hierarchical topology of mobility networks. Here, we leverage crowd-sourced, large-scale human mobility data to construct temporal hierarchical networks composed of over 175 000 block groups in the USA. Each daily network contains mobility between block groups within a Metropolitan Statistical Area (MSA), and long-distance travels across the MSAs. We examine percolation on both levels and demonstrate the changes of network metrics and the connected components under the influence of COVID-19. The research reveals the presence of functional subunits even with high thresholds of mobility. Finally, we locate a set of recurrent critical links that divide components resulting in the separation of core MSAs. Our findings provide novel insights into understanding the dynamical community structure of mobility networks during disruptions and could contribute to more effective infectious disease control at multiple scales. This article is part of the theme issue 'Data science approaches to infectious disease surveillance'.

A novel method for the 3D inhomogeneous percolation on the Lattice with its application

This article anticipates the puzzle of acquiring a three-dimensional inhomogeneous site percolation on an irregular Bethe Lattice (TDIBL). In this work, we explore a TDIBL with the intent to realize the right tradeoff among different percolating variates, namely, cluster size distribution (CSD), critical occupation probability (COP), percolating probability (PP), and mean cluster size (MCS). The variates results are acquired using the generating function (GF) and generalized recursive approaches (GRA). The findings revealed that, for inhomogeneous site percolation (ISP) on the proposed model, the high fraction of probabilities (occupation and distribution probabilities) boosts the intensity of the process, which will enlarge the mean degree of the system in the percolation process. Moreover, numerical simulation and sensitivity analysis will also enhance our understanding of the percolating process via 2D and 3D. Their corresponding shape profiles of earned findings are drawn to perceive their dynamics amongst the options of entailed parameters. Furthermore, exhausting the above scheme, we discuss the transmission behavior of the novel corona-virus 2019 (COVID-19) and present particular disease-control schemes courtesy of groups with numerous infection probabilities and find the effect according to the age distribution. We recognize that this endeavor is timely, that it will be about curiosity, and that it will include scientists working with diverse percolation approaches.

Analysis of global log trade network structure and crisis propagation simulation

【Objective】The deepening of economic globalization has made trade relations between countries closer. To a certain extent, the speed and breadth of the spread of crises, such as political and economic crises in the trade network has increased. Clarifying the global log trade network structure and the path of crisis propagation can help countries avoid trade risks and optimize the log trade structure.【Method】This paper used social network analysis method and bootstrap percolation model to analyze the structural characteristics of the global log trade network in 2018 from the perspective of the whole⁃part⁃individual, and then simulated the impact of the global log trade network when countries experience trade crises under different scenarios.【Result】Results show that the global log trade network is loose as a whole while close for some parts with there being differences in the centrality of different countries. The “grouping” phenomenon for core countries makes the “club” effect in the network relatively pronounced. The impact of trade crises in different countries in the network can generally be divided into four categories, namely “fast and wide”, “fast and narrow”, “slow and wide”, and “slow and narrow”. Log import⁃dependent and export⁃dependent countries have different characteristics in terms of crisis transmission, and log important⁃dependent countries show a strong crisis influence. The influence of a country’s crisis depends on the country’s network structure and its trade structure. A bootstrap percolation model can effectively simulate the process of crisis propagation in the log trade network, with an approximately similar crisis propagation process. Meanwhile, the degree of influence on the network is different under different thresholds. A country with a higher dependence on trade is more likely to be affected by the crisis. 【 Conclusion】 For this reason, countries should focus on building more stable trade relations from an overall perspective, and continuously reduce the log trade dependence on a single country to diversify risks and optimize the global log trading network. © 2022 Nanjing Forestry University.

Evaluating the Structural Robustness of Large-Scale Emerging Industry with Blurring Boundaries.

The present large-scale emerging industry evolves into a form of an open system with blurring boundaries. However, when complex structures with numerous nodes and connections encounter an open system with blurring boundaries, it becomes much more challenging to effectively depict the structure of an emerging industry, which is the precondition for robustness evaluation. Therefore, this study proposes a novel framework based on a data-driven percolation process and complex network theory to depict the network skeleton and thus evaluate the structural robustness of large-scale emerging industries. The empirical data we used are actual firm-level transaction data in the Chinese new energy vehicle industry in 2019, 2020, and 2021. We applied our method to explore the transformation of structural robustness in the Chinese new energy vehicle industry in pre-COVID (2019), under-COVID (2020), and post-COVID (2021) eras. We unveil that the Chinese new energy vehicle industry became more robust against random attacks in the post-COVID era than in pre-COVID.

Risk diffusion of international oil trade cuts: A network-based dynamics model

International trade relations between countries have enabled the integration of global oil trade while also creating a platform for the risk diffusion of oil supply cuts. The global energy supply shortages sparked by the Covid-19 pandemic has made the problem even more pronounced. This study develops a network-based dynamics model based on the modified bootstrap percolation theory to simulate the cascading diffusion of oil supply shortages. We assess the destructive effects of exporting countries’ cuts in oil exports on global oil trade relations and the vulnerability of importing countries when they encounter oil supply shortages. The cascading diffusion of oil supply breakdown occurs less frequently from 2015 to 2019, proving that the entire network’s antirisk capacity strengthens over time. The coronavirus pandemic has impaired the robustness of the oil trade network in 2020. OPEC’s export influence has continued to decline in recent years. The diversified evolution of the oil supply is conducive to the stability of the oil trade economy. This paper analyzes the risk propagation mechanism in the global oil trade and conducts a case study. The results have a specific early warning effect on regional oil supply risks.

Entre « universalisme » et « localisme », les degrés de percolation des standards SPHÈRE

While localization and decolonization are two of the current paradigms in the humanitarian field, we question the relevance of the universal SPHERE standards. The purpose of this article is to offer a reflection on the applications of these standards, on how they were received by humanitarian actors and on the tensions that emerge from both their application and their reception. We were able to identify three levels of percolation and resistance to SPHERE standards. Our results point to some inherent contradictions between standards and localization in the paradigm of decolonization of contents and underlying intents, recently intensified by the Covid-19 crisis. © 2022 Canadian Association for the Study of International Development (CASID).

EXTRACTING REAL SOCIAL INTERACTIONS FROM A DEBATE OF COVID-19 POLICIES ON TWITTER: THE CASE OF MEXICO

Twitter is a popular social medium for sharing opinions and engaging in topical debates, yet presents a wide spread of misinformation, especially in political debates, from bots and adversarial attacks. The current state-of-the-art methods for detecting humans and bots in Twitter often lack generalizability beyond English. Here, a language-agnostic method to detect real users and their interactions by leveraging network topology from retweets is presented. To that end, the chosen topic is COVID-19 policies in Mexico, which has been considered by users as polemic. Two kinds of network are built: a directed network of retweets;and the co-event network, where a non-directed link between two users exists if they have retweeted the same post in a given time window (projection of a bipartite network). Then, single node properties of these networks, such as the clustering coefficient and the degree, are studied. Three kinds of users are observed: some with a high clustering coefficient but a very small degree, a second group with zero clustering coefficient and a variable degree, and a third group in which the clustering coefficient as a function of the degree decays as a power law. This third group represents ∼2% of the users and is characteristic of dynamical networks with feedback. The latter seems to represent strongly interacting followers/followed in a real social network as confirmed by an inspection of such nodes. A percolation analysis of the resulting co-retweet and co-hashtag network reveals the relevance of such weak links, typical of real social human networks. The presented methods are simple to implement in other social media platforms and can be used to mitigate misinformation and conflicts. [ FROM AUTHOR] Copyright of Advances in Complex Systems is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Peak fraction of infected in epidemic spreading for multi-community networks

One of the most effective strategies to mitigate the global spreading of a pandemic (e.g. coronavirus disease 2019) is to shut down international airports. From a network theory perspective, this is since international airports and flights, essentially playing the roles of bridge nodes and bridge links between countries as individual communities, dominate the epidemic spreading characteristics in the whole multi-community system. Among all epidemic characteristics, the peak fraction of infected, I-ma(x), is a decisive factor in evaluating an epidemic strategy given limited capacity of medical resources but is seldom considered in multi-community models. In this article, we study a general two-community system interconnected by a fraction r of bridge nodes and its dynamic properties, especially I-max, under the evolution of the susceptibleinfected-recovered model. Comparing the characteristic time scales of different parts of the system allows us to analytically derive the asymptotic behaviour of I-max with r, as r -> 0, which follows different power-law relations in each regime of the phase diagram. We also detect crossovers when I-max changes from one power law to another, crossing different power-law regimes as driven by r. Our results enable a better prediction of the effectiveness of strategies acting on bridge nodes, denoted by the power-law exponent epsilon(I) as in I-max proportional to r(1/epsilon I).

A Computational Study of COVID-19 Detection using Colorimetric Plasmonic Sensors

Traditional molecular techniques for COVID-19 viral detection are time-consuming and can exhibit a high probability of false negatives. In this work, we present a computational study of COVID-19 detection using plasmonic gold nanoparticles. The resonance wavelength of a COVID-19 virion was recently estimated to be in the near-infrared region. By engineering gold nanospheres to bind with the outer surface of the COVID-19 virus specifically, the resonance frequency can be shifted to the visible range (380 nm-700 nm). Moreover, we show that broadband absorption will emerge in the visible spectrum when the virus is partially covered with gold nanoparticles at a certain percentage. This broadband absorption can be used to guide the development of an efficient and accurate colorimetric plasmon sensor for COVID-19 detection. © 2021 IEEE.

Resilience Analysis of Australian Electricity and Gas Transmission Networks

Given they are two critical infrastructure areas, the security of electricity and gas networks is highly important due to potential multifaceted social and economic impacts. Unexpected errors or sabotage can lead to blackouts, causing a significant loss for the public, businesses, and governments. Climate change and an increasing number of consequent natural disasters (e.g., bushfires and floods) are other emerging network resilience challenges. In this paper, we used network science to examine the topological resilience of national energy networks with two case studies of Australian gas and electricity networks. To measure the fragility and resilience of these energy networks, we assessed various topological features and theories of percolation. We found that both networks follow the degree distribution of power-law and the characteristics of a scale-free network. Then, using these models, we conducted node and edge removal experiments. The analysis identified the most critical nodes that can trigger cascading failure within the network upon a fault. The analysis results can be used by the network operators to improve network resilience through various mitigation strategies implemented on the identified critical nodes.

Percolation clusters of territorial inequality due to the spread of the coronavirus infection

Due to the influence of the coronavirus infection, the issues of regional governance and territorial planning have been included in the urgent agenda of territorial development for two years in a row. Within the framework of this issue, a number of challenges of territorial administration are already being investigated. One of them is maintaining the achieved level of economic development and replenishing problematic aspects due to the spread of the coronavirus infection, which seriously affected all economic processes in 2020. The article reveals the management structure of Sverdlovsk region (Russia) and analyses the main socio-economic indicators of the region's development. The results of the analysis contribute to identifying the urgent problem of regional governance and territorial planning of Sverdlovsk region, i.e. the deepening inter-territorial inequality caused by an unfavourable epidemiological situation. The authors propose management solutions aimed at improving regional governance and territorial planning of Sverdlovsk region in the context of the identified problem.

Percolation across households in mechanistic models of non-pharmaceutical interventions in SARS-CoV-2 disease dynamics.

Since the emergence of the novel coronavirus disease 2019 (COVID-19), mathematical modelling has become an important tool for planning strategies to combat the pandemic by supporting decision-making and public policies, as well as allowing an assessment of the effect of different intervention scenarios. A proliferation of compartmental models were developed by the mathematical modelling community in order to understand and make predictions about the spread of COVID-19. While compartmental models are suitable for simulating large populations, the underlying assumption of a well-mixed population might be problematic when considering non-pharmaceutical interventions (NPIs) which have a major impact on the connectivity between individuals in a population. Here we propose a modification to an extended age-structured SEIR (susceptible-exposed-infected-recovered) framework, with dynamic transmission modelled using contact matrices for various settings in Brazil. By assuming that the mitigation strategies for COVID-19 affect the connections among different households, network percolation theory predicts that the connectivity among all households decreases drastically above a certain threshold of removed connections. We incorporated this emergent effect at population level by modulating home contact matrices through a percolation correction function, with the few additional parameters fitted to hospitalisation and mortality data from the city of São Paulo. Our model with percolation effects was better supported by the data than the same model without such effects. By allowing a more reliable assessment of the impact of NPIs, our improved model provides a better description of the epidemiological dynamics and, consequently, better policy recommendations.

Mobility strategies based on percolation theory to avoid disease spread: COVID-19

Human mobility is an important factor in the spatial propagation of infectious diseases. On the other hand, the control strategies based on mobility restrictions are generally unpopular and costly. These high social and economic costs make it very important to design global protocols where the cost is minimized and effects maximized. In this work, we calculate the percolation threshold of the spread in a network of a disease. In particular, we found the number of roads to close and regions to isolate in the Puebla State, Mexico, to avoid the global spread of COVID-19. Computational simulations taking into account the proposed strategy show a potential reduction of 94% of infections. This methodology can be used in broader and different areas to help in the design of health policies.

Machine learning predictions of COVID-19 second wave end-times in Indian states.

The estimate of the remaining time of an ongoing wave of epidemic spreading is a critical issue. Due to the variations of a wide range of parameters in an epidemic, for simple models such as Susceptible-Infected-Removed (SIR) model, it is difficult to estimate such a time scale. On the other hand, multidimensional data with a large set attributes are precisely what one can use in statistical learning algorithms to make predictions. Here we show, how the predictability of the SIR model changes with various parameters using a supervised learning algorithm. We then estimate the condition in which the model gives the least error in predicting the duration of the first wave of the COVID-19 pandemic in different states in India. Finally, we use the SIR model with the above mentioned optimal conditions to generate a training data set and use it in the supervised learning algorithm to estimate the end-time of the ongoing second wave of the pandemic in different states in India.

Household bubbles and COVID-19 transmission: insights from percolation theory.

In the era of social distancing to curb the spread of COVID-19, bubbling is the combining of two or more households to create an exclusive larger group. The impact of bubbling on COVID-19 transmission is challenging to quantify because of the complex social structures involved. We developed a network description of households in the UK, using the configuration model to link households. We explored the impact of bubbling scenarios by joining together households of various sizes. For each bubbling scenario, we calculated the percolation threshold, that is, the number of connections per individual required for a giant component to form, numerically and theoretically. We related the percolation threshold to the household reproduction number. We find that bubbling scenarios in which single-person households join with another household have a minimal impact on network connectivity and transmission potential. Ubiquitous scenarios where all households form a bubble are likely to lead to an extensive transmission that is hard to control. The impact of plausible scenarios, with variable uptake and heterogeneous bubble sizes, can be mitigated with reduced numbers of contacts outside the household. Bubbling of households comes at an increased risk of transmission; however, under certain circumstances risks can be modest and could be balanced by other changes in behaviours. This article is part of the theme issue 'Modelling that shaped the early COVID-19 pandemic response in the UK'.

Epidemics, the Ising-model and percolation theory: A comprehensive review focused on Covid-19.

We revisit well-established concepts of epidemiology, the Ising-model, and percolation theory. Also, we employ a spin S = 1/2 Ising-like model and a (logistic) Fermi-Dirac-like function to describe the spread of Covid-19. Our analysis show that: (i) in many cases the epidemic curve can be described by a Gaussian-type function; (ii) the temporal evolution of the accumulative number of infections and fatalities follow a logistic function; (iii) the key role played by the quarantine to block the spread of Covid-19 in terms of an interacting parameter between people. In the frame of elementary percolation theory, we show that: (i) the percolation probability can be associated with the probability of a person being infected with Covid-19; (ii) the concepts of blocked and non-blocked connections can be associated, respectively, with a person respecting or not the social distancing. Yet, we make a connection between epidemiological concepts and well-established concepts in condensed matter Physics.