Quantifying the Influence of Delay in Opinion Transmission of COVID-19 Information Propagation: Modeling Study

Time matters in pandemic risk communication: A moderated effect of information timeliness on stakeholder perception in Singapore

The outbreak of the COVID-19 pandemic shows the increasing importance of determining the factors of the public perceptions of personal and societal risks. These perceptions can shape people's behaviors, which, in turn, alter the spread of a pandemic on the community level. However, previous research on risk communication was inconsistent, and little is known about the impact of timely warning messages on stakeholders' perceptions of public health emergencies. To address this theoretical gap, this study analyzes the survey data (N = 538) from Singapore to explore the main effect of information timeliness on the respondents' stakeholder perceptions. This effect is moderated by normative factors, including attention and threat perceptions. We find that the more timely the government updates the risk information, the more trustworthy the stakeholders appear in respondents' opinions. Such an effect is weakened when the pre-decision attention or the threat perception interacts with the predictor independently. However, this effect on stakeholder perceptions becomes stronger if both moderators interact with the information timeliness. That is, an appropriate combination of the information released by the government can effectively enhance the image of the stakeholders during the pandemic.

Hot-topics cross-propagation and opinion-transfer dynamics in the Chinese Sina-microblog social media: a modeling study

On social media platforms, hot topics often contain several pieces of related information that can influence internet users, generating either positive or negative opinion orientation. Some of them will choose to retain or change their original opinions after exposure to multiple related messages. To describe the opinion-transfer transient and collective behaviors in this scenario, this paper proposes an opinion-transfer susceptible-forwarding-immunized (OT-SFI) information cross-propagation model. Real multiple information in messages with opinions obtained from the Chinese Sina microblog is used for data fitting to illustrate how model parameters can be estimated and used to predict the accumulative numbers of users with a particular view. The study attempts to relate changes in group views in the network to initial opinion distribution and individuals' opinion choices at the macro level. Furthermore, the model parameters at the micro level are used to measure the probability of "retention" and "reversal" of views in events, as well as the extent to which the masses are influenced by new information views. The result illustrates that the viewpoint distribution of the initial message and the opinion selection of the new message opinion leaders play crucial roles in promoting attention to the topic and driving for a desired collective opinion.

Signal propagation and linear response in the delay Vicsek model

Retardation between sensation and action is an inherent biological trait. Here we study its effect in the Vicsek model, which is a paradigmatic swarm model. We find that (1) a discrete time delay in the orientational interactions diminishes the ability of strongly aligned swarms to follow a leader and, in return, increases their stability against random orientation fluctuations; (2) both longer delays and higher speeds favor ballistic over diffusive spreading of information (orientation) through the swarm; (3) for short delays, the mean change in the total orientation (the order parameter) scales linearly in a small orientational bias of the leaders and inversely in the delay time, while its variance first increases and then saturates with increasing delays; and (4) the linear response breaks down when orientation conservation is broken.

Dynamic analysis and optimal control considering cross transmission and variation of information

Cross-transmission of information has a profound influence on the progress of science and technology and the discipline integration in the field of education. In this work, knowledge gained from the viral recombination and variation in COVID-19 transmission is applied to information transmission. Virus recombination and virus variation are similar to the crossing and information fusion phenomena in information transmission. An S2I4MR model with information crossing and variation is constructed. Then, the local and global asymptotic stabilities of the information-free equilibrium and information-existence equilibrium are analyzed. Additionally, the basic reproduction number [Formula: see text] of the model is calculated. As such, an optimal control strategy is hereby proposed to promote the cross-transmission of information and generate variant information. The numerical simulations support the results of the theoretical analysis and the sensitivity of the system towards certain control parameters. In particular, the results show that strengthening information crossing promotes the generation of variant information. Furthermore, encouraging information exchange and enhancing education improve the generation of information crossing and information variation.

Public sentiments toward COVID-19 vaccines in South African cities: An analysis of Twitter posts

Amidst the COVID-19 vaccination, Twitter is one of the most popular platforms for discussions about the COVID-19 vaccination. These types of discussions most times lead to a compromise of public confidence toward the vaccine. The text-based data generated by these discussions are used by researchers to extract topics and perform sentiment analysis at the provincial, country, or continent level without considering the local communities. The aim of this study is to use clustered geo-tagged Twitter posts to inform city-level variations in sentiments toward COVID-19 vaccine-related topics in the three largest South African cities (Cape Town, Durban, and Johannesburg). VADER, an NLP pre-trained model was used to label the Twitter posts according to their sentiments with their associated intensity scores. The outputs were validated using NB (0.68), LR (0.75), SVMs (0.70), DT (0.62), and KNN (0.56) machine learning classification algorithms. The number of new COVID-19 cases significantly positively correlated with the number of Tweets in South Africa (Corr = 0.462, P < 0.001). Out of the 10 topics identified from the tweets using the LDA model, two were about the COVID-19 vaccines: uptake and supply, respectively. The intensity of the sentiment score for the two topics was associated with the total number of vaccines administered in South Africa (P < 0.001). Discussions regarding the two topics showed higher intensity scores for the neutral sentiment class (P = 0.015) than for other sentiment classes. Additionally, the intensity of the discussions on the two topics was associated with the total number of vaccines administered, new cases, deaths, and recoveries across the three cities (P < 0.001). The sentiment score for the most discussed topic, vaccine uptake, differed across the three cities, with (P = 0.003), (P = 0.002), and (P < 0.001) for positive, negative, and neutral sentiments classes, respectively. The outcome of this research showed that clustered geo-tagged Twitter posts can be used to better analyse the dynamics in sentiments toward community-based infectious diseases-related discussions, such as COVID-19, Malaria, or Monkeypox. This can provide additional city-level information to health policy in planning and decision-making regarding vaccine hesitancy for future outbreaks.

FOMO (fate of online media only) in infectious disease modeling: a review of compartmental models

Mathematical models played in a major role in guiding policy decisions during the COVID-19 pandemic. These models while focusing on the spread and containment of the disease, largely ignored the impact of media on the disease transmission. Media plays a major role in shaping opinions, attitudes and perspectives and as the number of people online increases, online media are fast becoming a major source for news and health related information and advice. Consequently, they may influence behavior and in due course disease dynamics. Unlike traditional media, online media are themselves driven and influenced by their users and thus have unique features. The main techniques used to incorporate online media mathematically into compartmental models, with particular reference to the ongoing COVID-19 pandemic are reviewed. In doing so, features specific to online media that have yet to be fully integrated into compartmental models such as misinformation, different time scales with regards to disease transmission and information, time delays, information super spreaders, the predatory nature of online media and other factors are identified together with recommendations for their incorporation.