Trending on Social Media: Integrating Social Media into Infectious Disease Dynamics

Online spaces and the control of communicable diseases: implications for nursing practice

The digital revolution has significantly altered healthcare, including communicable disease control, with online spaces emerging as vital tools in preventing, identifying and controlling the spread of diseases. However, healthcare professionals, including nurses, need to find a balance between harnessing the benefits of mass communication and mitigating the potentially harmful effects of online misinformation. This article explores the benefits and challenges of using online spaces such as social media platforms in the control of communicable diseases and discusses the potential use of telehealth in reducing the risk of healthcare-associated infection and antimicrobial resistance. The author also describes a framework that nurses can use to explore potential roles and practice in the context of communicable disease control in online spaces.

Chaos in Opinion-Driven Disease Dynamics

During the COVID-19 pandemic, it became evident that the effectiveness of applying intervention measures is significantly influenced by societal acceptance, which, in turn, is affected by the processes of opinion formation. This article explores one among the many possibilities of coupled opinion-epidemic systems. The findings reveal either intricate periodic patterns or chaotic dynamics, leading to substantial fluctuations in opinion distribution and, consequently, significant variations in the total number of infections over time. Interestingly, the model exhibits a protective pattern.

Bilibili, TikTok, and YouTube as sources of information on gastric cancer: assessment and analysis of the content and quality

BACKGROUND

Gastric cancer has attracted widespread attention on social media due to its high incidence and severity. The Bilibili, TikTok, and YouTube video-sharing platforms have received considerable interest among general health consumers. Nevertheless, it remains unclear whether the information in videos on these platforms is of satisfactory content and quality.

METHODS

A total of 300 eligible videos related to gastric cancer were screened from three video-sharing platforms, Bilibili, TikTok, and YouTube, for assessment and analysis. First, the basic information presented in the videos was recorded. Next, we identified the source and content type of each video. Then, the Global Quality Scale (GQS), Journal of the American Medical Association (JAMA), and Modified DISCERN were used to assess the educational content and quality of each video. A comparative analysis was undertaken of the videos procured from these three sources.

RESULTS

We identified six categories of uploaders of the 300 videos: 159 videos (53%) were uploaded by health professionals, 21 videos (7%) by users in science communications, 29 videos (9.67%) by general users, 27 videos (9%) from news agencies, 63 videos (12%) by nonprofit organizations, and one video (0.33%) by a for-profit organization. In terms of the content types of the 300 videos, we identified five distinct categories. There were 48 videos (16%) on early signals, 12 videos (4%) on late symptoms, 40 videos (13.33%) on etiologies and causations, 160 videos (53.33%) on scientific introductions, and 40 videos (13.33%) on treatment methods. The overall quality of the videos was evaluated by the GQS, JAMA, and Modified DISCERN and was found to be medium, with scores of 2.6/5, 2.41/4, and 2.71/5 points, respectively.

CONCLUSIONS

This innovative study demonstrates that videos on social media platforms can help the public learn about early signals, late symptoms, treatment methods, etiologies and causations, and scientific introductions of gastric cancer. However, both the content and quality of uploaded recordings are inadequate currently. More efforts should be made to enhance the content and quality of videos on gastric cancer and to increase public awareness.

Modeling the effects of Prophylactic behaviors on the spread of SARS-CoV-2 in West Africa

Various general and individual measures have been implemented to limit the spread of SARS-CoV-2 since its emergence in China. Several phenomenological and mechanistic models have been developed to inform and guide health policy. Many of these models ignore opinions about certain control measures, although various opinions and attitudes can influence individual actions. To account for the effects of prophylactic opinions on disease dynamics and to avoid identifiability problems, we expand the SIR-Opinion model of Tyson et al. (2020) to take into account the partial detection of infected individuals in order to provide robust modeling of COVID-19 as well as degrees of adherence to prophylactic treatments, taking into account a hybrid modeling technique using Richard's model and the logistic model. Applying the approach to COVID-19 data from West Africa demonstrates that the more people with a strong prophylactic opinion, the smaller the final COVID-19 pandemic size. The influence of individuals on each other and from the media significantly influences the susceptible population and, thus, the dynamics of the disease. Thus, when considering the opinion of susceptible individuals to the disease, the view of the population at baseline influences its dynamics. The results are expected to inform public policy in the context of emerging and re-emerging infectious diseases.

Evolution of Select Epidemiological Modeling and the Rise of Population Sentiment Analysis: A Literature Review and COVID-19 Sentiment Illustration

With social networking enabling the expressions of billions of people to be posted online, sentiment analysis and massive computational power enables systematic mining of information about populations including their affective states with respect to epidemiological concerns during a pandemic. Gleaning rationale for behavioral choices, such as vaccine hesitancy, from public commentary expressed through social media channels may provide quantifiable and articulated sources of feedback that are useful for rapidly modifying or refining pandemic spread predictions, health protocols, vaccination offerings, and policy approaches. Additional potential gains of sentiment analysis may include lessening of vaccine hesitancy, reduction in civil disobedience, and most importantly, better healthcare outcomes for individuals and their communities. In this article, we highlight the evolution of select epidemiological models; conduct a critical review of models in terms of the level and depth of modeling of social media, social network factors, and sentiment analysis; and finally, partially illustrate sentiment analysis using COVID-19 Twitter data.

Vaccine Hesitancy towards COVID-19 Vaccination: Investigating the Role of Information Sources through a Mediation Analysis

Mass vaccination campaigns have been implemented worldwide to counteract the SARS-CoV-2/COVID-19 pandemic, however their effectiveness could be challenged by vaccine hesitancy. The tremendous rise in the use of social media have made them acquire a leading role as an information source, thus representing a crucial factor at play that could contribute to increase or mitigate vaccine hesitancy, as information sources play a pivotal role in shaping public opinion and perceptions. The aims of the study were to investigate if information sources could affect the attitude towards COVID-19 vaccination and if they could act as a mediator in the relationship between individual characteristics and vaccine hesitancy. A cross-sectional online survey was conducted by a professional panellist on a representative sample of 1011 citizens from the Emilia-Romagna region in Italy in January 2021. A mediation analysis using structural equation modelling was performed. Our results show how social media directly or indirectly increases vaccine hesitancy towards COVID-19 vaccination, while the opposite effect was observed for institutional websites. Given the global widespread use of social media, their use should be enhanced to disseminate scientifically sound information to a greater audience to counteract vaccine hesitancy, while at the same time continuing to promote and update institutional websites that have proven to be effective in reducing vaccine hesitancy.

Social, economic, and environmental factors influencing the basic reproduction number of COVID-19 across countries

OBJECTIVE

To assess whether the basic reproduction number (R0) of COVID-19 is different across countries and what national-level demographic, social, and environmental factors other than interventions characterize initial vulnerability to the virus.

METHODS

We fit logistic growth curves to reported daily case numbers, up to the first epidemic peak, for 58 countries for which 16 explanatory covariates are available. This fitting has been shown to robustly estimate R0 from the specified period. We then use a generalized additive model (GAM) to discern both linear and nonlinear effects, and include 5 random effect covariates to account for potential differences in testing and reporting that can bias the estimated R0.

FINDINGS

We found that the mean R0 is 1.70 (S.D. 0.57), with a range between 1.10 (Ghana) and 3.52 (South Korea). We identified four factors-population between 20-34 years old (youth), population residing in urban agglomerates over 1 million (city), social media use to organize offline action (social media), and GINI income inequality-as having strong relationships with R0, across countries. An intermediate level of youth and GINI inequality are associated with high R0, (n-shape relationships), while high city population and high social media use are associated with high R0. Pollution, temperature, and humidity did not have strong relationships with R0 but were positive.

CONCLUSION

Countries have different characteristics that predispose them to greater intrinsic vulnerability to COVID-19. Studies that aim to measure the effectiveness of interventions across locations should account for these baseline differences in social and demographic characteristics.

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.

CoViD-19, learning from the past: A wavelet and cross-correlation analysis of the epidemic dynamics looking to emergency calls and Twitter trends in Italian Lombardy region

The first case of Coronavirus Disease 2019 in Italy was detected on February the 20th in Lombardy region. Since that date, Lombardy has been the most affected Italian region by the epidemic, and its healthcare system underwent a severe overload during the outbreak. From a public health point of view, therefore, it is fundamental to provide healthcare services with tools that can reveal possible new health system stress periods with a certain time anticipation, which is the main aim of the present study. Moreover, the sequence of law decrees to face the epidemic and the large amount of news generated in the population feelings of anxiety and suspicion. Considering this whole complex context, it is easily understandable how people "overcrowded" social media with messages dealing with the pandemic, and emergency numbers were overwhelmed by the calls. Thus, in order to find potential predictors of possible new health system overloads, we analysed data both from Twitter and emergency services comparing them to the daily infected time series at a regional level. Particularly, we performed a wavelet analysis in the time-frequency plane, to finely discriminate over time the anticipation capability of the considered potential predictors. In addition, a cross-correlation analysis has been performed to find a synthetic indicator of the time delay between the predictor and the infected time series. Our results show that Twitter data are more related to social and political dynamics, while the emergency calls trends can be further evaluated as a powerful tool to potentially forecast new stress periods. Since we analysed aggregated regional data, and taking into account also the huge geographical heterogeneity of the epidemic spread, a future perspective would be to conduct the same analysis on a more local basis.

Study on an SIHRS Model of COVID-19 Pandemic With Impulse and Time Delay Under Media Coverage

Media coverage plays an important role in prevention and control the spread of COVID-19 during the pandemic. In this paper, an SIHRS model of COVID-19 pandemic with impulse and time delay under media coverage is established. The positive and negative emotions of public are considered by the impact of confirmed cases and medical resources. In order to restrain the negative information of public, the factor of policies and regulations with impulse and time delay is introduced. Furthermore, the system model is simulated and verified by the reported data of COVID-19 pandemic in Wuhan. The main results are as follows: (1) When the implementation rate of the negative information generated by the confirmed cases gradually reduced to 0.4 times, the cumulative confirmed cases will be significantly reduced to about 37000, indicating that the popularization of pandemic related media information should be broad; (2) When the implementation rate affected by the amount of policies and regulations information gradually increases to 3 times, the cumulative confirmed cases will be significantly reduced to about 28000, indicating that the policies and regulations information should be continuously and incrementally reported; (3) When the inhibition rate of policies and regulation information on negative information gradually increases to 3 times, the cumulative confirmed cases will also be significantly reduced to about 27000 cases, indicating that the targeted policies and regulations information has a significant impact on inhibiting the corresponding negative emotions.