Micromodel and Analysis of COVID-19 Based on Hypergraph

In view of the fact that the existing propagation models ignore the influence of different fields and different virus variants on individual infection, and the classical propagation models only describe the macroscopic situation of virus transmission, which cannot be specific to individual cases, this paper proposes 67ya microscopic virus propagation model based on hypergraph (HC-SIRS). Firstly, the concept of hypergraph is used to divide different fields of individuals into corresponding hyperedges. Based on different contact probabilities of each hyperedge, the contact probability matrix is formed to relate the contact between individuals. The individual infection probability of micro-virus propagation model based on hypergraph is deduced, and the corresponding differential equation is established. Secondly, the basic regeneration number and its characteristics of the model are derived. The upper bound of the basic regeneration number of the model is less than or equal to that of the classical SIRS model, indicating that the virus is more difficult to spread in this model. In fact, the different fields people live in and the different personal constitutions have a certain impact on the spread of the virus. The model is more comprehensive, so it is more suitable for simulating the spread of the virus in theory. Finally, the COVID-19 data of Diamond Princess and two cities in China are used for simulation experiments, and the mean absolute error(MAE) is used as the evaluation standard. The results showed that HC-SIRS could well simulate the spread of COVID-19. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Scalable Adversarial Attack Algorithms on Influence Maximization

In this paper, we study the adversarial attacks on influence maximization under dynamic influence propagation models in social networks. In particular, given a known seed set S, the problem is to minimize the influence spread from S by deleting a limited number of nodes and edges. This problem reflects many application scenarios, such as blocking virus (e.g. COVID-19) propagation in social networks by quarantine and vaccination, blocking rumor spread by freezing fake accounts, or attacking competitor's influence by incentivizing some users to ignore the information from the competitor. In this paper, under the linear threshold model, we adapt the reverse influence sampling approach and provide efficient algorithms of sampling valid reverse reachable paths to solve the problem. We present three different design choices on reverse sampling, which all guarantee 1/2 - ϵ approximation (for any small ϵ >0) and an efficient running time. © 2023 ACM.

Time-lag rumor propagation model and rumor-refuting strategy of SEIRD under COVID-19

With the increasing popularity of the Internet and the spread of COVID-19, epidemic-related rumors have attracted significant attention, allowing them to brew quickly and pose extremely negative social impacts. It is of great significance to investigate the propagation process of online rumors and offer tentative strategies to curb it. Based on the traditional susceptible, infected, recovered (SIR) model of online rumor propagation, groups of potential and die-hard rumor believers were introduced in this paper, establishing an authoritative rumor-refuting mechanism. Meanwhile, this paper considered factors such as the time-lag effect of rumor refutation from the nonauthoritative and authoritative institutions and the impact of the popularizing rate of higher education on the propagation and refutation of rumors. As a result of the process, the SEIRD (susceptible, exposed, infected, recovered, die-hard-infected) rumor propagation model was established to study how the proportion of the susceptible, exposed, infected, recovered, and die-hard-infected varies under different popularizing rates of higher education, the presence or absence of the authoritative rumor-refuting institutions, and the time-lag effect of rumor refutation. Finally, the model's effectiveness was verified via experimental simulation, which provided a reference for controlling the spread of online rumor propagation. In addition, the paper proposed a rumor-refuting coefficient to measure the rumor-refuting ability of the nonauthoritative and authoritative institutions. The results show that (1) increasing popularizing rate of higher education significantly slows down the rumor propagation and reduces the rumor propagation peak;(2) refuting the rumors based on the authoritative institutions is decisive for the ultimate elimination of rumors;and (3) eliminating the time-lag effect in refuting rumors facilitates slowing down the propagation of the online rumors. Therefore, the paper puts forward a feasible strategy to eliminate the time-lag effect of online rumor refutation in the future. Copyright ©2022 Chinese Journal of Engineering. All rights reserved.

Spatial and Temporal Transmission Differences between SARS and COVID-19 and Analysis of Influence Factors

The outbreaks of SARS and COVID-19 have had a serious impact on public health, social economy and so on in China, in order to reveal the common law and difference characteristics of space-time transmission of respiratory infectious diseases and the reasons behind them, using space-time statistical methods, systematically analyzed and compared the difference characteristics of space-time transmission between SARS and COVID-19, and combined with the transmission characteristics of the virus itself and temperature, traffic and other factors to analyze the causes. The study shows that, ① SARS experiences two stages, the rising period-flat phase, and the COVID-19 experiences three stages, the rising period-sharp rise-slow up period. ② In the mode of spatial transmission, the transmission intensity and range of COVID-19 is greater than that of SARS, and the overall connectivity of COVID-19 is greater and the provinces are more closely related to the outbreak of the virus. Both SARS and COVID-19 transmission have obvious spatial aggregation characteristics. They are based on proximity propagation and long-range leaps, and SARS has a secondary communication center, and COVID-19 diffusion center has not been relocated. ③ In the direction of space communication, SARS is centered in Beijing, Hong Kong and Guangdong, the direction of spatial communication is stronger, and COVID-19 is only spread outwards with Hubei as the center. ④ In terms of spatial transmission speed, the spread time of the first case in each province of SARS is relatively large, and the spread time of the first case in each province of COVID-19 is roughly divided by Hu Huanyong Line, showing a phenomenon of "fast in the east and slow in the west", and the spread time span is relatively short. ⑤ R0 is the main reason for the difference between the spatial transmission range of SARS and COVID-19 and the speed of spatial transmission. The temperature suitability of SARS and COVID-19 viruses is different, but spatial aggregation transmission and adjacent area transmission are occurring in areas with similar temperatures. Besides the virus transmission capacity and temperature impact, traffic is the main reason affecting SARS and COVID-19 space long-range leap transmission, and the spatial transmission speed of both is negatively related to the density of the road network. 2021, Science Press. All right reserved.