Modeling the competitive diffusions of rumor and knowledge and the impacts on epidemic spreading

Epidemic dynamics with awareness cascade of positive and negative information on delayed multiplex networks

Human behavioral awareness is usually socially relevant, decisions are made based on the behavior of individuals, and this dynamic process of human awareness with herd effects is called awareness cascade. Based on the complexity of modern information dissemination, information is not monolithic. Individuals choose the type of epidemic-related information to accept, i.e., whether it is positive or negative information, according to the awareness cascade, and then take the corresponding measures to cope with the epidemic. In this paper, we use the microscopic Markov chain approach to model an information-virus dual network, where the information layer has a threshold model with awareness cascade of positive and negative information, and on the virus layer is a susceptible-infected-recovery model with epidemic infection time delay and recovery time delay. The time delay is also a non-negligible modeling factor as the complete infection of an individual and the complete recovery of an individual require sufficient time. An explicit formula for the critical threshold of epidemic spread for this model is derived. We find that positive and negative information and time delay have a significant effect on the critical threshold, and the recovery time delay is the time delay that mainly affects the epidemic size. Experiments show that the local acceptance rate of positive information has a threshold point for the spread of epidemics under awareness cascade, and that this point is significantly affected by the mass media. The local acceptance rate of negative information also divides the spread of epidemics into two stages.

Dynamic analysis and optimal control of competitive information dissemination model

Information dissemination is vital to human production and life. Identifying and analyzing the modes and mechanisms of information dissemination under different conditions is conducive to playing the role of positive information and reducing the impact of negative information. Considering that multiple pieces of information coexist and conflict with each other, in this paper, a competitive information dissemination model is established and the proposed system is analyzed. The next generation matrix method is used to determine the basic reproduction number R0. The equilibrium conditions of the system are presented, and the Routh-Hurwitz stability criterion and the second method of Liapunov are used for performing stability analysis. Based on Pontryagin's extreme value principle, the influence rate and the isolation rate of the boot mechanism are chosen as control parameters. In addition, the optimal control parameter expression of the system and optimal control system are presented. The numerical simulation shows various states and changes in information dissemination. The results presented in this work show that the competitive information suppresses other competitive information during the process of dissemination. The results presented in this paper can serve as a reference for relevant departments to manage information dissemination.

Information propagation dynamics on heterogeneous-homogeneous coupling bi-layer networks

The proliferation of multi-platform network information has expanded communication channels for users, enabling the integration and dissemination of information across both Social Networking Services (SNS)-type app and Instant Message (IM)-type app. With the intensification of convergent communication, some users in the two types of apps show active alternation in spreading information to each other's platforms. The study of the evolution trend of information in different platforms is of great practical significance for the mastery of the communication law. This study synthesizes the following three points: (1) The information in SNS-type app diffuses from key nodes with more followers to ordinary nodes, showing the characteristics of heterogeneous network with radial and explosive propagation. (2) The information in IM-type app mainly depends on the "relationship chain" diffusion, showing the characteristics of homogeneous network with gradual and multi-cluster propagation. (3) SNS-type apps and IM-type apps with some users showing coupled propagation characteristics. Therefore, this study constructs the heterogeneous-homogeneous asymmetric coupling two-layer network information propagation dynamics model. The propagation threshold R0 and the stability of the model are derived theoretically. Real network data sets are used to simulate the platform fusion. Numerical simulations confirm the rationality of the propagation threshold and perform changes analyses of parameters, such as the degree of cross-circulation of platforms, users' tendency of multi-platform expression, and changes in users' behaviors towards information dissemination. Simulation results reveal that promoting platform integration can improve communication efficiency in the real world. Dual-platform communication by IM-platform spreaders substantially contributes to the growth in the number of SNS-platform spreaders. The higher the level of disinterest in dual-platform spreaders, the more likely it is to inhibit the growth of spreaders and removers, with IM-type app demonstrating more pronounced effects.

Coupled infectious disease and behavior dynamics. A review of model assumptions

To comprehend the dynamics of infectious disease transmission, it is imperative to incorporate human protective behavior into models of disease spreading. While models exist for both infectious disease and behavior dynamics independently, the integration of these aspects has yet to yield a cohesive body of literature. Such an integration is crucial for gaining insights into phenomena like the rise of infodemics, the polarization of opinions regarding vaccines, and the dissemination of conspiracy theories during a pandemic. We make a threefold contribution. First, we introduce a framework todescribemodels coupling infectious disease and behavior dynamics, delineating four distinct update functions. Reviewing existing literature, we highlight a substantial diversity in the implementation of each update function. This variation, coupled with a dearth of model comparisons, renders the literature hardly informative for researchers seeking to develop models tailored to specific populations, infectious diseases, and forms of protection. Second, we advocate an approach tocomparingmodels' assumptions about human behavior, the model aspect characterized by the strongest disagreement. Rather than representing the psychological complexity of decision-making, we show that 'influence-response functions' allow one to identify which model differences generate different disease dynamics and which do not, guiding both model development and empirical research testing model assumptions. Third, we propose recommendations for future modeling endeavors and empirical research aimed atselectingmodels of coupled infectious disease and behavior dynamics. We underscore the importance of incorporating empirical approaches from the social sciences to propel the literature forward.

How can we implement targeted policies of rumor governance? An empirical study based on survey experiment of COVID-19

Since early 2020, COVID-19 has been a major public security crisis that has had an enormous impact on the world. With the spread of the epidemic, rumors occur, some of which have even caused public panic. They have greatly affected the government’s efforts of epidemic prevention and thus urgently need to be evaluated. This study aimed to examine how to make flexible use of different policy tools to govern rumors based on their different characteristics. From the perspective of behavioral public policy, this study observes the effectiveness of various behavioral policy tools in rumor governance, hoping to explore the optimal solution of rumor governance from the perspective of micro public psychology. The survey experiment shows that individual behavior-related rumors (hereafter referred to as IBRs) are easier to be governed than epidemic progress-related rumors (hereafter referred to as EPRs) are, and that quick response is more effective than non-quick response. Through interaction analysis, it is known that in the governance of IBRs, nudge is more effective in rapid response, while in the context of non-quick response, boost outperforms nudge in rumor governance. A similar phenomenon can be seen in the scenario of EPR governance, despite a tinier difference in effectiveness compared with that of IBRs. The study enlightens us that rumor refutation requires not only people’s disbelief in and restraint on rumors, but also the implementation of science-based targeted policies. Based on the conclusion, this study puts forward suggestions on implementing targeted policies of rumor governance.

A study on the competitive dissemination of disinformation and knowledge on social media

Purpose

The purpose of this study is to explore the evolutionary path and stable strategy for the competitive dissemination between disinformation and knowledge on social media to provide effective solutions to curb the dissemination of disinformation and promote the spread of knowledge.

Design/methodology/approach

Based on the social capital (SC) theory, the benefit matrix is constructed and an evolutional game model is established in this paper. Through model solving and Matrix Laboratory (MATLAB) simulation, the factors that influence disinformation-believing users (DUs) and knowledge-believing users (KUs) to choose different strategies are analyzed.

Findings

The initial dissemination willingness, the disinformation infection probability, the knowledge infection probability and the knowledge penetration probability are proved to be crucial factors influencing the game equilibrium in the competitive dissemination process of disinformation and knowledge. Moreover, some countermeasures and recommendations for the governance of disinformation are proposed.

Originality/value

Currently most research interest lies in the disinformation dissemination model but ignores the interaction between disinformation and knowledge in the diffusion process. This study reveals the dynamic mechanism of social media users disseminating disinformation and knowledge and is expected to promote the formation of cleaner cyberspace.

Novelty in Public Health and Epidemiology Informatics

OBJECTIVES

To highlight novelty studies and current trends in Public Health and Epidemiology Informatics (PHEI).

METHODS

Similar to last year's edition, a PubMed search of 2021 scientific publications on PHEI has been conducted. The resulting references were reviewed by the two section editors. Then, 11 candidate best papers were selected from the initial 782 references. These papers were then peer-reviewed by selected external reviewers. They included at least two senior researchers, to allow the Editorial Committee of the 2022 IMIA Yearbook edition to make an informed decision for selecting the best papers of the PHEI section.

RESULTS

Among the 782 references retrieved from PubMed, two were selected as the best papers. The first best paper reports a study which performed a comprehensive comparison of traditional statistical approaches (e.g., Cox Proportional Hazards models) vs. machine learning techniques in a large, real-world dataset for predicting breast cancer survival, with a focus on explainability. The second paper describes the engineering of deep learning models to establish associations between ocular features and major hepatobiliary diseases and to advance automated screening and identification of hepatobiliary diseases from ocular images.

CONCLUSION

Overall, from this year edition, we observed that the number of studies related to PHEI has decreased. The findings of the two studies selected as best papers on the topic suggest that a significant effort is still being made by the community to compare traditional learning methods with deep learning methods. Using multimodality datasets (images, texts) could improve approaches for tackling public health issues.

The impact of the global and local awareness diffusion on epidemic transmission considering the heterogeneity of individual influences

In this paper, we propose a coupled awareness-epidemic spreading model considering the heterogeneity of individual influences, which aims to explore the interaction between awareness diffusion and epidemic transmission. The considered heterogeneities of individual influences are threefold: the heterogeneity of individual influences in the information layer, the heterogeneity of individual influences in the epidemic layer and the heterogeneity of individual behavioral responses to epidemics. In addition, the individuals' receptive preference for information and the impacts of individuals' perceived local awareness ratio and individuals' perceived epidemic severity on self-protective behavior are included. The epidemic threshold is theoretically established by the microscopic Markov chain approach and the mean-field approach. Results indicate that the critical local and global awareness ratios have two-stage effects on the epidemic threshold. Besides, either the heterogeneity of individual influences in the information layer or the strength of individuals' responses to epidemics can influence the epidemic threshold with a nonlinear way. However, the heterogeneity of individual influences in the epidemic layer has few effect on the epidemic threshold, but can affects the magnitude of the final infected density.

The popularity of contradictory information about COVID-19 vaccine on social media in China

To eliminate the impact of contradictory information on vaccine hesitancy on social media, this research developed a framework to compare the popularity of information expressing contradictory attitudes towards COVID-19 vaccine or vaccination, mine the similarities and differences among contradictory information's characteristics, and determine which factors influenced the popularity mostly. We called Sina Weibo API to collect data. Firstly, to extract multi-dimensional features from original tweets and quantify their popularity, content analysis, sentiment computing and k-medoids clustering were used. Statistical analysis showed that anti-vaccine tweets were more popular than pro-vaccine tweets, but not significant. Then, by visualizing the features' centrality and clustering in information-feature networks, we found that there were differences in text characteristics, information display dimension, topic, sentiment, readability, posters' characteristics of the original tweets expressing different attitudes. Finally, we employed regression models and SHapley Additive exPlanations to explore and explain the relationship between tweets' popularity and content and contextual features. Suggestions for adjusting the organizational strategy of contradictory information to control its popularity from different dimensions, such as poster's influence, activity and identity, tweets' topic, sentiment, readability were proposed, to reduce vaccine hesitancy.

Effect of Global and Local Refutation Mechanism on Rumor Propagation in Heterogeneous Network

In the process of rumors propagation, the behavior of individuals to propagation or refutation is not only influenced by the surrounding global environment but also the local environment. In this paper, a modified rumor propagation model is proposed considering the global and local effects of the rumor refutation mechanism and the activity rate of individuals in a heterogeneous network, and the dynamics process of the rumor propagation is derived by using the mean-field equation. Combining theoretical proving and simulation analysis, it shows that the critical threshold of rumor propagation has a great relationship with individual activity rates and global and local effects of the rumor refutation mechanism. The greater the government’s efforts to refute rumors, the lower the critical threshold of rumor propagation and the smaller the final rumor size. While relatively, people are much more likely to believe the global rumor refutation information taken with official information, local rumor refutation information has little influence on rumor propagation.

COVID-19-Related Rumor Content, Transmission, and Clarification Strategies in China: Descriptive Study

Background

Given the permeation of social media throughout society, rumors spread faster than ever before, which significantly complicates government responses to public health emergencies such as the COVID-19 pandemic.

Objective

We aimed to examine the characteristics and propagation of rumors during the early months of the COVID-19 pandemic in China and evaluated the effectiveness of health authorities’ release of correction announcements.

Methods

We retrieved rumors widely circulating on social media in China during the early stages of the COVID-19 pandemic and assessed the effectiveness of official government clarifications and popular science articles refuting those rumors.

Results

We show that the number of rumors related to the COVID-19 pandemic fluctuated widely in China between December 1, 2019 and April 15, 2020. Rumors mainly occurred in 3 provinces: Hubei, Zhejiang, and Guangxi. Personal social media accounts constituted the major source of media reports of the 4 most widely distributed rumors (the novel coronavirus can be prevented with “Shuanghuanglian”: 7648/10,664, 71.7%; the novel coronavirus is the SARS coronavirus: 14,696/15,902, 92.4%; medical supplies intended for assisting Hubei were detained by the local government: 3911/3943, 99.2%; asymptomatically infected persons were regarded as diagnosed COVID-19 patients with symptoms in official counts: 322/323, 99.7%). The number of rumors circulating was positively associated with the severity of the COVID-19 epidemic (ρ=0.88, 95% CI 0.81-0.93). The release of correction articles was associated with a substantial decrease in the proportion of rumor reports compared to accurate reports. The proportions of negative sentiments appearing among comments by citizens in response to media articles disseminating rumors and disseminating correct information differ insignificantly (both correct reports: χ₁²=0.315, P=.58; both rumors: χ₁²=0.025, P=.88; first rumor and last correct report: χ₁²=1.287, P=.26; first correct report and last rumor: χ₁²=0.033, P=.86).

Conclusions

Our results highlight the importance and urgency of monitoring and correcting false or misleading reports on websites and personal social media accounts. The circulation of rumors can influence public health, and government bodies should establish guidelines to monitor and mitigate the negative impact of such rumors.

A rumor reversal model of online health information during the Covid-19 epidemic

The development of the Internet and social media has expanded the speed and scope of information dissemination, but not all widely disseminated information is true. Especially during the public health emergencies, the endogenous health information demand generated by the lack of scientific knowledge of health information among online users stimulates the dissemination of health information by mass media while providing opportunities for rumor mongers to publish and spread online rumors. Invalid scientific knowledge and rumors will have a serious negative impact and disrupt social order during epidemic outbreaks such as COVID-19. Therefore, it is extremely important to construct an effective online rumor reversal model. The purpose of this study is to build an online rumor reversal model to control the spread of online rumors and reduce their negative impact. From the perspective of internal and external factors, based on the SIR model, this study constructed a G-SCNDR online rumor reversal model by adopting scientific knowledge level theory and an external online rumor control strategy. In this study, the G-SCNDR model is simulated, and a sensitivity analysis of the important parameters of the model is performed. The reversal efficiency of the G-SCNDR model can be improved by properly adopting the isolation-conversion strategy as the external control approach to online rumors with improving the popularization rate of the level of users' scientific knowledge and accelerating the transformation efficiency of official nodes. This study can help provide a better understanding of the process of online rumor spreading and reversing, as well as offering ceritain guidance and countermeasures for online rumor control during public health emergencies.

The characteristics of knowledge diffusion of library and information science – from the perspective of citation

Purpose

This paper aims to explore the characteristics of knowledge diffusion of library and information science to reveal its development trend and influence on other disciplines.

Design/methodology/approach

Based on the ESI discipline classification, this paper measures the knowledge diffusion from the library and information science to other disciplines over the last 24 years using indicators in four dimensions: breadth, intensity, speed and theme of knowledge diffusion.

Findings

The results show that the knowledge diffusion breadth of library and information science is wide, spreading to 21 ESI disciplines; the knowledge spread mainly concentrates in four soft or applied disciplines, and yet partially inter-disciplinary, and the knowledge diffusion intensity to each ESI discipline is parabolic whose highest point is mostly in 2004–2005; the speed of spreading to the 21 ESI disciplines is faster and faster, and the articles at the highest speed of knowledge diffusion are basically published after 2005; the knowledge diffusion themes are becoming increasingly diverse, deepening and specialization over time.

Originality/value

This paper modifies the relevant indicators of knowledge diffusion and constructs a measurement framework of knowledge diffusion from four aspects: breadth, intensity, speed and theme. The research method can also be used to explore the characteristics of knowledge absorption of a discipline from other ones.

Impact of information diffusion on epidemic spreading in partially mapping two-layered time-varying networks

We propose a new epidemic model considering the partial mapping relationship in a two-layered time-varying network, which aims to study the influence of information diffusion on epidemic spreading. In the model, one layer represents the epidemic-related information diffusion in the social networks, while the other layer denotes the epidemic spreading in physical networks. In addition, there just exist mapping relationships between partial pairs of nodes in the two-layered network, which characterizes the interaction between information diffusion and epidemic spreading. Meanwhile, the information and epidemics can only spread in their own layers. Afterwards, starting from the microscopic Markov chain (MMC) method, we can establish the dynamic equation of epidemic spreading and then analytically deduce its epidemic threshold, which demonstrates that the ratio of correspondence between two layers has a significant effect on the epidemic threshold of the proposed model. Finally, it is found that MMC method can well match with Monte Carlo (MC) simulations, and the relevant results can be helpful to understand the epidemic spreading properties in depth.

A particle swarm optimization approach for predicting the number of COVID-19 deaths

The rapid spread of the COVID-19 pandemic has raised huge concerns about the prospect of a major health disaster that would result in a huge number of deaths. This anxiety was largely fueled by the fact that the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), responsible for the disease, was so far unknown, and therefore an accurate prediction of the number of deaths was particularly difficult. However, this prediction is of the utmost importance for public health authorities to make the most reliable decisions and establish the necessary precautions to protect people's lives. In this paper, we present an approach for predicting the number of deaths from COVID-19. This approach requires modeling the number of infected cases using a generalized logistic function and using this function for inferring the number of deaths. An estimate of the parameters of the proposed model is obtained using a Particle Swarm Optimization algorithm (PSO) that requires iteratively solving a quadratic programming problem. In addition to the total number of deaths and number of infected cases, the model enables the estimation of the infection fatality rate (IFR). Furthermore, using some mild assumptions, we derive estimates of the number of active cases. The proposed approach was empirically assessed on official data provided by the State of Qatar. The results of our computational study show a good accuracy of the predicted number of deaths.

Impacts of social distancing on the spread of infectious diseases with asymptomatic infection: A mathematical model

Social distancing can be divided into two categories: spontaneous social distancing adopted by the individuals themselves, and public social distancing promoted by the government. Both types of social distancing have been proved to suppress the spread of infectious disease effectively. While previous studies examined the impact of each social distancing separately, the simultaneous impacts of them are less studied. In this research, we develop a mathematical model to analyze how spontaneous social distancing and public social distancing simultaneously affect the outbreak threshold of an infectious disease with asymptomatic infection. A communication-contact two-layer network is constructed to consider the difference between spontaneous social distancing and public social distancing. Based on link overlap of the two layers, the two-layer network is divided into three subnetworks: communication-only network, contact-only network, and overlapped network. Our results show that public social distancing can significantly increase the outbreak threshold of an infectious disease. To achieve better control effect, the subnetwork of higher infection risk should be more targeted by public social distancing, but the subnetworks of lower infection risk shouldn't be overlooked. The impact of spontaneous social distancing is relatively weak. On the one hand, spontaneous social distancing in the communication-only network has no impact on the outbreak threshold of the infectious disease. On the other hand, the impact of spontaneous social distancing in the overlapped network is highly dependent on the detection of asymptomatic infection sources. Moreover, public social distancing collaborates with infection detection on controlling an infectious disease, but their impacts can't add up perfectly. Besides, public social distancing is slightly less effective than infection detection, because infection detection can also promote spontaneous social distancing.

The Public Servants' Response When Facing Pandemic: The Role of Public Service Motivation, Accountability Pressure, and Emergency Response Capacity

(1) Background: Public servants are regarded as guardians of the public interest, and their pandemic response played a vital role in controlling the spread of the epidemic. However, there is limited knowledge of the factors that influence public servants' response (PSR) when facing pandemic prevention and control tasks. (2) Methods: Based on the theory of planned behavior (TPB), models were constructed and a regression method was employed with Chinese civil servant data to investigate how PSR is influenced by public service motivation (PSM), accountability pressure (AP), and emergency response capacity (ERC). (3) Results and discussion: PSM, AP, and ERC all have a positive effect on PSR, with AP having the greatest influence, followed by PSM and ERC. The effects of PSM, AP, and ERC on PSR have group heterogeneity, which had little effect on civil servants with very low levels of PSR and the greatest impact on civil servants with medium-level PSR. Job categories of civil servants also are a factor related to PSR; PSM and AP have the strongest effects on civil servants in professional technology, and ERC has the greatest influence on administrative law enforcement. Moreover, gender, administrative level, and leadership positions also have an impact on PSR. (4) Conclusions: Based on the factors of PSR, we found at least three important aspects that governments need to consider in encouraging PSR when facing a pandemic.