Communicating Ebola through social media and electronic news media outlets: A cross-sectional study

Prevention and control of Ebola virus transmission: mathematical modelling and data fitting

The Ebola virus disease (EVD) has been endemic since 1976, and the case fatality rate is extremely high. EVD is spread by infected animals, symptomatic individuals, dead bodies, and contaminated environment. In this paper, we formulate an EVD model with four transmission modes and a time delay describing the incubation period. Through dynamical analysis, we verify the importance of blocking the infection source of infected animals. We get the basic reproduction number without considering the infection source of infected animals. And, it is proven that the model has a globally attractive disease-free equilibrium when the basic reproduction number is less than unity; the disease eventually becomes endemic when the basic reproduction number is greater than unity. Taking the EVD epidemic in Sierra Leone in 2014-2016 as an example, we complete the data fitting by combining the effect of the media to obtain the unknown parameters, the basic reproduction number and its time-varying reproduction number. It is shown by parameter sensitivity analysis that the contact rate and the removal rate of infected group have the greatest influence on the prevalence of the disease. And, the disease-controlling thresholds of these two parameters are obtained. In addition, according to the existing vaccination strategy, only the inoculation ratio in high-risk areas is greater than 0.4, the effective reproduction number can be less than unity. And, the earlier the vaccination time, the greater the inoculation ratio, and the faster the disease can be controlled.

Unveiling Coronasomnia: Pandemic Stress and Sleep Problems During the COVID-19 Outbreak

The COVID-19 pandemic posed an unprecedented challenge to public well-being, necessitating an examination of its health impact. This review discusses the relationship between pandemic-induced stressors and individual sleep patterns and quality. The pandemic stressors include lockdown or physical distancing measures, direct virus exposure, and the dissemination of misinformation and disinformation. The pandemic led to delayed sleep-wake cycles, except for healthcare professionals, and worsened sleep quality. The prevalence of insomnia was higher for women due to pre-existing conditions and susceptibility stressors such as lockdown stress and family responsibilities. Healthcare professionals, who experienced worsened work conditions during the pandemic, reported higher rates of insomnia and sleep difficulties due to infection anxiety and post-traumatic stress from direct virus exposure. For the general population, stress stemmed from social isolation under lockdown and overwhelming false information available online, resulting in sleep problems. Taken together, the findings highlight the importance of promoting social interactions, providing psychological support services, and caution in navigating health information. In summary, this review underscores the need for individual- and group-centered approaches in ongoing research and interventions to address pandemic-related stress and sleep issues during COVID-19.

Development of an early alert model for pandemic situations in Germany

The COVID-19 pandemic has pointed out the need for new technical approaches to increase the preparedness of healthcare systems. One important measure is to develop innovative early warning systems. Along those lines, we first compiled a corpus of relevant COVID-19 related symptoms with the help of a disease ontology, text mining and statistical analysis. Subsequently, we applied statistical and machine learning (ML) techniques to time series data of symptom related Google searches and tweets spanning the time period from March 2020 to June 2022. In conclusion, we found that a long-short-term memory (LSTM) jointly trained on COVID-19 symptoms related Google Trends and Twitter data was able to accurately forecast up-trends in classical surveillance data (confirmed cases and hospitalization rates) 14 days ahead. In both cases, F1 scores were above 98% and 97%, respectively, hence demonstrating the potential of using digital traces for building an early alert system for pandemics in Germany.

The effect of fear and situational motivation on online information avoidance: The case of COVID-19

During the COVID-19 pandemic, a plethora of online sources for information and news dissemination have emerged. Extant research suggests that very quickly, individuals become disinterested and begin avoiding the information. In this study, we investigate how an individual's fear and situational motivation impact Online Information Avoidance. Using the self-determination theory and information avoidance theories, we argue that fear and external regulation are associated with increased Online Information Avoidance. We also argue that intrinsic motivation and identified regulation are associated with a decrease in Online Information Avoidance. Our findings suggest that fear, intrinsic motivation, and external regulation drive Online Information Avoidance, where intrinsic motivation is the most significant driver. We also found that identified regulation is a crucial inhibitor of Online Information Avoidance. While focusing on COVID-19, our study contributes to the broader information systems research literature and specifically to the information avoidance literature during a pandemic or a prolonged crisis. Our study's findings will be useful for governments, health organizations, and communities that utilize online platforms, forums, and related outlets to reach larger audiences for disseminating pertinent information and recommendations during a crisis.

Factors affecting consumer attitudes towards using digital media platforms on health knowledge communication: Findings of cognition-affect-conation pattern

As the world enters the second year of the pandemic, many posts are marketed to promote products under the guise of spreading knowledge to fulfill the users' requirement for health knowledge. Current research, however, has primarily focused on methods to improve health literacy rather than the impact of this new form. To remedy this deficiency, this research use Cognition-Affect-Conation Pattern (CAC) to investigate digital media users and to determine attitudes towards this new form and influential. Data was collected in December 2021 from 314 users in Guangzhou, China, via a Likert-type scale developed by the researchers. Regression analyzes were also performed. Research has shown that consumer attitudes in health knowledge marketing fall within the standard learning hierarchy, with consumer self-cognition and information quality cognitions showing a positive relationship with their affect and conation. At the same time, affect did play a mediating role in this model. The results of our study provide constructive solutions for companies to optimize the communication environment of health spread in society. The findings not only provide researchers with a new perspective to study the impact of interactive digital media communication on health knowledge, but also help identify users' information needs and formulate effective strategies to increase consumers' understanding and application of health knowledge and products. Providing health knowledge content and leveraging digital media to develop well-established communication channels is important to foster relationships with customers.

Social media in health communication: A literature review of information quality

BACKGROUND

Social media is used in health communication by individuals, health professionals, disease centres and other health regulatory bodies. However, varying degrees of information quality are churned out daily on social media. This review is concerned with the quality of Social Media Health Information (SMHI).

OBJECTIVE

The review sought to understand how SMHI quality issues have been framed and addressed in the literature. Health topics, users and social media platforms that have raised health information quality concerns are reviewed. The review also looked at the suitability of existing criteria and instruments used in evaluating SMHI and identified gaps for future research.

METHOD

The Preferred Reporting Items for Systematic Reviews and Meta-Analyses and the forward chaining strategy were used in the document search. Data were sourced according to inclusion criteria from five academic databases, namely Scopus, Web of Science, Cochrane Library, PubMed and MEDLINE.

RESULTS

A total of 93 articles published between 2000 and 2019 were used in the review. The review revealed a worrying trend of health content and communication on social media, especially of cancer, dental care and diabetes information on YouTube. The review further discovered that the Journal of the American Medical Association, the DISCERN and the Health on the Net Foundation, which were designed before the advent of social media, continue to be used as quality evaluation instruments for SMHI, even though technical and user characteristics of social media differ from traditional portals such as websites.

CONCLUSION

The study synthesises varied opinions on SMHI quality in the literature and recommends that future research proposes quality evaluation criteria and instruments specifically for SMHI.

Use of equity-informed social media COVID-19 risk communication tools: a scoping review protocol

INTRODUCTION

Health agencies and community organisations play a crucial role in disseminating information to the public about COVID-19 risks and events, providing instructions on how to change behaviour to mitigate those risks, motivating compliance with health directives and addressing false information. Social media platforms are a critical tool in risk communication, providing a medium for rapid transmission of messages as well as providing the opportunity for engagement and immediate feedback. Access to health information, services and support are especially important for marginalised and underserved ('equity-deserving') populations who are disproportionately affected by COVID-19. This scoping review aims to review the breadth and depth of the academic and grey literature on equity-informed social media risk communication tools to provide guidance on promising practices and principles for reaching equity-deserving populations through social media.

METHODS AND ANALYSIS

Arksey and O'Malley's (2005) framework guided the identification of the research question; identification and selection of relevant studies from electronic databases and hand-searches of discipline-specific journals; extraction and charting of the data; and collating and reporting of findings. The results of the screening process will be reported using the Preferred Reporting Items for Systematic Review and Meta-Analysis-Scoping Review guidelines.

FINDINGS

We will identify reported facilitators and barriers to the development of risk communications that target equity-deserving communities. We will also identify recommendations for equity-informed risk communication for COVID-19.

ETHICS AND DISSEMINATION

This study does not require ethics approval. We intend to disseminate the results through publication in an open-access peer-reviewed journal, conference presentations, lay summaries (eg, checklists) for health organisations and messages to be shared through social media.

Microblog data analysis of emotional reactions to COVID-19 in China

To explore the emotional attitudes of microblog users in the different COVID-19 stages in China, this study used data mining and machine-learning methods to crawl 112,537 Sina COVID-19- related microblogs and conduct sentiment and group difference analyses. It was found that: (1) the microblog users' emotions shifted from negative to positive from the second COVID-19 pandemic phase; (2) there were no significant differences in the microblog users' emotions in the different regions; (3) males were more optimistic than females in the early stages of the pandemic; however, females were more optimistic than males in the last three stages; and (4) females posted more microblogs and expressed more sadness and fear while males expressed more anger and disgust. This research captured online information in real-time, with the results providing a reference for future research into public opinion and emotional reactions to crises.

A comparative analysis of anti-vax discourse on twitter before and after COVID-19 onset

This study aimed to identify and assess the prevalence of vaccine-hesitancy-related topics on Twitter in the periods before and after the Coronavirus Disease 2019 (COVID-19) outbreak. Using a search query, 272,780 tweets associated with anti-vaccine topics and posted between 1 January 2011, and 15 January 2021, were collected. The tweets were classified into a list of 11 topics and analyzed for trends during the periods before and after the onset of COVID-19. Since the beginning of COVID-19, the percentage of anti-vaccine tweets has increased for two topics, “government and politics” and “conspiracy theories,” and decreased for “developmental disabilities.” Compared to tweets regarding flu and measles, mumps, and rubella vaccines, those concerning COVID-19 vaccines showed larger percentages for the topics of conspiracy theories and alternative treatments, and a lower percentage for developmental disabilities. The results support existing anti-vaccine literature and the assertion that anti-vaccine sentiments are an important public-health issue.

An optimal control policy in fighting COVID-19 and infectious diseases

When an outbreak starts spreading, policymakers have to make decisions that affect the health of their citizens and the economy. Some might induce harsh measures, such as a lockdown. Following a long, harsh lockdown, the recession forces policymakers to rethink reopening. To provide an effective strategy, here we propose a control strategy model. Our model assesses the trade-off between social performance and limited medical resources by determining individuals' propensities. The proposed strategy also helps decision-makers to find optimal lockdown and exit strategies for each region. Moreover, the financial loss is minimized. We use the public sentiment information during the pandemic to determine the percentage of individuals with high-risk behavior and the percentage of individuals with low-risk behavior. Hence, we propose an online platform using fear-sentiment information to estimate the personal protective equipment (PPE) burn rate overtime for the entire population. In addition, a study of a COVID-19 dataset for Los Angeles County is performed to validate our model and its results. The total social cost reduces by 18% compared with a control strategy where susceptible individuals are assumed to be homogeneous. We also reduce the total social costs by 26% and 22% compared to other strategies that consider the health-care cost or the social performance cost, respectively.

Assessing the Role of Social Bots During the COVID-19 Pandemic: Infodemic, Disagreement, and Criticism

BACKGROUND

Social media has changed the way we live and communicate, as well as offering unprecedented opportunities to improve many aspects of our lives, including health promotion and disease prevention. However, there is also a darker side to social media that is not always as evident as its possible benefits. In fact, social media has also opened the door to new social and health risks that are linked to health misinformation.

OBJECTIVE

This study aimed to study the role of social media bots during the COVID-19 outbreak.

METHODS

The Twitter streaming API was used to collect tweets regarding COVID-19 during the early stages of the outbreak. The Botometer tool was then used to obtain the likelihood of whether each account is a bot or not. Bot classification and topic-modeling techniques were used to interpret the Twitter conversation. Finally, the sentiment associated with the tweets was compared depending on the source of the tweet.

RESULTS

Regarding the conversation topics, there were notable differences between the different accounts. The content of nonbot accounts was associated with the evolution of the pandemic, support, and advice. On the other hand, in the case of self-declared bots, the content consisted mainly of news, such as the existence of diagnostic tests, the evolution of the pandemic, and scientific findings. Finally, in the case of bots, the content was mostly political. Above all, there was a general overriding tone of criticism and disagreement. In relation to the sentiment analysis, the main differences were associated with the tone of the conversation. In the case of self-declared bots, this tended to be neutral, whereas the conversation of normal users scored positively. In contrast, bots tended to score negatively.

CONCLUSIONS

By classifying the accounts according to their likelihood of being bots and performing topic modeling, we were able to segment the Twitter conversation regarding COVID-19. Bot accounts tended to criticize the measures imposed to curb the pandemic, express disagreement with politicians, or question the veracity of the information shared on social media.

Public Social Media Discussions on Agricultural Product Safety Incidents: Chinese African Swine Fever Debate on Weibo

Public concern over major agricultural product safety incidents, such as swine flu and avian flu, can intensify financial losses in the livestock and poultry industries. Crawler technology were applied to reviewed the Weibo social media discussions on the African Swine Fever (ASF) incident in China that was reported on 3 August 2018, and used content analysis and network analysis to specifically examine the online public opinion network dissemination characteristics of verified individual users, institutional users and ordinary users. It was found that: (1) attention paid to topics related to "epidemic," "treatment," "effect" and "prevent" decrease in turn, with the interest in "prevent" increasing significantly when human infections were possible; (2) verified individual users were most concerned about epidemic prevention and control and play a supervisory role, the greatest concern of institutional users and ordinary users were issues related to agricultural industry and agricultural products price fluctuations respectively; (3) among institutional users, media was the main opinion leader, and among non-institutional users, elites from all walks of life, especially the food safety personnel acted as opinion leaders. Based on these findings, some policy suggestions are given: determine the nature of the risk to human health of the safety incident, stabilizing prices of relevant agricultural products, and giving play to the role of information dissemination of relevant institutions.

Engaging social media users with attitudinal messages during health crisis communication

To cope with the COVID-19 pandemic, various policy measures accompanied by health crisis communication were adopted in China to engage publics. In this study, we investigated how People's Daily communicated COVID-19 messages on Weibo. Drawing on the Appraisal Framework, we developed a three-stage mixed method approach to study 400 COVID-19 posts to identify the attitude resources employed and their association with public engagement. We found that attitudinal posts were more engaging than non-attitudinal posts. Judgment, both positive and negative, was positively associated with public engagement, whereas the use of positive Affect and Appreciation could be ineffective. These findings contribute to the understanding of how public engagement on social media can be enhanced through the use of attitudinal messages in health emergencies.

Towards a Bibliometric Mapping of Network Public Opinion Studies

To grasp the current status of network public opinion (NPO) research and explore the knowledge base and hot trends from a quantitative perspective, we retrieved 1385 related papers and conducted a bibliometric mapping analysis on them. Co-occurrence analysis, cluster analysis, co-citation analysis and keyword burst analysis were performed using VOSviewer and CiteSpace software. The results show that the NPO is mainly distributed in the disciplinary fields associated with journalism and communication and public management. There are four main hotspots: analysis of public opinion, analysis of communication channels, technical means and challenges faced. The knowledge base in the field of NPO research includes social media, user influence, and user influence related to opinion dynamic modeling and sentiment analysis. With the advent of the era of big data, big data technology has been widely used in various fields and to some extent can be said to be the research frontier in the field. Transforming big data public opinion into early warning, realizing in-depth analysis and accurate prediction of public opinion as well as improving decision-making ability of public opinion are the future research directions of NPO.

Evaluating Rumor Debunking Effectiveness During the COVID-19 Pandemic Crisis: Utilizing User Stance in Comments on Sina Weibo

Background: The spread of rumors related to COVID-19 on social media has posed substantial challenges to public health governance, and thus exposing rumors and curbing their spread quickly and effectively has become an urgent task. This study aimed to assist in formulating effective strategies to debunk rumors and curb their spread on social media.

Methods: A total of 2,053 original postings and 100,348 comments that replied to the postings of five false rumors related to COVID-19 (dated from January 20, 2020, to June 28, 2020) belonging to three categories, authoritative, social, and political, on Sina Weibo in China were randomly selected. To study the effectiveness of different debunking methods, a new annotation scheme was proposed that divides debunking methods into six categories: denial, further fact-checking, refutation, person response, organization response, and combination methods. Text classifiers using deep learning methods were built to automatically identify four user stances in comments that replied to debunking postings: supporting, denying, querying, and commenting stances. Then, based on stance responses, a debunking effectiveness index (DEI) was developed to measure the effectiveness of different debunking methods.

Results: The refutation method with cited evidence has the best debunking effect, whether used alone or in combination with other debunking methods. For the social category of Car rumor and political category of Russia rumor, using the refutation method alone can achieve the optimal debunking effect. For authoritative rumors, a combination method has the optimal debunking effect, but the most effective combination method requires avoiding the use of a combination of a debunking method where the person or organization defamed by the authoritative rumor responds personally and the refutation method.

Conclusion: The findings provide relevant insights into ways to debunk rumors effectively, support crisis management of false information, and take necessary actions in response to rumors amid public health emergencies.

Analysis of social media data for public emotion on the Wuhan lockdown event during the COVID-19 pandemic

BACKGROUND

With outbreaks of COVID-19 around the world, lockdown restrictions are routinely imposed to limit the spread of the virus. During periods of lockdown, social media has become the main channel for citizens to exchange information with others. Public emotions are being generated and shared rapidly online with citizens using internet platforms to reduce anxiety and stress, and stay connected while isolated.

OBJECTIVES

This study aims to explore the regularity of emotional evolution by examining public emotions expressed in online discussions about the Wuhan lockdown event in January 2020.

METHODS

Data related to the Wuhan lockdown was collected from Sina Weibo by web crawler. In this study, the Ortony, Clore, and Collins (OCC) model, Word2Vec, and Bi-directional Long Short-Term Memory model were employed to determine emotional types, train vectorization of words, and identify each text emotion for the training set. Latent Dirichlet Allocation models were also employed to mine the various topic categories, while topic emotional evolution was visualized.

RESULTS

Seven types of emotions and four phases were categorized to describe emotional evolution on the Wuhan lockdown event. The study found that negative emotions such as blame and fear dominated in the early days, and public attitudes towards the lockdown gradually alleviated and reached a balance as the situation improved. Emotional expression about Wuhan lockdown event were significantly related to users' gender, location, and whether or not their account was verified. There were statistically significant correlations between different emotions within the subtle emotional categories. In addition, the evolution of emotions presented a different path due to different topics.

CONCLUSIONS

Multiple emotional categories were determined in our study, providing a detailed and explainable emotion analysis to explored emotional appeal of citizen. The public emotions were gradually easing related to the Wuhan lockdown event, there yet exists regional discrimination and post-traumatic stress disorder in this process, which would lead us to pay continuous attention to citizens lives and psychological status post-pandemic. In addition, this study provided an appropriate method and reference case for the government's public opinion control and emotional appeasement.

A study of Chinese college students' COVID-19-related information needs and seeking behavior

Purpose

To better meet people's information needs under the public health crisis, this study explored the information needs and seeking behavior of college students in the context of the COVID-19 pandemic.

Design/methodology/approach

This research utilized a survey and the convenience snowball sampling method. A sample of 400 college students was obtained.

Findings

The results revealed that college students have paid great attention to COVID-19-related information. They sought the information mainly for personal protection, obtaining the latest information and seeking information for family and friends. Their COVID-19-related information needs were diverse; at different stages, the concerns were different. Social media and mass media were both found to be the most useful tools for seeking COVID-19 information. Too much repetitive information, unclear authoritative and reliable informational sources, and difficulties in judging the authenticity of the information were the most common obstacles for them in information seeking. Nevertheless, the majority of the students agreed that they were very satisfied and somewhat satisfied with the current information services. This did not affect the students' understanding of the importance of health services provided by libraries.

Originality/value

Insights into the people's information needs and information-seeking behavior of this study could help the information providers to offer appropriate information in better ways to improve the public's virus protection capabilities and maintain the stability of social order.

Delivering eye health education to deprived communities in India through a social media-based innovation

In this article, Chandrani Maitra, with her supervisor, Prof. Jennifer Rowley, reports on her PhD research conducted at Manchester Metropolitan University. This research aimed to develop understanding of the benefits of, and the challenges associated with the use of social media to disseminate eye health information in deprived communities in India. Such communities typically have a low level of access to health information, as the result of poor literacy, poverty, lack of women's empowerment, cultural practices, society dynamics, and medical malpractice. This study used an intervention based on the social media platform, WhatsApp, to educate a group of women volunteers so that they were able to contribute to the management of the eye health of their family, friends, and neighbours. Interviews were conducted with deprived community members (DCMs), community healthcare advocates (CHAs) and, healthcare professionals (HCPs). The DCMs reported a number of benefits associated with their participation in the eye health intervention. CHAs and HCPs agreed that an extended roll out of the intervention had potential to deliver benefits, but expressed concerns that some of the ongoing social challenges facing deprived communities might act as barriers to progress. F.J.

Determinants of Infodemics During Disease Outbreaks: A Systematic Review

Background: The widespread use of social media represents an unprecedented opportunity for health promotion. We have more information and evidence-based health related knowledge, for instance about healthy habits or possible risk behaviors. However, these tools also carry some disadvantages since they also open the door to new social and health risks, in particular during health emergencies. This systematic review aims to study the determinants of infodemics during disease outbreaks, drawing on both quantitative and qualitative methods. Methods: We searched research articles in PubMed, Scopus, Medline, Embase, CINAHL, Sociological abstracts, Cochrane Library, and Web of Science. Additional research works were included by searching bibliographies of electronically retrieved review articles. Results: Finally, 42 studies were included in the review. Five determinants of infodemics were identified: (1) information sources; (2) online communities' structure and consensus; (3) communication channels (i.e., mass media, social media, forums, and websites); (4) messages content (i.e., quality of information, sensationalism, etc.,); and (5) context (e.g., social consensus, health emergencies, public opinion, etc.). Studied selected in this systematic review identified different measures to combat misinformation during outbreaks. Conclusion: The clarity of the health promotion messages has been proven essential to prevent the spread of a particular disease and to avoid potential risks, but it is also fundamental to understand the network structure of social media platforms and the emergency context where misinformation might dynamically evolve. Therefore, in order to prevent future infodemics, special attention will need to be paid both to increase the visibility of evidence-based knowledge generated by health organizations and academia, and to detect the possible sources of mis/disinformation.

Influence of Online Social Support on the Public's Belief in Overcoming COVID-19

The sudden outbreak of coronavirus disease 2019 (COVID-19) in early 2020 has dramatically changed people's lives. Some countries have taken mass home quarantine to control the virus. However, the side effects of quarantine have rarely been interrogated by current COVID-19 research. This study thus investigates the effects of online social support on the public's beliefs in overcoming COVID-19 by embracing their cognition and emotion during the epidemic. First, by crawling and content analysis of the messages posted on "Baidu COVID-19 bar", this study identified 5 types of online social support given or received by the public during COVID-19. On this basis, a model explaining the public's beliefs was developed from the perspectives of online social support, cognition and emotion. 334 valid online questionnaires were collected to examine the proposed model and hypotheses. The results show that cognition has a direct effect on the belief, while emotion affects the belief via a full mediating effect of cognition. Tangible support and esteem support can directly affect the public's beliefs, and educational level significantly moderates these effects. In addition, the public's cognition is influenced by informational support, however, emotion is not influenced by social support but by other factors (e.g., information disclosure, material supplies and frustration caused by the epidemic). These research results provide a deep insight into how to reduce the negative effects of quarantine, consolidate the theoretical basis of the public's beliefs, and have important practical implications for individuals and the government in dealing with such emergencies.

Social media: A new tool for outbreak surveillance

Social media platforms allow users to share news, ideas, thoughts, and opinions on a global scale. Data processing methods allow researchers to automate the collection and interpretation of social media posts for efficient and valuable disease surveillance. Data derived from social media and internet search trends have been used successfully for monitoring and forecasting disease outbreaks such as Zika, Dengue, MERS, and Ebola viruses. More recently, data derived from social media have been used to monitor and model disease incidence during the coronavirus disease 2019 (COVID-19) pandemic. We discuss the use of social media for disease surveillance.

Promoting Public Engagement during the COVID-19 Crisis: How Effective Is the Wuhan Local Government's Information Release?

During times of public crises (such as COVID-19), governments must act swiftly to release crisis information effectively and efficiently to the public. This paper provides a general overview of the way that the Wuhan local government use Weibo as a channel to engage with their citizens during the COVID-19 pandemic. Based on the media richness, dialogic loop, and a series of theoretically relevant factors, such as content type, text length, and information source, we try to examine how citizen engage with their local government. By analyzing the data mining samples from Wuhan Release, the official Sina Weibo account of Wuhan's local government, results show that, despite the unstable situation COVID-19 over the crisis, there exist three stages of a crisis on the whole. Combining the behavior of the government and the public, duration from 31 December 2019 to 19 January 2020 could be seen as the development period, then the outbreak period (30 January 2020 to 28 February 2020), and a grace period (29 February 2020 to19 April 2020). Public attention to different types of information changes over time, but curbing rumors has always been a priority. Media richness features partially influent citizen engagement. Text length is significantly positively associated with citizen engagement through government social media. However, posts containing information sources have a negative impact on citizen engagement.

Associations Between COVID-19 Misinformation Exposure and Belief With COVID-19 Knowledge and Preventive Behaviors: Cross-Sectional Online Study

BACKGROUND

Online misinformation proliferation during the COVID-19 pandemic has become a major public health concern.

OBJECTIVE

We aimed to assess the prevalence of COVID-19 misinformation exposure and beliefs, associated factors including psychological distress with misinformation exposure, and the associations between COVID-19 knowledge and number of preventive behaviors.

METHODS

A cross-sectional online survey was conducted with 1049 South Korean adults in April 2020. Respondents were asked about receiving COVID-19 misinformation using 12 items identified by the World Health Organization. Logistic regression was used to compute adjusted odds ratios (aORs) for the association of receiving misinformation with sociodemographic characteristics, source of information, COVID-19 misinformation belief, and psychological distress, as well as the associations of COVID-19 misinformation belief with COVID-19 knowledge and the number of COVID-19 preventive behaviors among those who received the misinformation. All data were weighted according to the Korea census data in 2018.

RESULTS

Overall, 67.78% (n=711) of respondents reported exposure to at least one COVID-19 misinformation item. Misinformation exposure was associated with younger age, higher education levels, and lower income. Sources of information associated with misinformation exposure were social networking services (aOR 1.67, 95% CI 1.20-2.32) and instant messaging (aOR 1.79, 1.27-2.51). Misinformation exposure was also associated with psychological distress including anxiety (aOR 1.80, 1.24-2.61), depressive (aOR 1.47, 1.09-2.00), and posttraumatic stress disorder symptoms (aOR 1.97, 1.42-2.73), as well as misinformation belief (aOR 7.33, 5.17-10.38). Misinformation belief was associated with poorer COVID-19 knowledge (high: aOR 0.62, 0.45-0.84) and fewer preventive behaviors (≥7 behaviors: aOR 0.54, 0.39-0.74).

CONCLUSIONS

COVID-19 misinformation exposure was associated with misinformation belief, while misinformation belief was associated with fewer preventive behaviors. Given the potential of misinformation to undermine global efforts in COVID-19 disease control, up-to-date public health strategies are required to counter the proliferation of misinformation.

The social life of COVID-19: Early insights from social media monitoring data collected in Poland

Research has revealed that social media data may be promising in many health threats and help to understand how people respond to them. As the outbreak of a novel coronavirus disease (COVID-19) is a global pandemic, a real-time social media monitoring is needed to know the scale of this phenomenon. We have reported the frequency, reach and impact of online mentions about the COVID-19 illness taken from social media platforms: Facebook, Instagram, Twitter, blogs, forums, and news portals to highlight and better understand the scope of coronavirus discussion in Poland. We used SentiOne social listening tool to gather the data and perform the monitoring between 24 February 2020 to 25 March 2020. We found a total of 1,415,750 mentions related to COVID-19 which gives the average 47,192 mentions per day. 95.36% (1,350,059) of mentions were people's updates and expressions, 4.64% (65,691) mentions were articles from news portals and social media. Males have dominated the online conversation about COVID-19 (65.32% vs 34.68% females). At the same time, women were more likely to discuss the topic on social media platforms such as: Facebook, Twitter, and Instagram. We concluded with theoretical and practical implications.

Grappling With the COVID-19 Health Crisis: Content Analysis of Communication Strategies and Their Effects on Public Engagement on Social Media

Background

The coronavirus disease (COVID-19) has posed an unprecedented challenge to governments worldwide. Effective government communication of COVID-19 information with the public is of crucial importance.

Objective

We investigate how the most-read state-owned newspaper in China, People’s Daily, used an online social networking site, Sina Weibo, to communicate about COVID-19 and whether this could engage the public. The objective of this study is to develop an integrated framework to examine the content, message style, and interactive features of COVID-19–related posts and determine their effects on public engagement in the largest social media network in China.

Methods

Content analysis was employed to scrutinize 608 COVID-19 posts, and coding was performed on three main dimensions: content, message style, and interactive features. The content dimension was coded into six subdimensions: action, new evidence, reassurance, disease prevention, health care services, and uncertainty, and the style dimension was coded into the subdimensions of narrative and nonnarrative. As for interactive features, they were coded into links to external sources, use of hashtags, use of questions to solicit feedback, and use of multimedia. Public engagement was measured in the form of the number of shares, comments, and likes on the People’s Daily’s Sina Weibo account from January 20, 2020, to March 11, 2020, to reveal the association between different levels of public engagement and communication strategies. A one-way analysis of variance followed by a post-hoc Tukey test and negative binomial regression analysis were employed to generate the results.

Results

We found that although the content frames of action, new evidence, and reassurance delivered in a nonnarrative style were predominant in COVID-19 communication by the government, posts related to new evidence and a nonnarrative style were strong negative predictors of the number of shares. In terms of generating a high number of shares, it was found that disease prevention posts delivered in a narrative style were able to achieve this purpose. Additionally, an interaction effect was found between content and style. The use of a narrative style in disease prevention posts had a significant positive effect on generating comments and likes by the Chinese public, while links to external sources fostered sharing.

Conclusions

These results have implications for governments, health organizations, medical professionals, the media, and researchers on their epidemic communication to engage the public. Selecting suitable communication strategies may foster active liking and sharing of posts on social media, which in turn, might raise the public’s awareness of COVID-19 and motivate them to take preventive measures. The sharing of COVID-19 posts is particularly important because this action can reach out to a large audience, potentially helping to contain the spread of the virus.

An "Infodemic": Leveraging High-Volume Twitter Data to Understand Early Public Sentiment for the Coronavirus Disease 2019 Outbreak

BACKGROUND

Twitter has been used to track trends and disseminate health information during viral epidemics. On January 21, 2020, the Centers for Disease Control and Prevention activated its Emergency Operations Center and the World Health Organization released its first situation report about coronavirus disease 2019 (COVID-19), sparking significant media attention. How Twitter content and sentiment evolved in the early stages of the COVID-19 pandemic has not been described.

METHODS

We extracted tweets matching hashtags related to COVID-19 from January 14 to 28, 2020 using Twitter's application programming interface. We measured themes and frequency of keywords related to infection prevention practices. We performed a sentiment analysis to identify the sentiment polarity and predominant emotions in tweets and conducted topic modeling to identify and explore discussion topics over time. We compared sentiment, emotion, and topics among the most popular tweets, defined by the number of retweets.

RESULTS

We evaluated 126 049 tweets from 53 196 unique users. The hourly number of COVID-19-related tweets starkly increased from January 21, 2020 onward. Approximately half (49.5%) of all tweets expressed fear and approximately 30% expressed surprise. In the full cohort, the economic and political impact of COVID-19 was the most commonly discussed topic. When focusing on the most retweeted tweets, the incidence of fear decreased and topics focused on quarantine efforts, the outbreak and its transmission, as well as prevention.

CONCLUSIONS

Twitter is a rich medium that can be leveraged to understand public sentiment in real-time and potentially target individualized public health messages based on user interest and emotion.

An "Infodemic": Leveraging High-Volume Twitter Data to Understand Early Public Sentiment for the Coronavirus Disease 2019 Outbreak

Background

Twitter has been used to track trends and disseminate health information during viral epidemics. On January 21, 2020, the Centers for Disease Control and Prevention activated its Emergency Operations Center and the World Health Organization released its first situation report about coronavirus disease 2019 (COVID-19), sparking significant media attention. How Twitter content and sentiment evolved in the early stages of the COVID-19 pandemic has not been described.

Methods

We extracted tweets matching hashtags related to COVID-19 from January 14 to 28, 2020 using Twitter’s application programming interface. We measured themes and frequency of keywords related to infection prevention practices. We performed a sentiment analysis to identify the sentiment polarity and predominant emotions in tweets and conducted topic modeling to identify and explore discussion topics over time. We compared sentiment, emotion, and topics among the most popular tweets, defined by the number of retweets.

Results

We evaluated 126 049 tweets from 53 196 unique users. The hourly number of COVID-19-related tweets starkly increased from January 21, 2020 onward. Approximately half (49.5%) of all tweets expressed fear and approximately 30% expressed surprise. In the full cohort, the economic and political impact of COVID-19 was the most commonly discussed topic. When focusing on the most retweeted tweets, the incidence of fear decreased and topics focused on quarantine efforts, the outbreak and its transmission, as well as prevention.

Conclusions

Twitter is a rich medium that can be leveraged to understand public sentiment in real-time and potentially target individualized public health messages based on user interest and emotion.

Mining the Characteristics of COVID-19 Patients in China: Analysis of Social Media Posts

BACKGROUND

In December 2019, pneumonia cases of unknown origin were reported in Wuhan City, Hubei Province, China. Identified as the coronavirus disease (COVID-19), the number of cases grew rapidly by human-to-human transmission in Wuhan. Social media, especially Sina Weibo (a major Chinese microblogging social media site), has become an important platform for the public to obtain information and seek help.

OBJECTIVE

This study aims to analyze the characteristics of suspected or laboratory-confirmed COVID-19 patients who asked for help on Sina Weibo.

METHODS

We conducted data mining on Sina Weibo and extracted the data of 485 patients who presented with clinical symptoms and imaging descriptions of suspected or laboratory-confirmed cases of COVID-19. In total, 9878 posts seeking help on Sina Weibo from February 3 to 20, 2020 were analyzed. We used a descriptive research methodology to describe the distribution and other epidemiological characteristics of patients with suspected or laboratory-confirmed SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) infection. The distance between patients' home and the nearest designated hospital was calculated using the geographic information system ArcGIS.

RESULTS

All patients included in this study who sought help on Sina Weibo lived in Wuhan, with a median age of 63.0 years (IQR 55.0-71.0). Fever (408/485, 84.12%) was the most common symptom. Ground-glass opacity (237/314, 75.48%) was the most common pattern on chest computed tomography; 39.67% (167/421) of families had suspected and/or laboratory-confirmed family members; 36.58% (154/421) of families had 1 or 2 suspected and/or laboratory-confirmed members; and 70.52% (232/329) of patients needed to rely on their relatives for help. The median time from illness onset to real-time reverse transcription-polymerase chain reaction (RT-PCR) testing was 8 days (IQR 5.0-10.0), and the median time from illness onset to online help was 10 days (IQR 6.0-12.0). Of 481 patients, 32.22% (n=155) lived more than 3 kilometers away from the nearest designated hospital.

CONCLUSIONS

Our findings show that patients seeking help on Sina Weibo lived in Wuhan and most were elderly. Most patients had fever symptoms, and ground-glass opacities were noted in chest computed tomography. The onset of the disease was characterized by family clustering and most families lived far from the designated hospital. Therefore, we recommend the following: (1) the most stringent centralized medical observation measures should be taken to avoid transmission in family clusters; and (2) social media can help these patients get early attention during Wuhan's lockdown. These findings can help the government and the health department identify high-risk patients and accelerate emergency responses following public demands for help.

Misinformation and the US Ebola communication crisis: analyzing the veracity and content of social media messages related to a fear-inducing infectious disease outbreak

Background

The Ebola communication crisis of 2014 generated widespread fear and attention among Western news media, social media users, and members of the United States (US) public. Health communicators need more information on misinformation and the social media environment during a fear-inducing disease outbreak to improve communication practices. The purpose of this study was to describe the content of Ebola-related tweets with a specific focus on misinformation, political content, health related content, risk framing, and rumors.

Methods

We examined tweets from a random 1% sample of all tweets published September 30th - October 30th, 2014, filtered for English-language tweets mentioning “Ebola” in the content or hashtag, that had at least 1 retweet (N = 72,775 tweets). A randomly selected subset of 3639 (5%) tweets were evaluated for inclusion. We analyzed the 3113 tweets that meet inclusion criteria using public health trained human coders to assess tweet characteristics (joke, opinion, discord), veracity (true, false, partially false), political context, risk frame, health context, Ebola specific messages, and rumors. We assessed the proportion of tweets with specific content using descriptive statistics and chi-squared tests.

Results

Of non-joke tweets, 10% of Ebola-related tweets contained false or partially false information. Twenty-five percent were related to politics, 28% contained content that provoked reader response or promoted discord, 42% contained risk elevating messages and 72% were related to health. The most frequent rumor mentioned focused on government conspiracy. When comparing tweets with true information to tweets with misinformation, a greater percentage of tweets with misinformation were political in nature (36% vs 15%) and contained discord-inducing statements (45% vs 10%). Discord-inducing statements and political messages were both significantly more common in tweets containing misinformation compared with those without(p < 0.001).

Conclusions

Results highlight the importance of anticipating politicization of disease outbreaks, and the need for policy makers and social media companies to build partnerships and develop response frameworks in advance of an event. While each public health event is different, our findings provide insight into the possible social media environment during a future epidemic and could help optimize potential public health communication strategies.

COVID-19-Related Web Search Behaviors and Infodemic Attitudes in Italy: Infodemiological Study

BACKGROUND

Since the beginning of the novel coronavirus disease (COVID-19) outbreak, fake news and misleading information have circulated worldwide, which can profoundly affect public health communication.

OBJECTIVE

We investigated online search behavior related to the COVID-19 outbreak and the attitudes of "infodemic monikers" (ie, erroneous information that gives rise to interpretative mistakes, fake news, episodes of racism, etc) circulating in Italy.

METHODS

By using Google Trends to explore the internet search activity related to COVID-19 from January to March 2020, article titles from the most read newspapers and government websites were mined to investigate the attitudes of infodemic monikers circulating across various regions and cities in Italy. Search volume values and average peak comparison (APC) values were used to analyze the results.

RESULTS

Keywords such as "novel coronavirus," "China coronavirus," "COVID-19," "2019-nCOV," and "SARS-COV-2" were the top infodemic and scientific COVID-19 terms trending in Italy. The top five searches related to health were "face masks," "amuchina" (disinfectant), "symptoms of the novel coronavirus," "health bulletin," and "vaccines for coronavirus." The regions of Umbria and Basilicata recorded a high number of infodemic monikers (APC weighted total >140). Misinformation was widely circulated in the Campania region, and racism-related information was widespread in Umbria and Basilicata. These monikers were frequently searched (APC weighted total >100) in more than 10 major cities in Italy, including Rome.

CONCLUSIONS

We identified a growing regional and population-level interest in COVID-19 in Italy. The majority of searches were related to amuchina, face masks, health bulletins, and COVID-19 symptoms. Since a large number of infodemic monikers were observed across Italy, we recommend that health agencies use Google Trends to predict human behavior as well as to manage misinformation circulation in Italy.

Chinese Public's Attention to the COVID-19 Epidemic on Social Media: Observational Descriptive Study

BACKGROUND

Since the coronavirus disease (COVID-19) epidemic in China in December 2019, information and discussions about COVID-19 have spread rapidly on the internet and have quickly become the focus of worldwide attention, especially on social media.

OBJECTIVE

This study aims to investigate and analyze the public's attention to events related to COVID-19 in China at the beginning of the COVID-19 epidemic (December 31, 2019, to February 20, 2020) through the Sina Microblog hot search list.

METHODS

We collected topics related to the COVID-19 epidemic on the Sina Microblog hot search list from December 31, 2019, to February 20, 2020, and described the trend of public attention on COVID-19 epidemic-related topics. ROST Content Mining System version 6.0 was used to analyze the collected text for word segmentation, word frequency, and sentiment analysis. We further described the hot topic keywords and sentiment trends of public attention. We used VOSviewer to implement a visual cluster analysis of hot keywords and build a social network of public opinion content.

RESULTS

The study has four main findings. First, we analyzed the changing trend of the public's attention to the COVID-19 epidemic, which can be divided into three stages. Second, the hot topic keywords of public attention at each stage were slightly different. Third, the emotional tendency of the public toward the COVID-19 epidemic-related hot topics changed from negative to neutral, with negative emotions weakening and positive emotions increasing as a whole. Fourth, we divided the COVID-19 topics with the most public concern into five categories: the situation of the new cases of COVID-19 and its impact, frontline reporting of the epidemic and the measures of prevention and control, expert interpretation and discussion on the source of infection, medical services on the frontline of the epidemic, and focus on the worldwide epidemic and the search for suspected cases.

CONCLUSIONS

Our study found that social media (eg, Sina Microblog) can be used to measure public attention toward public health emergencies. During the epidemic of the novel coronavirus, a large amount of information about the COVID-19 epidemic was disseminated on Sina Microblog and received widespread public attention. We have learned about the hotspots of public concern regarding the COVID-19 epidemic. These findings can help the government and health departments better communicate with the public on health and translate public health needs into practice to create targeted measures to prevent and control the spread of COVID-19.

Ebola and Localized Blame on Social Media: Analysis of Twitter and Facebook Conversations During the 2014-2015 Ebola Epidemic

This study aimed to analyze main groups accused on social media of causing or spreading the 2014-2016 Ebola epidemic in West Africa. In this analysis, blame is construed as a vehicle of meaning through which the lay public makes sense of an epidemic, and through which certain classes of people become "figures of blame". Data was collected from Twitter and Facebook using key word extraction, then categorized thematically. Our findings indicate an overall proximate blame tendency: blame was typically cast on "near-by" figures, namely national governments, and less so on "distant" figures, such as generalized figures of otherness ("Africans", global health authorities, global elites). Our results also suggest an evolution of online blame. In the early stage of the epidemic, blame directed at the affected populations was more prominent. However, during the peak of the outbreak, the increasingly perceived threat of inter-continental spread was accompanied by a progressively proximal blame tendency, directed at figures with whom the social media users had pre-existing biopolitical frustrations. Our study proposes that pro-active and on-going analysis of blame circulating in social media can usefully help to guide communications strategies, making them more responsive to public perceptions.

Identifying Key Topics Bearing Negative Sentiment on Twitter: Insights Concerning the 2015-2016 Zika Epidemic

Background

To understand the public sentiment regarding the Zika virus, social media can be leveraged to understand how positive, negative, and neutral sentiments are expressed in society. Specifically, understanding the characteristics of negative sentiment could help inform federal disease control agencies’ efforts to disseminate relevant information to the public about Zika-related issues.

Objective

The purpose of this study was to analyze the public sentiment concerning Zika using posts on Twitter and determine the qualitative characteristics of positive, negative, and neutral sentiments expressed.

Methods

Machine learning techniques and algorithms were used to analyze the sentiment of tweets concerning Zika. A supervised machine learning classifier was built to classify tweets into 3 sentiment categories: positive, neutral, and negative. Tweets in each category were then examined using a topic-modeling approach to determine the main topics for each category, with focus on the negative category.

Results

A total of 5303 tweets were manually annotated and used to train multiple classifiers. These performed moderately well (F1 score=0.48-0.68) with text-based feature extraction. All 48,734 tweets were then categorized into the sentiment categories. Overall, 10 topics for each sentiment category were identified using topic modeling, with a focus on the negative sentiment category.

Conclusions

Our study demonstrates how sentiment expressed within discussions of epidemics on Twitter can be discovered. This allows public health officials to understand public sentiment regarding an epidemic and enables them to address specific elements of negative sentiment in real time. Our negative sentiment classifier was able to identify tweets concerning Zika with 3 broad themes: neural defects,Zika abnormalities, and reports and findings. These broad themes were based on domain expertise and from topics discussed in journals such as Morbidity and Mortality Weekly Report and Vaccine. As the majority of topics in the negative sentiment category concerned symptoms, officials should focus on spreading information about prevention and treatment research.

Precision Global Health - The case of Ebola: a scoping review

BACKGROUND

The 2014-2016 Ebola outbreak across West Africa was devastating, acting not only as a wake-up call for the global health community, but also as a catalyst for innovative change and global action. Improved infectious disease monitoring is the stepping-stone toward better disease prevention and control efforts, and recent research has revealed the potential of digital technologies to transform the field of global health. This scoping review aimed to identify which digital technologies may improve disease prevention and control, with regard to the 2014-2016 Ebola outbreak in West Africa.

METHODS

A search was conducted on PubMed, EBSCOhost and Web of Science, with search dates ranging from 2013 (01/01/2013) - 2017 (13/06/2017). Data was extracted into a summative table and data synthesized through grouping digital technology domains, using narrative and graphical methods.

FINDINGS

The scoping review identified 82 full-text articles, and revealed big data (48%, n = 39) and modeling (26%, n = 21) technologies to be the most utilized within the Ebola outbreak. Digital technologies were mainly used for surveillance purposes (90%, n = 74), and key challenges were related to scalability and misinformation from social media platforms.

INTERPRETATION

Digital technologies demonstrated their potential during the Ebola outbreak through: more rapid diagnostics, more precise predictions and estimations, increased knowledge transfer, and raising situational awareness through mHealth and social media platforms such as Twitter and Weibo. However, better integration into both citizen and health professionals' communities is necessary to maximise the potential of digital technologies.

Social media and outbreaks of emerging infectious diseases: A systematic review of literature

•

A systematic review of published articles on social media and emerging infectious disease.

•

Three major approaches are identified: assessment of public opinion, organization's social media use, and evaluation of information accuracy.

•

More theorization and methodologic rigor is needed.

Background

The public often turn to social media for information during emerging infectious diseases (EIDs) outbreaks. This study identified the major approaches and assessed the rigors in published research articles on EIDs and social media.

Methods

We searched 5 databases for published journal articles on EIDs and social media. We then evaluated these articles in terms of EIDs studied, social media examined, theoretical frameworks, methodologic approaches, and research findings.

Results

Thirty articles were included in the analysis (published between January 1, 2010, and March 1, 2016). EIDs that received most scholarly attention were H1N1 (or swine flu, n = 15), Ebola virus (n = 10), and H7N9 (or avian flu/bird flu, n = 2). Twitter was the most often studied social media (n = 17), followed by YouTube (n = 6), Facebook (n = 6), and blogs (n = 6). Three major approaches in this area of inquiry are identified: (1) assessment of the public's interest in and responses to EIDs, (2) examination of organizations' use of social media in communicating EIDs, and (3) evaluation of the accuracy of EID-related medical information on social media.

Conclusions

Although academic studies of EID communication on social media are on the rise, they still suffer from a lack of theorization and a need for more methodologic rigor.

Virtual Zika transmission after the first U.S. case: who said what and how it spread on Twitter

BACKGROUND

This paper goes beyond detecting specific themes within Zika-related chatter on Twitter, to identify the key actors who influence the diffusive process through which some themes become more amplified than others.

METHODS

We collected all Zika-related tweets during the 3 months immediately after the first U.S. case of Zika. After the tweets were categorized into 12 themes, a cross-section were grouped into weekly datasets, to capture 12 amplifier/user groups, and analyzed by 4 amplification modes: mentions, retweets, talkers, and Twitter-wide amplifiers.

RESULTS

We analyzed 3,057,130 tweets in the United States and categorized 4997 users. The most talked about theme was Zika transmission (~58%). News media, public health institutions, and grassroots users were the most visible and frequent sources and disseminators of Zika-related Twitter content. Grassroots users were the primary sources and disseminators of conspiracy theories.

CONCLUSIONS

Social media analytics enable public health institutions to quickly learn what information is being disseminated, and by whom, regarding infectious diseases. Such information can help public health institutions identify and engage with news media and other active information providers. It also provides insights into media and public concerns, accuracy of information on Twitter, and information gaps. The study identifies implications for pandemic preparedness and response in the digital era and presents the agenda for future research and practice.

Harnessing Big Data for Communicable Tropical and Sub-Tropical Disorders: Implications From a Systematic Review of the Literature

Aim

According to the World Health Organization (WHO), communicable tropical and sub-tropical diseases occur solely, or mainly in the tropics, thriving in hot, and humid conditions. Some of these disorders termed as neglected tropical diseases are particularly overlooked. Communicable tropical/sub-tropical diseases represent a diverse group of communicable disorders occurring in 149 countries, favored by tropical and sub-tropical conditions, affecting more than one billion people and imposing a dramatic societal and economic burden.

Methods

A systematic review of the extant scholarly literature was carried out, searching in PubMed/MEDLINE and Scopus. The search string used included proper keywords, like big data, nontraditional data sources, social media, social networks, infodemiology, infoveillance, novel data streams (NDS), digital epidemiology, digital behavior, Google Trends, Twitter, Facebook, YouTube, Instagram, Pinterest, Ebola, Zika, dengue, Chikungunya, Chagas, and the other neglected tropical diseases.

Results

47 original, observational studies were included in the current systematic review: 1 focused on Chikungunya, 6 on dengue, 19 on Ebola, 2 on Malaria, 1 on Mayaro virus, 2 on West Nile virus, and 16 on Zika. Fifteen were dedicated on developing and validating forecasting techniques for real-time monitoring of neglected tropical diseases, while the remaining studies investigated public reaction to infectious outbreaks. Most studies explored a single nontraditional data source, with Twitter being the most exploited tool (25 studies).

Conclusion

Even though some studies have shown the feasibility of utilizing NDS as an effective tool for predicting epidemic outbreaks and disseminating accurate, high-quality information concerning neglected tropical diseases, some gaps should be properly underlined. Out of the 47 articles included, only 7 were focusing on neglected tropical diseases, while all the other covered communicable tropical/sub-tropical diseases, and the main determinant of this unbalanced coverage seems to be the media impact and resonance. Furthermore, efforts in integrating diverse NDS should be made. As such, taking into account these limitations, further research in the field is needed.

Communicating infectious disease prevalence through graphics: Results from an international survey

•

The impact of graphics to inform the general public on health knowledge is unknown.

•

Heat maps were evaluated as superior to dot maps or picto-trendlines.

•

Heat maps are a viable option to widely disseminate information about infectious diseases.

Background

Graphics are increasingly used to represent the spread of infectious diseases (e.g., influenza, Zika, Ebola); however, the impact of using graphics to adequately inform the general population is unknown.

Objective

To examine whether three ways of visually presenting data (heat map, dot map, or picto-trendline)—all depicting the same information regarding the spread of a hypothetical outbreak of influenza—influence intent to vaccinate, risk perception, and knowledge.

Design

Survey with participants randomized to receive a simulated news article accompanied by one of the three graphics that communicated prevalence of influenza and number of influenza-related deaths.

Setting

International online survey.

Participants

16,510 adults living in 11 countries selected using stratified random sampling based on age and gender.

Measurements

After reading the article and viewing the presented graphic, participants completed a survey that measured interest in vaccination, perceived risk of contracting disease, knowledge gained, interest in additional information about the disease, and perception of the graphic.

Results

Heat maps and picto-trendlines were evaluated more positively than dot maps. Heat maps were more effective than picto-trendlines and no different from dot maps at increasing interest in vaccination, perceived risk of contracting disease, and interest in additional information about the disease. Heat maps and picto-trendlines were more successful at conveying knowledge than dot maps. Overall, heat maps were the only graphic to be superior in every outcome.

Limitations

Results are based on a hypothetical scenario.

Conclusion

Heat maps are a viable option to promote interest in and concern about infectious diseases.

Ebola virus disease and social media: A systematic review

OBJECTIVES

We systematically reviewed existing research pertinent to Ebola virus disease and social media, especially to identify the research questions and the methods used to collect and analyze social media.

METHODS

We searched 6 databases for research articles pertinent to Ebola virus disease and social media. We extracted the data using a standardized form. We evaluated the quality of the included articles.

RESULTS

Twelve articles were included in the main analysis: 7 from Twitter with 1 also including Weibo, 1 from Facebook, 3 from YouTube, and 1 from Instagram and Flickr. All the studies were cross-sectional. Eleven of the 12 articles studied ≥ 1of these 3 elements of social media and their relationships: themes or topics of social media contents, meta-data of social media posts (such as frequency of original posts and reposts, and impressions) and characteristics of the social media accounts that made these posts (such as whether they are individuals or institutions). One article studied how news videos influenced Twitter traffic. Twitter content analysis methods included text mining (n = 3) and manual coding (n = 1). Two studies involved mathematical modeling. All 3 YouTube studies and the Instagram/Flickr study used manual coding of videos and images, respectively.

CONCLUSIONS

Published Ebola virus disease-related social media research focused on Twitter and YouTube. The utility of social media research to public health practitioners is warranted.

Epidemic spreading on adaptively weighted scale-free networks

We introduce three modified SIS models on scale-free networks that take into account variable population size, nonlinear infectivity, adaptive weights, behavior inertia and time delay, so as to better characterize the actual spread of epidemics. We develop new mathematical methods and techniques to study the dynamics of the models, including the basic reproduction number, and the global asymptotic stability of the disease-free and endemic equilibria. We show the disease-free equilibrium cannot undergo a Hopf bifurcation. We further analyze the effects of local information of diseases and various immunization schemes on epidemic dynamics. We also perform some stochastic network simulations which yield quantitative agreement with the deterministic mean-field approach.

Lessons from the Ebola Outbreak: Action Items for Emerging Infectious Disease Preparedness and Response

As the Ebola outbreak in West Africa wanes, it is time for the international scientific community to reflect on how to improve the detection of and coordinated response to future epidemics. Our interdisciplinary team identified key lessons learned from the Ebola outbreak that can be clustered into three areas: environmental conditions related to early warning systems, host characteristics related to public health, and agent issues that can be addressed through the laboratory sciences. In particular, we need to increase zoonotic surveillance activities, implement more effective ecological health interventions, expand prediction modeling, support medical and public health systems in order to improve local and international responses to epidemics, improve risk communication, better understand the role of social media in outbreak awareness and response, produce better diagnostic tools, create better therapeutic medications, and design better vaccines. This list highlights research priorities and policy actions the global community can take now to be better prepared for future emerging infectious disease outbreaks that threaten global public health and security.

Assessing Ebola-related web search behaviour: insights and implications from an analytical study of Google Trends-based query volumes

Background

The 2014 Ebola epidemic in West Africa has attracted public interest worldwide, leading to millions of Ebola-related Internet searches being performed during the period of the epidemic. This study aimed to evaluate and interpret Google search queries for terms related to the Ebola outbreak both at the global level and in all countries where primary cases of Ebola occurred. The study also endeavoured to look at the correlation between the number of overall and weekly web searches and the number of overall and weekly new cases of Ebola.

Methods

Google Trends (GT) was used to explore Internet activity related to Ebola. The study period was from 29 December 2013 to 14 June 2015. Pearson’s correlation was performed to correlate Ebola-related relative search volumes (RSVs) with the number of weekly and overall Ebola cases. Multivariate regression was performed using Ebola-related RSV as a dependent variable, and the overall number of Ebola cases and the Human Development Index were used as predictor variables.

Results

The greatest RSV was registered in the three West African countries mainly affected by the Ebola epidemic. The queries varied in the different countries. Both quantitative and qualitative differences between the affected African countries and other Western countries with primary cases were noted, in relation to the different flux volumes and different time courses. In the affected African countries, web query search volumes were mostly concentrated in the capital areas. However, in Western countries, web queries were uniformly distributed over the national territory. In terms of the three countries mainly affected by the Ebola epidemic, the correlation between the number of new weekly cases of Ebola and the weekly GT index varied from weak to moderate. The correlation between the number of Ebola cases registered in all countries during the study period and the GT index was very high.

Conclusion

Google Trends showed a coarse-grained nature, strongly correlating with global epidemiological data, but was weaker at country level, as it was prone to distortions induced by unbalanced media coverage and the digital divide. Global and local health agencies could usefully exploit GT data to identify disease-related information needs and plan proper communication strategies, particularly in the case of health-threatening events.

Electronic supplementary material

The online version of this article (doi:10.1186/s40249-015-0090-9) contains supplementary material, which is available to authorized users.

Modeling the effect of comprehensive interventions on Ebola virus transmission

Since the re-emergence of Ebola in West Africa in 2014, comprehensive and stringent interventions have been implemented to decelerate the spread of the disease. The effectiveness of interventions still remains unclear. In this paper, we develop an epidemiological model that includes various controlling measures to systematically evaluate their effects on the disease transmission dynamics. By fitting the model to reported cumulative cases and deaths in Guinea, Sierra Leone and Liberia until March 22, 2015, we estimate the basic reproduction number in these countries as 1.2552, 1.6093 and 1.7994, respectively. Model analysis shows that there exists a threshold of the effectiveness of isolation, below which increasing the fraction of latent individuals diagnosed prior to symptoms onset or shortening the duration between symptoms onset and isolation may lead to more Ebola infection. This challenges an existing view. Media coverage plays a substantial role in reducing the final epidemic size. The response to reported cumulative infected cases and deaths may have a different effect on the epidemic spread in different countries. Among all the interventions, we find that shortening the duration between death and burial and improving the effectiveness of isolation are two effective interventions for controlling the outbreak of Ebola virus infection.

Australian Hajj pilgrims' knowledge, attitude and perception about Ebola, November 2014 to February 2015

Upon return from Hajj 2014, 150 Australian pilgrims were interviewed about their understanding of the Ebola epidemic. Most (89%, 134/150) knew of the epidemic before travelling and 60% (80/134) of those knew Ebola transmits through body fluids. Pilgrims who received pre-travel health advice were more conscious of Ebola (69% vs 31%, p = 0.01) and adhered better to hand hygiene after touching an ill person (68% vs 31%, p < 0.01). Mass media was the main information source (78%).

Publications in PubMed on Ebola and the 2014 outbreak

In this research note we examine the biomedical publication output about Ebola in 2014. We show that the volume of publications has dramatically increased in the past year. In 2014 there have been over 888 publications with 'ebola' or 'ebolavirus' in the title, approximately 13 times the volume of publication of 2013. The rise reflects an impressive growth starting in the month of August, concomitant with or following the surge in infections, deaths and coverage in news and social media. Though non-research articles have been the major contributors to this growth, there has been a substantial increase in original research articles too, including many papers of basic science. The United States has been the country with the highest number of research articles, followed by Canada and the United Kingdom. We present a comprehensive set of charts and facts that, by describing the volumes and nature of publications in 2014, show how the scientific community has responded to the Ebola outbreak and how it might respond to future similar global threats and media events. This information will assist scholars and policymakers in their efforts to improve scientific research policies with the goal of maximizing both public health and knowledge advancement.