基于手机大数据的中国人口迁徙模式及疫情影响研究

新型冠状病毒感染的肺炎(COVID-19)可通过人员接触与流动迅速传播,因此研究人类迁徙和出行模式的变化对疫情防控至关重要.本文基于手机运营商2020年春运及疫情暴发前后连续两个月的全国地级市之间的人口流动数据,运用时序网络分析方法构建人口流动网络拓扑结构指标,并通过引入地理衰减因子提出Spatial-Louvain社团检测算法,研究平时、春运、疫情防控隔离和生产复工四阶段的人口迁徙模式的时空演化规律.研究发现:受各地疫情防控措施影响,武汉封城后全国城市间人口流量急剧下降,并持续至2月中旬.疫情期间人口流动网络结构呈现四阶段的时空演化模式;本文提出的空间网络社团检测算法比传统Louvain算法平均模块度值提高了14%;中国城市分布以经济交互和地理位置为基础,形成了以核心城市为中心,向周边辐射的城市群格局;疫情因素仅能在短暂时间内改变部分城市的城市群归属,当该因素消失或减弱后,城市群能迅速恢复原有格局.     

Dynamical behaviour of an epidemic on complex networks with population mobility

In this paper, we study the dynamical behaviour of an epidemic on complex networks with population mobility. In our model, the number of people on each node is unrestricted as the nodes of the network are considered as cities, communities, and so on. Because people can travel between different cities, we study the effect of a population's mobility on the epidemic spreading. In view of the population's mobility, we suppose that the susceptible individual can be infected by an infected individual in the same city or other connected cities. Simulations are presented to verify our analysis.     

Impact of mobility structure on optimization of small-world networks of mobile agents

In ad hoc wireless networking, units are connected to each other rather than to acentral, fixed, infrastructure. Constructing and maintaining such networks create severaltrade-off problems between robustness, communication speed, power consumption, etc., thatbridges engineering, computer science and the physics of complex systems. In this work, weaddress the role of mobility patterns of the agents on the optimal tuning of a small-worldtype network construction method. By this method, the network is updated periodically andheld static between the updates. We investigate the optimal updating times for differentscenarios of the movement of agents (modeling, for example, the fat-tailed trip distances,and periodicities, of human travel). We find that these mobility patterns affect the powerconsumption in non-trivial ways and discuss how these effects can best be handled.     

Understanding the Impact of Walkability,Population Density,and Population Size on COVID-19 Spread: A Pilot Study of the Early Contagion in the United States

The novel coronavirus disease 2019 (COVID-19) pandemic is an unprecedented global event that has been challenging governments, health systems, and communities worldwide. Available data from the first months indicated varying patterns of the spread of COVID-19 within American cities, when the spread was faster in high-density and walkable cities such as New York than in low-density and car-oriented cities such as Los Angeles. Subsequent containment efforts, underlying population characteristics, variants, and other factors likely affected the spread significantly. However, this work investigates the hypothesis that urban configuration and associated spatial use patterns directly impact how the disease spreads and infects a population. It follows work that has shown how the spatial configuration of urban spaces impacts the social behavior of people moving through those spaces. It addresses the first 60 days of contagion (before containment measures were widely adopted and had time to affect spread) in 93 urban counties in the United States, considering population size, population density, walkability, here evaluated through walkscore, an indicator that measures the density of amenities, and, therefore, opportunities for population mixing, and the number of confirmed cases and deaths. Our findings indicate correlations between walkability, population density, and COVID-19 spreading patterns but no clear correlation between population size and the number of cases or deaths per 100 k habitants. Although virus spread beyond these initial cases may provide additional data for analysis, this study is an initial step in understanding the relationship between COVID-19 and urban configuration.     

Modeling the COVID-19 epidemic and awareness diffusion on multiplex networks

The coronavirus disease 2019(COVID-19)has been widely spread around the world,and the control and behavior dynamics are still one of the important research directions in the world.Based on the characteristics of COVID-19’s spread,a coupled disease-awareness model on multiplex networks is proposed in this paper to study and simulate the interaction between the spreading behavior of COVID-19 and related information.In the layer of epidemic spreading,the nodes can be divided into five categories,where the topology of the network represents the physical contact relationship of the population.The topological structure of the upper network shows the information interaction among the nodes,which can be divided into aware and unaware states.Awareness will make people play a positive role in preventing the epidemic diffusion,influencing the spread of the disease.Based on the above model,we have established the state transition equation,through the microscopic Markov chain approach(MMCA),and proposed the propagation threshold calculation method under the epidemic model.Furthermore,MMCA iteration and the Monte Carlo method are simulated on the static network and dynamic network,respectively.The current results will be beneficial to the study of COVID-19,and propose a more rational and effective model for future research on epidemics.     

Correlation of charge extraction properties and short circuit current in various organic binary and ternary blend photovoltaic devices

Charge extraction properties of various binary and ternary blends of organic photovoltaic devices covering both polymers and small molecules are studied. Due to their bipolar nature, both slow and fast carrier mobilities are identified from the extraction current transient. The equilibrium carrier concentration is also estimated for each of the blend films. The product of the slow carrier mobility and equilibrium concentration spreading two orders of magnitude can be used to estimate the short circuit current density. A good agreement between the estimated and measured short circuit current density is obtained with the accuracy reliant on the estimation of the slowest carrier mobility. This simplistic approach will be very useful to predict the short circuit current density for devices based on new materials.     

Modeling the effects of social impact on epidemic spreading in complex networks

We investigate by mean-field analysis and extensive simulations the effects of social impact on epidemic spreading in various typical networks with two types of nodes: active nodes and passive nodes, of which the behavior patterns are modeled according to the social impact theory. In this study, nodes are not only the media to spread the virus, but also disseminate their opinions on the virus—whether there is a need for certain self-protection measures to be taken to reduce the risk of being infected. Our results indicate that the interaction between epidemic spreading and opinion dynamics can have significant influences on the spreading of infectious diseases and related applications, such as the implementation of prevention and control measures against the infectious diseases.     

基于串口转WiFi的物联网终端远程控制实现方法

随着物联网技术的高速发展,对同一无线局域网内的设备进行控制时,存在传输距离短、可移动性差等缺点。针对此问题,提出了一种物联网终端远程控制的实现方法,采用串口转WiFi模块,通过Socket模式下的透传机制,传统的串口设备能够无线接入到互网络中基于MQTT消息传输协议的服务器上,完成数据的接收和发送,从而使终端设备突破无线通信距离的限制,达到数据交互和远程控制的目的。实验结果表明该方法正确、可靠,可广泛应用于智能家居、工业控制等领域。     

基于节点度信息的自愿免疫模型研究

疾病的广泛传播给人类带来了巨大的损失, 因此抑制疾病的传播非常重要. 本文考虑了个体接种疫苗意愿的差异性, 并结合博弈理论建立了一个基于节点度信息的自愿免疫模型. 理论解析结果证明当感染率超过某个阈值时, 该模型与忽略个体接种意愿差异性的经典模型(Zhang et al 2010 New J. Phys. 12 023015) 传播效果(感染节点数)一样. 继而考虑疫苗永久有效和有效期有限两种情况, 在Barabási-Albert网络中利用SIS传播模型对疾病的传播进程进行了数值模拟, 发现数值模拟结果与理论解析结果非常符合. 实验证明, 当感染耗费和接种疫苗耗费相同时, 该模型比忽略个体接种意愿差异性的经典模型能够更好的抑制疾病的传播, 且感染人数下降比例超过65%, 更重要的是,疫苗有效期越长本文的模型 (与忽略个体接种意愿差异性的经典模型相比)抑制疾病传播效果越好. 关键词： 疾病传播 自愿免疫 接种疫苗倾向 节点度     

推荐重要节点部署防御策略的优化模型

当前网络安全防御策略集中部署于高连接度节点主要有2个方面的不足: 一是高连接度节点在很多场合中并不是网络通信的骨干节点; 二是该类节点对信息的转发和传播并非总是最有效的.针对以上传统部署策略的不足, 改进了恶意病毒程序传播的离散扩散模型并采用中间路径跳数来衡量网络节点的重要程度, 提出了基于介数中心控制力和接近中心控制力模型的重要节点优先推荐部署技术.实验结果显示具有高介数中心控制力和低接近中心控制力的节点相对于传统的高连接度节点无论在无标度网络还是小世界网络均能够对恶意病毒程序的疫情扩散和早期传播速度起到更加有效的抑制作用, 同时验证了网络分簇聚类行为产生的簇团特性也将对恶意程序的传播起到一定的负面影响.     

A random walk evolution model of wireless sensor networks and virus spreading

In this paper, considering both cluster heads and sensor nodes, we propose a novel evolving a network model based on a random walk to study the fault tolerance decrease of wireless sensor networks (WSNs) due to node failure, and discuss the spreading dynamic behavior of viruses in the evolution model. A theoretical analysis shows that the WSN generated by such an evolution model not only has a strong fault tolerance, but also can dynamically balance the energy loss of the entire network. It is also found that although the increase of the density of cluster heads in the network reduces the network efficiency, it can effectively inhibit the spread of viruses. In addition, the heterogeneity of the network improves the network efficiency and enhances the virus prevalence. We confirm all the theoretical results with sufficient numerical simulations.     

Global dynamics of a novel multi-group model for computer worms

In this paper, we study worm dynamics in computer networks composed of many autonomous systems. A novel multigroup SIQR (susceptible-infected-quarantined-removed) model is proposed for computer worms by explicitly considering anti-virus measures and the network infrastructure. Then, the basic reproduction number of worm R0 is derived and the global dynamics of the model are established. It is shown that if R0 is less than or equal to 1, the disease-free equilibrium is globally asymptotically stable and the worm dies out eventually, whereas, if R0 is greater than 1, one unique endemic equilibrium exists and it is globally asymptotically stable, thus the worm persists in the network. Finally, numerical simulations are given to illustrate the theoretical results.     

Impact of heterogeneous human activities on epidemic spreading

Recent empirical observations suggest a heterogeneous nature of human activities. The heavy-tailed inter-event time distribution at the population level is well accepted, while whether the individual acts in a heterogeneous way is still under debate. Motivated by the impact of temporal heterogeneity of human activities on epidemic spreading, this paper studies the susceptible-infected model on a fully mixed population, where each individual acts in a completely homogeneous way but different individuals have different mean activities. Extensive simulations show that the heterogeneity of activities at the population level remarkably affects the speed of spreading, even though each individual behaves regularly. Furthermore, the spreading speed of this model is more sensitive to the change of system heterogeneity compared with the model consisted of individuals acting with heavy-tailed inter-event time distributions. This work refines our understanding of the impact of heterogeneous human activities on epidemic spreading.     

Modelling the effect of heterogeneous vaccination on metapopulation epidemic dynamics

Vaccination as an epidemic control strategy has a significant effect on epidemic spreading. In this paper, we propose a novel epidemic spreading model on metapopulation networks to study the impact of heterogeneous vaccination on epidemic dynamics, where nodes represent geographical areas and links connecting nodes correspond to human mobility between areas. Using a mean-field approach, we derive the theoretical spreading threshold revealing a non-trivial dependence on the heterogeneity of vaccination. Extensive Monte Carlo simulations validate the theoretical threshold and also show the complex temporal epidemic behaviours above the threshold.     

Proximity networks and epidemics

Disease spread in most biological populations requires the proximity of agents. In populations where the individuals have spatial mobility, the contact graph is generated by the “collision dynamics” of the agents, and thus the evolution of epidemics couples directly to the spatial dynamics of the population. We first briefly review the properties and the methodology of an agent-based simulation (EPISIMS) to model disease spread in realistic urban dynamic contact networks. Using the data generated by this simulation, we introduce the notion of dynamic proximity networks which takes into account the relevant time-scales for disease spread: contact duration, infectivity period, and rate of contact creation. This approach promises to be a good candidate for a unified treatment of epidemic types that are driven by agent collision dynamics. In particular, using a simple model, we show that it can account for the observed qualitative differences between the degree distributions of contact graphs of diseases with short infectivity period (such as air-transmitted diseases) or long infectivity periods (such as HIV).     

Adsorption and spreading of polymers at plane interfaces; theory and molecular dynamics simulations

Nonequilibrium processes play a key role in the adsorption kinetics of macromolecules. It is expected that the competition between transport of polymer towards an interface and its subsequent spreading has a significant influence on the adsorbed amount. An increase of the transport rate can lead to an increase of the adsorbed amount, especially when the polymer has too little time to spread at the interface. In this study we present both molecular dynamics simulations and analytical calculations to describe some aspects of the adsorption kinetics. From MD simulations on a poly(ethylene oxide) chain in vacuum near a graphite surface, we conclude that the spreading process can, in first approximation, be described by either a simple exponential function or by first-order reaction kinetics. Combining these spreading models with the transport equations for two different geometries (stagnation-point flow and overflowing cylinder) we are able to derive analytical equations for the adsorption kinetics of polymers at solid-liquid and at liquid-fluid interfaces. Received: 18 July 1997 / Received in final form: 27 October 1997 / Accepted: 6 November 1997     

Global Migration Can Lead to Stronger Spatial Selection than Local Migration

The outcome of evolutionary processes depends on population structure. It is well known that mobility plays an important role in affecting evolutionary dynamics in group structured populations. But it is largely unknown whether global or local migration leads to stronger spatial selection and would therefore favor to a larger extent the evolution of cooperation. To address this issue, we quantify the impacts of these two migration patterns on the evolutionary competition of two strategies in a finite island model. Global migration means that individuals can migrate from any one island to any other island. Local migration means that individuals can only migrate between islands that are nearest neighbors; we study a simple geometry where islands are arranged on a one-dimensional, regular cycle. We derive general results for weak selection and large population size. Our key parameters are: the number of islands, the migration rate and the mutation rate. Surprisingly, our comparative analysis reveals that global migration can lead to stronger spatial selection than local migration for a wide range of parameter conditions. Our work provides useful insights into understanding how different mobility patterns affect evolutionary processes.     

Spatiotemporal Characteristic of Epidemic Spreading in Mobile Individuals

We numerical simulate the propagation behaviour and people distribution trait of epidemic spreading in mobile individuals by using cellular automaton method. The simulation results show that there exists a critical value of infected rate fluctuating amplitude, above which the epidemic can spread in whole population. Moreover, with the value of infected rate fluctuating amplitude increasing, the spatial distribution of infected population exhibits the spontaneous formation of irregular spiral waves and convergence phenomena, at the same time, the density of different populations will oscillate automatically with time. What is more, the traits of dynamic grow clearly and stably when the time and the value of infected rate fluctuating amplitude increasing. It is also found that the maximal proportion of infected individuals is independent of the value of fluctuating amplitude rate, but increases linearly with the population density.     

Rumor Spreading Model with Immunization Strategy and Delay Time on Homogeneous Networks

In order to prevent and control the spread of rumors, the implementation of immunization strategies for ignorant individuals is very necessary, where the immunization usually means letting them learn the truth of rumors.Considering the facts that there is always a delay time between rumor spreading and implementing immunization, and that the truth of rumors can also be spread out, this paper constructs a novel susceptible-infected-removed(SIR) model.The propagation dynamical behaviors of the SIR model on homogeneous networks are investigated by using the meanfield theory and the Monte Carlo method. Research shows that the greater the delay time, the worse the immune effect of the immunization strategy. It is also found that the spread of the truth can inhibit to some extent the propagation of rumors, and the trend will become more obvious with the increase of reliability of the truth. Moreover, under the influence of delay time, the existence of nodes' identification force still slightly reduces the propagation degree of rumors.     

Information Dynamics of Electric Field Intensity before and during the COVID-19 Pandemic

This work investigates the temporal statistical structure of time series of electric field (EF) intensity recorded with the aim of exploring the dynamical patterns associated with periods with different human activity in urban areas. The analyzed time series were obtained from a sensor of the EMF RATEL monitoring system installed in the campus area of the University of Novi Sad, Serbia. The sensor performs wideband cumulative EF intensity monitoring of all active commercial EF sources, thus including those linked to human utilization of wireless communication systems. Monitoring was performed continuously during the years 2019 and 2020, allowing us to investigate the effects on the patterns of EF intensity of varying conditions of human mobility, including regular teaching and exam activity within the campus, as well as limitations to mobility related to the COVID-19 pandemic. Time series analysis was performed using both simple statistics (mean and variance) and combining the information-theoretic measure of information storage (IS) with the method of surrogate data to quantify the regularity of EF dynamic patterns and detect the presence of nonlinear dynamics. Moreover, to assess the possible coexistence of dynamic behaviors across multiple temporal scales, IS analysis was performed over consecutive observation windows lasting one day, week, month, and year, respectively coarse grained at time scales of 6 min, 30 min, 2 h, and 1 day. Our results document that the EF intensity patterns of variability are modulated by the movement of people at daily, weekly, and monthly scales, and are blunted during periods of restricted mobility related to the COVID-19 pandemic. Mobility restrictions also affected significantly the regularity of the EF intensity time series, resulting in lower values of IS observed simultaneously with a loss of nonlinear dynamics. Thus, our analysis can be useful to investigate changes in the global patterns of human mobility both during pandemics or other types of events, and from this perspective may serve to implement strategies for safety assessment and for optimizing the design of networks of EF sensors.