According to scientists we all are experiencing "Extreme Events" every day. Even by being part of a traffic jam. In order to better understand these extreme events, the data of over one hundred gauging stations along the "Kolner Ring" a Freeway around the city of Cologne is being collected and analyzed. To help the scientists to understand their data, but also to help the car drivers of Cologne to decide which direction to take on the freeway in case of a traffic congestion, we developed an application that displays the data and gives users the possibility to calculate their journey time. Traffic jams on the 'Kolner Ring' are recurring incidents and allow relative exact predictions for prospective congestions. The gathered data shows the concentration and speed of the traffic at any time, at every kilometer and every track of the freeway.

Extreme events

Exploring the dynamic mechanism of rumor reversal in several public emergency events can support managers to control and guild the spread of public opinion. In this study a two-stage rumor model, namely, susceptible, positive, negative, and recovered state (SPNR) is built to analyze rumor spread and reversal of rumors regarding emergencies on Weibo. The model considers the hysteresis of official news and public swing mentality based on epidemic models. To validate the proposed model, we compare the simulated data and the spread curve of rumor reversal on the basis of the real event “Chongqing bus plunging into the river”. The result shows the proposed model well simulates this real situation of news breakout, confirming the change of rumor-infected rate and probability of rumor disseminators transforming into positive public opinion disseminators, and the time of official statement for truth affecting rumor propagation in varying degrees. To effectively control and dispel rumors, proper guidance of official statement or authority release for truth in the public event is necessary.

Dynamic rumor spreading of public opinion reversal on Weibo based on a two-stage SPNR model

We modeled the mobility of mobile phone users in order to study the fundamental spreading patterns that characterize a mobile virus outbreak. We find that although Bluetooth viruses can reach all susceptible handsets with time, they spread slowly because of human mobility, offering ample opportunities to deploy antiviral software. In contrast, viruses using multimedia messaging services could infect all users in hours, but currently a phase transition on the underlying call graph limits them to only a small fraction of the susceptible users. These results explain the lack of a major mobile virus breakout so far and predict that once a mobile operating system's market share reaches the phase transition point, viruses will pose a serious threat to mobile communications.

http://absimage.aps.org/image/MAR09/MWS_MAR09-2008-002783.pdf

Understanding the spreading patterns of mobile phone viruses

In this work, we present a methodology to identify COVID-19 spreaders using the analysis of the relationship between socio-cultural and economic characteristics with the number of infections and deaths caused by the COVID-19 virus in different countries. For this, we analyze the information of each country using the complex networks approach, specifically by analyzing the spreaders countries based on the separator set in 5-layer multiplex networks. The results show that, we obtain a classification of the countries based on their numerical values in socioeconomics, population, Gross Domestic Product (GDP), health and air connections; where, in the spreader set there are those countries that have high, medium or low values in the different characteristics; however, the aspect that all the countries belonging to the separator set share is a high value in air connections.

/pdf/identification-of-covid-19-spreaders-using-multiplex-1uwzmlnpwd.pdf

Identification of COVID-19 Spreaders Using Multiplex Networks Approach

Recommendations for linking climate change adaptation and disaster risk reduction in urban coastal zones: Lessons from East London, South Africa

In complex networks, identifying influential spreader is of great significance for improving the reliability of networks and ensuring the safe and effective operation of networks. Nowadays, it is widely used in power networks, aviation networks, computer networks, and social networks, and so on. Traditional centrality methods mainly include degree centrality, closeness centrality, betweenness centrality, eigenvector centrality, k-shell, etc. However, single centrality method is one-sided and inaccurate, and sometimes many nodes have the same centrality value, namely the same ranking result, which makes it difficult to distinguish between nodes. According to several classical methods of identifying influential nodes, in this paper we propose a novel method that is more full-scaled and universally applicable. Taken into account in this method are several aspects of node's properties, including local topological characteristics, central location of nodes, propagation characteristics, and properties of neighbor nodes. In view of the idea of the multi-attribute decision-making, we regard the basic centrality method as node's attribute and use the entropy weight method to weigh different attributes, and obtain node's combined centrality. Then, the combined centrality is applied to the gravity law to comprehensively identify influential nodes in networks. Finally, the classical susceptible-infected-recovered (SIR) model is used to simulate the epidemic spreading in six real-society networks. Our proposed method not only considers the four topological properties of nodes, but also emphasizes the influence of neighbor nodes from the aspect of gravity. It is proved that the new method can effectively overcome the disadvantages of single centrality method and increase the accuracy of identifying influential nodes, which is of great significance for monitoring and controlling the complex networks.

Identifying influential spreaders in complex networks based on entropy weight method and gravity law

Testing is one of the most effective means to manage the COVID-19 pandemic. However, there is an upper bound on daily testing volume because of limited healthcare staff and working hours, as well as different testing methods, such as random testing and contact-tracking testing. In this study, a network-based epidemic transmission model combined with a testing mechanism was proposed to study the role of testing in epidemic control. The aim of this study was to determine how testing affects the spread of epidemics and the daily testing volume needed to control infectious diseases. We simulated the epidemic spread process on complex networks and introduced testing preferences to describe different testing strategies. Different networks were generated to represent social contact between individuals. An extended susceptible-exposed-infected-recovered (SEIR) epidemic model was adopted to simulate the spread of epidemics in these networks. The model establishes a testing preference of between 0 and 1; the larger the testing preference, the higher the testing priority for people in close contact with confirmed cases. The numerical simulations revealed that the higher the priority for testing individuals in close contact with confirmed cases, the smaller the infection scale. In addition, the infection peak decreased with an increase in daily testing volume and increased as the testing start time was delayed. We also discovered that when testing and other measures were adopted, the daily testing volume required to keep the infection scale below 5% was reduced by more than 40% even if other measures only reduced individuals’ infection probability by 10%. The proposed model was validated using COVID-19 testing data. Although testing could effectively inhibit the spread of infectious diseases and epidemics, our results indicated that it requires a huge daily testing volume. Thus, it is highly recommended that testing be adopted in combination with measures such as wearing masks and social distancing to better manage infectious diseases. Our research contributes to understanding the role of testing in epidemic control and provides useful suggestions for the government and individuals in responding to epidemics.

/pdf/a-network-based-model-to-explore-the-role-of-testing-in-the-4eiz49tgve.pdf

A network-based model to explore the role of testing in the epidemiological control of the COVID-19 pandemic.

Information dissemination has drawn increasing attention of scholars and the government for its importance to disaster response. Understanding the dynamics of information dissemination can help the government disseminate information effectively. Considering people tend to share information with the intimate under disaster, social distance which measures the intimacy between individuals is introduced in this paper. An information dissemination model with preference selection based on social distance is also proposed. Through extensive simulations, we discover the information dissemination process will be suppressed if people prefer disseminating information to individuals with short social distance. In order to facilitate the information dissemination, award to spreaders is found useful for its significant acceleration in dissemination speed. In addition, we examine the efficiency of strategies the government adopts to disseminate information. Comparing to inform-way (q fraction of nodes informed in descending order of H-index), the government is recommended to disseminate information by broadcast-way (all nodes accepting information with a probability λ b ). Our work contributes to understanding the dynamics of information dissemination, and provides the government with suggestions to disseminate information effectively under disaster.

Modeling the dynamics of information dissemination under disaster

Human settlements are embedded in the traffic networks with hierarchical structure. In order to understand the spreading mechanism of infectious disease and deploy control measures, the susceptible-infected-removed spreading process is studied with agents moving globally on the hierarchical geographic network, taking into account agents' preference for node layers and memory of initial nodes. We investigate the spreading behavior in the case of global infection under different scenarios, including different directions of human flow, different locations of infection source, and different moving behaviors of agents between each layer. Based on the analysis above, we propose screening strategies based on layer rank and moving distance, and compare their effects on delaying epidemic spreading. We find that in the case of global infection, infection spreads faster in high layers than in low layers, and early infection in high layers and moving to high layers both accelerate epidemic spreading. Travel of high-layer and low-layer residents have different effects on accelerating epidemic spreading, and moving between high and low layers increases the peak value of new infected cases more than moving in the same layer or between adjacent layers. Infection in intermediate nodes enhances the effects of the moving of low-layer residents more than the moving of high-layer residents on accelerating epidemic spreading. For screening measures, improving the success rate is more effective on delaying epidemic spreading than expanding the screening range. With the same number of moves screened, screening moves into or out of high-layer nodes combined with screening moves between subnetworks has better results than only screening moves into or out of high-layer nodes, and screening long-distance moves has the worst results when the screening range is small, but it achieves the best results in reducing the peak value of new infected cases when the screening range is large enough. This paper probes into the spreading process and control measures under different scenarios on the hierarchical geographical network, and is of great significance for epidemic control in the real world.

Dynamics and control strategies of infectious disease under different scenarios on hierarchical geographical networks

Individuals' preventive measures, as an effective way to suppress epidemic transmission and to protect themselves from infection, have attracted much academic concern, especially during the COVID-19 pandemic In this paper, a reinforcement learning-based model is proposed to explore individuals' effective preventive measures against epidemics Through extensive simulations, we find that the cost of preventive measures influences the epidemic transmission process significantly The infection scale increases as the cost of preventive measures grows, which means that the government needs to provide preventive measures with low cost to suppress the epidemic transmission In addition, the effective preventive measures vary from individual to individual according to the social contacts Individuals who contact with others frequently in daily life are highly recommended to take strict preventive measures to protect themselves from infection, while those who have little social contacts do not need to take any measures considering the inevitable cost Our research contributes to exploring the effective measures for individuals, which can provide the government and individuals useful suggestions in response to epidemics © 2021 Chinese Physical Society and IOP Publishing Ltd

https://iopscience.iop.org/article/10.1088/1674-1056/abcfa5/pdf

Ya-Peng Cui

Papers

Identifying influential spreaders in complex networks based on entropy weight method and gravity law

A network-based model to explore the role of testing in the epidemiological control of the COVID-19 pandemic.

Modeling the dynamics of information dissemination under disaster

Dynamics and control strategies of infectious disease under different scenarios on hierarchical geographical networks

Exploring individuals' effective preventive measures against epidemics through reinforcement learning