Accounting for social media effects to improve the accuracy of infection models: combatting the COVID-19 pandemic and infodemic

A game theoretic complex network model to estimate the epidemic threshold under individual vaccination behaviour and adaptive social connections

In today's interconnected world, the spread of information is closely linked to infectious disease dynamics. Public awareness plays a crucial role, as individual vaccination decisions significantly impact collective efforts to combat emerging health threats. This study explores disease transmission within a framework integrating social connections, information sharing, and individual vaccination decisions. We introduce a behaviour-prevalence model on an adaptive multiplex network, where the physical layer (Layer-II) captures disease transmission under vaccination. In contrast, the virtual layer (Layer-I) represents adaptive social contacts and the flow of information, shaping vaccination decisions within a socially influenced environment. We derive analytical expressions for the epidemic threshold using the microscopic Markov Chain Method (MMCM). Simulation results highlight that adaptive social contacts lead to a higher epidemic threshold than non-adaptive networks. Additionally, network characteristics, such as the power-law exponent in scale-free networks, significantly impact infection spread within populations. Our results reveal that changes in perceived infection risk and an individual's sensitivity to non-vaccinated neighbour's status strongly influence vaccine uptake across populations. These insights can guide public health officials in developing targeted vaccination programs that address the evolving dynamics of social connections, information dissemination, and vaccination choice in the digital era.

MGLEP: Multimodal Graph Learning for Modeling Emerging Pandemics with Big Data

Accurate forecasting and analysis of emerging pandemics play a crucial role in effective public health management and decision-making. Traditional approaches primarily rely on epidemiological data, overlooking other valuable sources of information that could act as sensors or indicators of pandemic patterns. In this paper, we propose a novel framework, MGLEP, that integrates temporal graph neural networks and multi-modal data for learning and forecasting. We incorporate big data sources, including social media content, by utilizing specific pre-trained language models and discovering the underlying graph structure among users. This integration provides rich indicators of pandemic dynamics through learning with temporal graph neural networks. Extensive experiments demonstrate the effectiveness of our framework in pandemic forecasting and analysis, outperforming baseline methods across different areas, pandemic situations, and prediction horizons. The fusion of temporal graph learning and multi-modal data enables a comprehensive understanding of the pandemic landscape with less time lag, cheap cost, and more potential information indicators.

Crowdsourced Perceptions of Human Behavior to Improve Computational Forecasts of US National Incident Cases of COVID-19: Survey Study

BACKGROUND

Past research has shown that various signals associated with human behavior (eg, social media engagement) can benefit computational forecasts of COVID-19. One behavior that has been shown to reduce the spread of infectious agents is compliance with nonpharmaceutical interventions (NPIs). However, the extent to which the public adheres to NPIs is difficult to measure and consequently difficult to incorporate into computational forecasts of infectious diseases. Soliciting judgments from many individuals (ie, crowdsourcing) can lead to surprisingly accurate estimates of both current and future targets of interest. Therefore, asking a crowd to estimate community-level compliance with NPIs may prove to be an accurate and predictive signal of an infectious disease such as COVID-19.

OBJECTIVE

We aimed to show that crowdsourced perceptions of compliance with NPIs can be a fast and reliable signal that can predict the spread of an infectious agent. We showed this by measuring the correlation between crowdsourced perceptions of NPIs and US incident cases of COVID-19 1-4 weeks ahead, and evaluating whether incorporating crowdsourced perceptions improves the predictive performance of a computational forecast of incident cases.

METHODS

For 36 weeks from September 2020 to April 2021, we asked 2 crowds 21 questions about their perceptions of community adherence to NPIs and public health guidelines, and collected 10,120 responses. Self-reported state residency was compared to estimates from the US census to determine the representativeness of the crowds. Crowdsourced NPI signals were mapped to 21 mean perceived adherence (MEPA) signals and analyzed descriptively to investigate features, such as how MEPA signals changed over time and whether MEPA time series could be clustered into groups based on response patterns. We investigated whether MEPA signals were associated with incident cases of COVID-19 1-4 weeks ahead by (1) estimating correlations between MEPA and incident cases, and (2) including MEPA into computational forecasts.

RESULTS

The crowds were mostly geographically representative of the US population with slight overrepresentation in the Northeast. MEPA signals tended to converge toward moderate levels of compliance throughout the survey period, and an unsupervised analysis revealed signals clustered into 4 groups roughly based on the type of question being asked. Several MEPA signals linearly correlated with incident cases of COVID-19 1-4 weeks ahead at the US national level. Including questions related to social distancing, testing, and limiting large gatherings increased out-of-sample predictive performance for probabilistic forecasts of incident cases of COVID-19 1-3 weeks ahead when compared to a model that was trained on only past incident cases.

CONCLUSIONS

Crowdsourced perceptions of nonpharmaceutical adherence may be an important signal to improve forecasts of the trajectory of an infectious agent and increase public health situational awareness.

COVID-19 research in management: An updated bibliometric analysis

The unprecedented impact of COVID-19 on the global economy as well as on the academic literature. Since early 2020, management researchers have made exceptional efforts to extend our understanding of the pandemic's effect on consumption, sourcing, the workplace, and corporate strategies. The present study uses a bibliometric design to analyze the extensive database of COVID-19 studies in management literature generated over a 2-year period. The analysis focused on the performance of research constituents, thematic analysis of the literature, categorization of the themes at a societal, organizational, and individual level, and finally, a deep analysis of future research calls in the body of literature.

The impact of the COVID-19 pandemic on the creative industries: A literature review and future research agenda

The ongoing COVID-19 pandemic has affected countless businesses, leading to serious disruptions for many industries. Drawing on the resilience literature, this study offers an understanding of the impact of the COVID-19 pandemic on the creative industries and their response to the challenges they have encountered. This study reviews 59 papers following the systematic literature review approach and reveals several positive implications of the COVID-19 pandemic within the creative industries (e.g., IT and software) as well as the negative (the music industry, festivals, cultural events). Identifying six themes related to the impact of the COVID-19 pandemic on the creative industries, we develop a response matrix based on the discussion of firms' digital capabilities and their ability to adapt to the COVID-19 crisis. We outline future research directions using a Theory-Context-Characteristics-Methodology (TCCM) framework.

Multifaceted Nature of Social Media Content Propagating COVID-19 Vaccine Hesitancy: Ukrainian Case

COVID-19 became an issue affecting different parts of our life. Different communication campaigns use vaccination as an information peg, argument in discussions, and so on. As a result, they have an impact on people's attitudes to immunization. We applied the message analysis to the dataset of social media posts from Ukraine to detect the messages used in the communication regarding the vaccine and reveal communication campaigns propagating these messages. We found five campaigns launched by different actors and shaping the attitude to COVID-19 immunization expressed in the people's posts. The incoherence of the information about immunization and authorities' inconsistency in the communications about vaccines may lead to vaccine hesitancy and undermine confidence in the sources of the official information about COVID-19. Vaccine hesitancy has multifaceted nature and cannot be reduced just to politicians' conspiracy theories or far-right propaganda.

Social Media Use, Self-Efficacy, Perceived Threat, and Preventive Behavior in Times of COVID-19: Results of a Cross-Sectional Study in Pakistan

Although the role of social media in infectious disease outbreaks is receiving increasing attention, little is known about the mechanisms by which social media use affects risk perception and preventive behaviors during such outbreaks. This study aims to determine whether there are any relationships between social media use, preventive behavior, perceived threat of coronavirus, self-efficacy, and socio-demographic characteristics. The data were collected from 310 respondents across Pakistan using an online cross-sectional survey. Reliability analyses were performed for all scales and structural equational modeling was used to identify the relationships between study variables. We found that: (i) social media use predicts self-efficacy (β = 0.25, p < 0.05) and perceived threat of coronavirus (β = 0.54, p < 0.05, R 2 = 0.06), and (ii) preventive behavior is predicted by self-efficacy and perceived threat of coronavirus (R = 0.10, p < 0.05). Therefore, these results indicate the importance of social media's influence on health-related behaviors. These findings are valuable for health administrators, governments, policymakers, and social scientists, specifically for individuals whose situations are similar to those in Pakistan.

Harnessing Social Media in the Modelling of Pandemics-Challenges and Opportunities

As COVID-19 spreads throughout the world without a straightforward treatment or widespread vaccine coverage in the near future, mathematical models of disease spread and of the potential impact of mitigation measures have been thrust into the limelight. With their popularity and ability to disseminate information relatively freely and rapidly, information from social media platforms offers a user-generated, spontaneous insight into users' minds that may capture beliefs, opinions, attitudes, intentions and behaviour towards outbreaks of infectious disease not obtainable elsewhere. The interactive, immersive nature of social media may reveal emergent behaviour that does not occur in engagement with traditional mass media or conventional surveys. In recognition of the dramatic shift to life online during the COVID-19 pandemic to mitigate disease spread and the increasing threat of further pandemics, we examine the challenges and opportunities inherent in the use of social media data in infectious disease modelling with particular focus on their inclusion in compartmental models.