Social media in Ebola outbreak

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.

Spatio-temporal evaluation of social media as a tool for livestock disease surveillance

Recent outbreaks of Avian Influenza across Europe have highlighted the potential for syndromic surveillance systems that consider other modes of data, namely social media. This study investigates the feasibility of using social media, primarily Twitter, to monitor illness outbreaks such as avian flu. Using temporal, geographical, and correlation analyses, we investigated the association between avian influenza tweets and officially verified cases in the United Kingdom in 2021 and 2022. Pearson correlation coefficient, bivariate Moran's I analysis and time series analysis, were among the methodologies used. The findings show a weak, statistically insignificant relationship between the number of tweets and confirmed cases in a temporal context, implying that relying simply on social media data for surveillance may be insufficient. The spatial analysis provided insights into the overlaps between confirmed cases and tweet locations, shedding light on regionally targeted interventions during outbreaks. Although social media can be useful for understanding public sentiment and concerns during outbreaks, it must be combined with traditional surveillance methods and official data sources for a more accurate and comprehensive approach. Improved data mining techniques and real-time analysis can improve outbreak detection and response even further. This study underscores the need of having a strong surveillance system in place to properly monitor and manage disease outbreaks and protect public health.

People's perceptions on COVID-19 vaccination: an analysis of twitter discourse from four countries

More than six and half million people have died as a result of the COVID-19 pandemic till Dec 2022. Vaccination is the most effective means to prevent mortality and infection attributed to COVID-19. Identifying public attitudes and perceptions on COVID-19 vaccination is essential to strengthening the vaccination programmes. This study aims to identify attitudes and perceptions of twitter users towards COVID-19 vaccinations in four different countries. A sentiment analysis of 663,377 tweets from October 2020 to September 2022 from four different countries (i.e., India, South Africa, UK, and Australia) was conducted. Text mining using roBERTA (Robustly Optimized Bert Pretraining approach) python library was used to identify the polarity of people's attitude as "negative", "positive" or "neutral" based on tweets. A sample of 2000 tweets (500 from each country) were thematically analysed to explore the people's perception concerning COVID-19 vaccines across the countries. The attitudes towards COVID-19 vaccines varied by countries. Negative attitudes were observed to be highest in India (58.48%), followed by United Kingdom (33.22%), Australia (31.42%) and South Africa (28.88%). Positive attitudes towards vaccines were highest in the United Kingdom (21.09%). The qualitative analysis yielded eight themes namely (i) vaccine shortages, (ii) vaccine side-effects, (iii) distrust on COVID-19 vaccines, (iv) voices for vaccine equity, (v) awareness about vaccines, (vi) myth busters, (vii) vaccines work and (viii) vaccines are safe. The twitter discourse reflected the evolving situation of COVID-19 pandemic and vaccination strategies, lacunae and positives in the respective countries studied.

Public Awareness and Sentiment Analysis of COVID-Related Discussions Using BERT-Based Infoveillance

Understanding different aspects of public concerns and sentiments during large health emergencies, such as the COVID-19 pandemic, is essential for public health agencies to develop effective communication strategies, deliver up-to-date and accurate health information, and mitigate potential impacts of emerging misinformation. Current infoveillance systems generally focus on discussion intensity (i.e., number of relevant posts) as an approximation of public awareness, while largely ignoring the rich and diverse information in texts with granular information of varying public concerns and sentiments. In this study, we address this grand challenge by developing a novel natural language processing (NLP) infoveillance workflow based on bidirectional encoder representation from transformers (BERT). We first used a smaller COVID-19 tweet sample to develop a content classification and sentiment analysis model using COVID-Twitter-BERT. The classification accuracy was between 0.77 and 0.88 across the five identified topics. In the sentiment analysis with a three-class classification task (positive/negative/neutral), BERT achieved decent accuracy, 0.7. We then applied the content topic and sentiment classifiers to a much larger dataset with more than 4 million tweets in a 15-month period. We specifically analyzed non-pharmaceutical intervention (NPI) and social issue content topics. There were significant differences in terms of public awareness and sentiment towards the overall COVID-19, NPI, and social issue content topics across time and space. In addition, key events were also identified to associate with abrupt sentiment changes towards NPIs and social issues. This novel NLP-based AI workflow can be readily adopted for real-time granular content topic and sentiment infoveillance beyond the health context.

Mining Social Media Data to Predict COVID-19 Case Counts

The unpredictability and unknowns surrounding the ongoing coronavirus disease (COVID-19) pandemic have led to an unprecedented consequence taking a heavy toll on the lives and economies of all countries. There have been efforts to predict COVID-19 case counts (CCC) using epidemiological data and numerical tokens online, which may allow early preventive measures to slow the spread of the disease. In this paper, we use state-of-the-art natural language processing (NLP) algorithms to numerically encode COVID-19 related tweets originated from eight cities in the United States and predict city-specific CCC up to eight days in the future. A city-embedding is proposed to obtain a time series representation of daily tweets posted from a city, which is then used to predict case counts using a custom long-short term memory (LSTM) model. The universal sentence encoder yields the best normalized root mean squared error (NRMSE) 0.090 (0.039), averaged across all cities in predicting CCC six days in the future. The R ² scores in predicting CCC are more than 0.70 and often over 0.8, which suggests a strong correlation between the actual and our model predicted CCC values. Our analyses show that the NRMSE and R ² scores are consistently robust across different cities and different numbers of time steps in time series data. Results show that the LSTM model can learn the mapping between the NLP-encoded tweet semantics and the case counts, which infers that social media text can be directly mined to identify the future course of the pandemic.

Detecting Fine-Grained Emotions on Social Media during Major Disease Outbreaks: Health and Well-being before and during the COVID-19 Pandemic

The COVID-19 pandemic has affected the whole world in various ways. One type of impact is that communication, work, interaction, a great part of our lives has moved online on various platforms, with some of the most popular being the social media ones. Another, arguably less visible impact, is the emotional impact. Detecting and understanding emotions is important, to better discern the emotional health and well-being of the global population. Thus, in this work, we use a social media platform (Twitter) to analyse emotions in detail. Our contribution is twofold: (1) we propose EmoBERT, a new emotion-based variant of the BERT transformer model, able to learn emotion representations and outperform the state-of-the-art; (2) we provide a fine-grained analysis of the pandemic's effect in a major location, London, comparing specific emotions (annoyed, anxious, empathetic, sad) before and during the epidemic.

Crowdsourcing for Machine Learning in Public Health Surveillance: Lessons Learned From Amazon Mechanical Turk

Background

Crowdsourcing services, such as Amazon Mechanical Turk (AMT), allow researchers to use the collective intelligence of a wide range of web users for labor-intensive tasks. As the manual verification of the quality of the collected results is difficult because of the large volume of data and the quick turnaround time of the process, many questions remain to be explored regarding the reliability of these resources for developing digital public health systems.

Objective

This study aims to explore and evaluate the application of crowdsourcing, generally, and AMT, specifically, for developing digital public health surveillance systems.

Methods

We collected 296,166 crowd-generated labels for 98,722 tweets, labeled by 610 AMT workers, to develop machine learning (ML) models for detecting behaviors related to physical activity, sedentary behavior, and sleep quality among Twitter users. To infer the ground truth labels and explore the quality of these labels, we studied 4 statistical consensus methods that are agnostic of task features and only focus on worker labeling behavior. Moreover, to model the meta-information associated with each labeling task and leverage the potential of context-sensitive data in the truth inference process, we developed 7 ML models, including traditional classifiers (offline and active), a deep learning–based classification model, and a hybrid convolutional neural network model.

Results

Although most crowdsourcing-based studies in public health have often equated majority vote with quality, the results of our study using a truth set of 9000 manually labeled tweets showed that consensus-based inference models mask underlying uncertainty in data and overlook the importance of task meta-information. Our evaluations across 3 physical activity, sedentary behavior, and sleep quality data sets showed that truth inference is a context-sensitive process, and none of the methods studied in this paper were consistently superior to others in predicting the truth label. We also found that the performance of the ML models trained on crowd-labeled data was sensitive to the quality of these labels, and poor-quality labels led to incorrect assessment of these models. Finally, we have provided a set of practical recommendations to improve the quality and reliability of crowdsourced data.

Conclusions

Our findings indicate the importance of the quality of crowd-generated labels in developing ML models designed for decision-making purposes, such as public health surveillance decisions. A combination of inference models outlined and analyzed in this study could be used to quantitatively measure and improve the quality of crowd-generated labels for training ML models.

Using a Machine Learning Approach to Monitor COVID-19 Vaccine Adverse Events (VAE) from Twitter Data

Social media can be used to monitor the adverse effects of vaccines. The goal of this project is to develop a machine learning and natural language processing approach to identify COVID-19 vaccine adverse events (VAE) from Twitter data. Based on COVID-19 vaccine-related tweets (1 December 2020-1 August 2021), we built a machine learning-based pipeline to identify tweets containing personal experiences with COVID-19 vaccinations and to extract and normalize VAE-related entities, including dose(s); vaccine types (Pfizer, Moderna, and Johnson & Johnson); and symptom(s) from tweets. We further analyzed the extracted VAE data based on the location, time, and frequency. We found that the four most populous states (California, Texas, Florida, and New York) in the US witnessed the most VAE discussions on Twitter. The frequency of Twitter discussions of VAE coincided with the progress of the COVID-19 vaccinations. Sore to touch, fatigue, and headache are the three most common adverse effects of all three COVID-19 vaccines in the US. Our findings demonstrate the feasibility of using social media data to monitor VAEs. To the best of our knowledge, this is the first study to identify COVID-19 vaccine adverse event signals from social media. It can be an excellent supplement to the existing vaccine pharmacovigilance systems.

Perceptions of blame on social media during the coronavirus pandemic

The outbreak of the coronavirus (COVID-19) disease is overwhelming resources, economies and countries around the world. Millions of people have been infected and hundreds of thousands have succumbed to the virus. Research regarding the coronavirus pandemic is published every day. However, there is limited discourse regarding societal perception. Thus, this paper examines blame attribution concerning the origin and propagation of the coronavirus crisis according to public perception. Specifically, data were extracted from the social media platform Twitter concerning the coronavirus during the early stages of the outbreak and further investigated using thematic analysis. The findings revealed the public predominantly blames national governments for the coronavirus pandemic. In addition, the results documented the explosion of conspiracy theories among social media users regarding the virus' origin. In the early stages of the pandemic, the blame tendency was most frequent to conspiracy theories and restriction of information from the government, whilst in the later months, responsibility had shifted to political leaders and the media. The findings indicate an emerging government mistrust that may result in disregard of preventive health behaviours and the amplification of conspiracy theories, and an evolving dynamic of blame. This study argues for a transparent, continuing dialogue between governments and the public to stop the spread of the coronavirus.

Data and Digital Solutions to Support Surveillance Strategies in the Context of the COVID-19 Pandemic

Background: In order to prevent spread and improve control of infectious diseases, public health experts need to closely monitor human and animal populations. Infectious disease surveillance is an established, routine data collection process essential for early warning, rapid response, and disease control. The quantity of data potentially useful for early warning and surveillance has increased exponentially due to social media and other big data streams. Digital epidemiology is a novel discipline that includes harvesting, analysing, and interpreting data that were not initially collected for healthcare needs to enhance traditional surveillance. During the current COVID-19 pandemic, the importance of digital epidemiology complementing traditional public health approaches has been highlighted. Objective: The aim of this paper is to provide a comprehensive overview for the application of data and digital solutions to support surveillance strategies and draw implications for surveillance in the context of the COVID-19 pandemic and beyond. Methods: A search was conducted in PubMed databases. Articles published between January 2005 and May 2020 on the use of digital solutions to support surveillance strategies in pandemic settings and health emergencies were evaluated. Results: In this paper, we provide a comprehensive overview of digital epidemiology, available data sources, and components of 21st-century digital surveillance, early warning and response, outbreak management and control, and digital interventions. Conclusions: Our main purpose was to highlight the plausible use of new surveillance strategies, with implications for the COVID-19 pandemic strategies and then to identify opportunities and challenges for the successful development and implementation of digital solutions during non-emergency times of routine surveillance, with readiness for early-warning and response for future pandemics. The enhancement of traditional surveillance systems with novel digital surveillance methods opens a direction for the most effective framework for preparedness and response to future pandemics.

Suitability of Google Trends™ for Digital Surveillance During Ongoing COVID-19 Epidemic: A Case Study from India

OBJECTIVE

Digital surveillance has shown mixed results as a supplement to traditional surveillance. Google Trends™ (GT) (Google, Mountain View, CA, United States) has been used for digital surveillance of H1N1, Ebola and MERS. We used GT to correlate the information seeking on COVID-19 with number of tests and cases in India.

METHODS

Data was obtained on daily tests and cases from WHO, ECDC and covid19india.org. We used a comprehensive search strategy to retrieve GT data on COVID-19 related information-seeking behavior in India between January 1 and May 31, 2020 in the form of relative search volume (RSV). We also used time-lag correlation analysis to assess the temporal relationships between RSV and daily new COVID-19 cases and tests.

RESULTS

GT RSV showed high time-lag correlation with both daily reported tests and cases for the terms "COVID 19," "COVID," "social distancing," "soap," and "lockdown" at the national level. In 5 high-burden states, high correlation was observed for these 5 terms along with "Corona." Peaks in RSV, both at the national level and in high-burden states corresponded with media coverage or government declarations on the ongoing pandemic.

CONCLUSION

The correlation observed between GT data and COVID-19 tests/cases in India may be either due to media-coverage-induced curiosity, or health-seeking curiosity.

Global Public Interests and Dynamic Trends in Osteoporosis From 2004 to 2019: Infodemiology Study

Background

With the prolonging of human life expectancy and subsequent population aging, osteoporosis (OP) has become an important public health issue.

Objective

This study aimed to understand the global public search interests and dynamic trends in “osteoporosis” using the data derived from Google Trends.

Methods

An online search was performed using the term “osteoporosis” in Google Trends from January 1, 2004, to December 31, 2019, under the category “Health.” Cosinor analysis was used to test the seasonality of relative search volume (RSV) for “osteoporosis.” An analysis was conducted to investigate the public search topic rising in RSV for “osteoporosis.”

Results

There was a descending trend of global RSV for “osteoporosis” from January 2004 to December 2014, and a slowly increasing trend from January 2015 to December 2019. Cosinor analysis showed significant seasonal variations in global RSV for “osteoporosis” (P=.01), with a peak in March and a trough in September. In addition, similar decreasing trends of RSV for “osteoporosis” were found in Australia, New Zealand, Ireland, and Canada from January 2004 to December 2019. Cosinor test revealed significant seasonal variations in RSV for “osteoporosis” in Australia, New Zealand, Canada, Ireland, UK, and USA (all P<.001). Furthermore, public search rising topics related to “osteoporosis” included denosumab, fracture risk assessment tool, bone density, osteopenia, osteoarthritis, and risk factor.

Conclusions

Our study provided evidence about the public search interest and dynamic trends in OP using web-based data, which would be helpful for public health and policy making.

Digital Data Sources and Their Impact on People's Health: A Systematic Review of Systematic Reviews

Background: Digital data sources have become ubiquitous in modern culture in the era of digital technology but often tend to be under-researched because of restricted access to data sources due to fragmentation, privacy issues, or industry ownership, and the methodological complexity of demonstrating their measurable impact on human health. Even though new big data sources have shown unprecedented potential for disease diagnosis and outbreak detection, we need to investigate results in the existing literature to gain a comprehensive understanding of their impact on and benefits to human health. Objective: A systematic review of systematic reviews on identifying digital data sources and their impact area on people's health, including challenges, opportunities, and good practices. Methods: A multidatabase search was performed. Peer-reviewed papers published between January 2010 and November 2020 relevant to digital data sources on health were extracted, assessed, and reviewed. Results: The 64 reviews are covered by three domains, that is, universal health coverage (UHC), public health emergencies, and healthier populations, defined in WHO's General Programme of Work, 2019-2023, and the European Programme of Work, 2020-2025. In all three categories, social media platforms are the most popular digital data source, accounting for 47% (N = 8), 84% (N = 11), and 76% (N = 26) of studies, respectively. The second most utilized data source are electronic health records (EHRs) (N = 13), followed by websites (N = 7) and mass media (N = 5). In all three categories, the most studied impact of digital data sources is on prevention, management, and intervention of diseases (N = 40), and as a tool, there are also many studies (N = 10) on early warning systems for infectious diseases. However, they could also pose health hazards (N = 13), for instance, by exacerbating mental health issues and promoting smoking and drinking behavior among young people. Conclusions: The digital data sources presented are essential for collecting and mining information about human health. The key impact of social media, electronic health records, and websites is in the area of infectious diseases and early warning systems, and in the area of personal health, that is, on mental health and smoking and drinking prevention. However, further research is required to address privacy, trust, transparency, and interoperability to leverage the potential of data held in multiple datastores and systems. This study also identified the apparent gap in systematic reviews investigating the novel big data streams, Internet of Things (IoT) data streams, and sensor, mobile, and GPS data researched using artificial intelligence, complex network, and other computer science methods, as in this domain systematic reviews are not common.

Health information diffusion on Twitter: The content and design of WHO tweets matter

BACKGROUND

Micro-blogging services empower health institutions to quickly disseminate health information to many users. By analysing user data, infodemiology (i.e. improving public health using user contributed health related content) can be measured in terms of information diffusion.

OBJECTIVES

Tweets by the WHO were examined in order to identify tweet attributes that lead to a high information diffusion rate using Twitter data collected between November 2019 and January 2020.

METHODS

One thousand hundred and seventy-seven tweets were collected using Python's Tweepy library. Afterwards, k-means clustering and manual coding were used to classify tweets by theme, sentiment, length and count of emojis, pictures, videos and links. Resulting groups with different characteristics were analysed for significant differences using Mann-Whitney U- and Kruskal-Wallis H-tests.

RESULTS

The topic of the tweet, the included links, emojis and (one) picture as well as the tweet length significantly affected the tweets' diffusion, whereas sentiment and videos did not show any significant influence on the diffusion of tweets.

DISCUSSION

The findings of this study give insights on why specific health topics might generate less attention and do not showcase sufficient information diffusion.

CONCLUSION

The subject and appearance of a tweet influence its diffusion, making the design equally essential to the preparation of its content.

The impact of social networking services on the coronavirus disease 2019 (COVID-19) pandemic in sub-Saharan Africa

Social networking services played a crucial role in the management of previous outbreaks around the world. African populations are increasingly using social networks and this may have benefits but also harmful consequences, especially at this time of coronavirus disease 2019 pandemic. This paper concisely discusses of these consequences which include the propagation of “fake news” and the misinterpretation of messages pertaining to the prevention and the treatment of the disease. Moreover, our commentary provides some ways to alleviate them, chiefly represented by a framed and practical communication by health authorities. We suggest for instance the systematic sharing of correct messages through official Facebook and Twitter accounts and the conception of tailored web tools dedicated to the verification of circulating information.

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.

Tracking COVID-19 in Europe: Infodemiology Approach

BACKGROUND

Infodemiology (ie, information epidemiology) uses web-based data to inform public health and policy. Infodemiology metrics have been widely and successfully used to assess and forecast epidemics and outbreaks.

OBJECTIVE

In light of the recent coronavirus disease (COVID-19) pandemic that started in Wuhan, China in 2019, online search traffic data from Google are used to track the spread of the new coronavirus disease in Europe.

METHODS

Time series from Google Trends from January to March 2020 on the Topic (Virus) of "Coronavirus" were retrieved and correlated with official data on COVID-19 cases and deaths worldwide and in the European countries that have been affected the most: Italy (at national and regional level), Spain, France, Germany, and the United Kingdom.

RESULTS

Statistically significant correlations are observed between online interest and COVID-19 cases and deaths. Furthermore, a critical point, after which the Pearson correlation coefficient starts declining (even if it is still statistically significant) was identified, indicating that this method is most efficient in regions or countries that have not yet peaked in COVID-19 cases.

CONCLUSIONS

In the past, infodemiology metrics in general and data from Google Trends in particular have been shown to be useful in tracking and forecasting outbreaks, epidemics, and pandemics as, for example, in the cases of the Middle East respiratory syndrome, Ebola, measles, and Zika. With the COVID-19 pandemic still in the beginning stages, it is essential to explore and combine new methods of disease surveillance to assist with the preparedness of health care systems at the regional level.

Reducing medical error during a pandemic

On 30 January 2020, the WHO declared the coronavirus disease 2019 (COVID-19) a public health emergency of international concern. By 11 March 2020, it was designated a pandemic owing to its rapid worldwide spread. In this short article we provide some information that might be useful and help equip colleagues to reduce medical error during a pandemic.

We advocate a systems-based approach, rather than an individual’s sole responsibility, and, look at ways to provide safer healthcare.

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.

Can Digitization of Health Care Help Low-Resourced Countries Provide Better Community-Based Rehabilitation Services?

In the wake of globalization, proliferation of digital technologies (DTs) is rapidly changing many activities across sectors, including influencing health to "go digital." Harnessing opportunities of DTs can be a pathway for delivery of health services, such as community-based rehabilitation (CBR) to the vulnerable groups of populations, particularly those in countries with low resources where health systems are weak and experiencing a deficit of trained health workers necessary to effectively deliver a full spectrum of health services. This perspective explored how some DTs can be leveraged in delivery of CBR services in rural and remote areas of countries with low resources. This is described based on information access and exchange, social satisfaction, shortages of rehabilitation workforce, professional development, and capacity building. However, since seizing advantages of DTs can inevitably be associated with spillovers and limitations, including needs prioritization, skills and language limitations, internet addiction and censorship issues, professionalism and ethical dilemmas, and sustainability, if proper measures are not taken, a caution is made. Moreover, as DTs are revolutionizing various activities across sectors, including health, this is not meant as a substitute for traditional health care activities, including those delivered through CBR, but rather to augment their delivery in settings with low resources and elsewhere.

Preparedness Through Urban Resilience

This book explores the topic of resilience at the city level. The focus is more on outbreak events at the city level, or how cities should prepare and react in facing the larger events of epidemic and pandemic.

Qualitative and quantitative evaluation of the use of Twitter as a tool of antimicrobial stewardship

INTRODUCTION

Social media networks have transformed the sources of information, including health information. In particular, the microblogging service Twitter has been used as a learning tool in the field of medicine as well as a tool for disease surveillance and outbreak management. As antimicrobial resistance is one of the biggest concerns of public health, we aimed to review how Twitter is being used as a tool for antimicrobial stewardship (AMS).

METHODS

We used the software Kampal Social® to collect, analyze and monitor tweets from the whole Twitter network to assess the activity that takes place about antibiotics. The study was carried out in three phases: data acquisition, during which we collected data over a six-month period (from 21 September 2016 to 8 February 2017) by monitoring selected users, hashtags and keywords that we knew to be related to AMS; data cleansing, which involved identifying users who were not related to the topic, thus creating a new collection process to remove those users and add newly discovered ones; and, finally, data acquisition and analysis (From 1 April 2017 to 7 March 2018), during which we collected data using the new users obtained in the cleansing phase. We qualitatively characterized the most influential users, we analysed the use of hashtags and the flow of information (the most retweeted users and the global network formed by all the users).

RESULTS

Using the tool Kampal Social®, and after a cleansing phase to remove irrelevant information, we worked with a dataset of 1,765,388 tweets. Studying the qualitative characterization of the top-ten influencers, we found that most of them are institutional users, but individual users, such as physicians, and an important medical journal also appeared. Regarding hashtags, '#antibiotics' was the one with the most occurrences. Hashtags follow a regular distribution over time, with some defined peaks connected to important dates and reports about antibiotics. As for the flow of information, we obtained a rather dense network of interconnections formed by all the users who had sent a message, which means that a strong relation exists between the different organizations, professionals and users in general.

CONCLUSIONS

Institutions, medical journals, physicians and pharmacists are key opinion leaders in the topic of antibiotics, so they must incorporate social media into their communication strategy to spread the AMS message. More evidence is needed regarding the optimal method of communication to spread information throughout the general population. The development of tools capable of collecting and querying large amounts of Twitter data helped us to assess the impact of antibiotic awareness campaigns and to gain an idea of how Twitter is being used to spread the message about AMS.

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.

Google Searches and Detection of Conjunctivitis Epidemics Worldwide

PURPOSE

Epidemic and seasonal infectious conjunctivitis outbreaks can impact education, workforce, and economy adversely. Yet conjunctivitis typically is not a reportable disease, potentially delaying mitigating intervention. Our study objective was to determine if conjunctivitis epidemics could be identified using Google Trends search data.

DESIGN

Search data for conjunctivitis-related and control search terms from 5 years and countries worldwide were obtained. Country and term were masked. Temporal scan statistics were applied to identify candidate epidemics. Candidates then were assessed for geotemporal concordance with an a priori defined collection of known reported conjunctivitis outbreaks, as a measure of sensitivity.

PARTICIPANTS

Populations by country that searched Google's search engine using our study terms.

MAIN OUTCOME MEASURES

Percent of known conjunctivitis outbreaks also found in the same country and period by our candidate epidemics, identified from conjunctivitis-related searches.

RESULTS

We identified 135 candidate conjunctivitis epidemic periods from 77 countries. Compared with our a priori defined collection of known reported outbreaks, candidate conjunctivitis epidemics identified 18 of 26 (69% sensitivity) of the reported country-wide or island nationwide outbreaks, or both; 9 of 20 (45% sensitivity) of the reported region or district-wide outbreaks, or both; but far fewer nosocomial and reported smaller outbreaks. Similar overall and individual sensitivity, as well as specificity, were found on a country-level basis. We also found that 83% of our candidate epidemics had start dates before (of those, 20% were more than 12 weeks before) their concurrent reported outbreak's report issuance date. Permutation tests provided evidence that on average, conjunctivitis candidate epidemics occurred geotemporally closer to outbreak reports than chance alone suggests (P < 0.001) unlike control term candidates (P = 0.40).

CONCLUSIONS

Conjunctivitis outbreaks can be detected using temporal scan analysis of Google search data alone, with more than 80% detected before an outbreak report's issuance date, some as early as the reported outbreak's start date. Future approaches using data from smaller regions, social media, and more search terms may improve sensitivity further and cross-validate detected candidates, allowing identification of candidate conjunctivitis epidemics from Internet search data potentially to complementarily benefit traditional reporting and detection systems to improve epidemic awareness.

Assessing the Methods, Tools, and Statistical Approaches in Google Trends Research: Systematic Review

Background

In the era of information overload, are big data analytics the answer to access and better manage available knowledge? Over the last decade, the use of Web-based data in public health issues, that is, infodemiology, has been proven useful in assessing various aspects of human behavior. Google Trends is the most popular tool to gather such information, and it has been used in several topics up to this point, with health and medicine being the most focused subject. Web-based behavior is monitored and analyzed in order to examine actual human behavior so as to predict, better assess, and even prevent health-related issues that constantly arise in everyday life.

Objective

This systematic review aimed at reporting and further presenting and analyzing the methods, tools, and statistical approaches for Google Trends (infodemiology) studies in health-related topics from 2006 to 2016 to provide an overview of the usefulness of said tool and be a point of reference for future research on the subject.

Methods

Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines for selecting studies, we searched for the term “Google Trends” in the Scopus and PubMed databases from 2006 to 2016, applying specific criteria for types of publications and topics. A total of 109 published papers were extracted, excluding duplicates and those that did not fall inside the topics of health and medicine or the selected article types. We then further categorized the published papers according to their methodological approach, namely, visualization, seasonality, correlations, forecasting, and modeling.

Results

All the examined papers comprised, by definition, time series analysis, and all but two included data visualization. A total of 23.1% (24/104) studies used Google Trends data for examining seasonality, while 39.4% (41/104) and 32.7% (34/104) of the studies used correlations and modeling, respectively. Only 8.7% (9/104) of the studies used Google Trends data for predictions and forecasting in health-related topics; therefore, it is evident that a gap exists in forecasting using Google Trends data.

Conclusions

The monitoring of online queries can provide insight into human behavior, as this field is significantly and continuously growing and will be proven more than valuable in the future for assessing behavioral changes and providing ground for research using data that could not have been accessed otherwise.

Assessment of knowledge, attitudes and perceptions regarding Ebola disease in healthcare workers from a tertiary care hospital in Romania

OBJECTIVES

The National Institute for Infectious Diseases 'Prof. Dr. Matei Balș' was the designated centre for managing Ebola alerts in Romania during the 2014 African outbreak. We surveyed Ebola knowledge, attitudes and perceptions (KAP) among the institute's healthcare workers.

STUDY DESIGN

This was a cross-sectional survey.

METHODS

The study consisted of a self-administered paper-based anonymous questionnaire that included 24 closed-item questions and two scales of personal concern.

RESULTS

Respondents were generally well informed; compared to nurses, doctors recorded a 1.9-fold higher rate of correct responses regarding Ebola transmission (P < 0.001), but both nurses and doctors correctly identified Ebola's aetiological agent. Nurses perceived higher personal (P = 0.008) and family (P < 0.001) risk than doctors. Respondents reporting high perceived risks were more likely to be less informed about Ebola (P = 0.019) and its prevention options (P = 0.033). Males were 6.7-fold more likely to volunteer than females (P = 0.001) and so were graduates of higher rather than lower education (1.5-fold more likely, P = 0.017) and doctors than nurses (1.7-fold more likely, P = 0.018). The institute ranked first among sources of information on Ebola; respondents who had received Ebola training in the institute 2 years previously were 1.2-1.3 times more likely to correctly identify transmission routes.

CONCLUSIONS

We have characterised KAP on Ebola disease among Romanian healthcare workers from a tertiary care hospital in Bucharest. Nurses, specialist physicians and laboratory personnel may need more frequent retraining than residents and senior physicians.

Amplification of perceived risk among users of a national travel health Web site during the 2013-2016 West African Ebola virus outbreak

Timely outbreak information was paramount to public health bodies issuing travel advisories during the 2013-2016 West Africa Ebola virus outbreak. This article explores the potential for a syndromic system/Shewhart control chart based on the online interaction with a national travel health Web site in comparison with searches on the Google UK search engine. The study showed an amplification of perceived risk among users of a national travel health Web site months before the World Health Organization declared the outbreak a Public Health Emergency of International Concern and the initial surge in public interest on Google UK in August 2014.

Disease mentions in airport and hospital geolocations expose dominance of news events for disease concerns

Background

In recent years, Twitter has been applied to monitor diseases through its facility to monitor users’ comments and concerns in real-time. The analysis of tweets for disease mentions should reflect not only user specific concerns but also disease outbreaks. This requires the use of standard terminological resources and can be focused on selected geographic locations. In our study, we differentiate between hospital and airport locations to better distinguish disease outbreaks from background mentions of disease concerns.

Results

Our analysis covers all geolocated tweets over a 6 months time period, uses SNOMED-CT as a standard medical terminology, and explores language patterns (as well as MetaMap) to identify mentions of diseases in reference to the geolocation of tweets. Contrary to our expectation, hospital and airport geolocations are not suitable to collect significant portions of tweets concerned with disease outcomes. Overall, geolocated tweets exposed a large number of messages commenting on disease-related news articles. Furthermore, the geolocated messages exposed an over-representation of non-communicable diseases in contrast to infectious diseases.

Conclusions

Our findings suggest that disease mentions on Twitter not only serve the purpose to share personal statements but also to share concerns about news articles. In particular, our assumption about the relevance of hospital and airport geolocations for an increased frequency of diseases mentions has not been met. To further address the linguistic cues, we propose the study of health forums to understand how a change in medium affects the language applied by the users. Finally, our research on the language use may provide essential clues to distinguish complementary trends in the use of language in Twitter when analysing health-related topics.

Global reaction to the recent outbreaks of Zika virus: Insights from a Big Data analysis

Objective

The recent spreading of Zika virus represents an emerging global health threat. As such, it is attracting public interest worldwide, generating a great amount of related Internet searches and social media interactions. The aim of this research was to understand Zika-related digital behavior throughout the epidemic spreading and to assess its consistence with real-world epidemiological data, using a behavioral informatics and analytics approach.

Methods

In this study, the global web-interest and reaction to the recently occurred outbreaks of the Zika Virus were analyzed in terms of tweets and Google Trends (GT), Google News, YouTube, and Wikipedia search queries. These data streams were mined from 1^st January 2004 to 31^st October 2016, with a focus on the period November 2015—October 2016. This analysis was complemented with the use of epidemiological data. Spearman’s correlation was performed to correlate all Zika-related data. Moreover, a multivariate regression was performed using Zika-related search queries as a dependent variable, and epidemiological data, number of inhabitants in 2015 and Human Development Index as predictor variables.

Results

Overall 3,864,395 tweets, 284,903 accesses to Wikipedia pages dedicated to the Zika virus were analyzed during the study period. All web-data sources showed that the main spike of researches and interactions occurred in February 2016 with a second peak in August 2016. All novel data streams-related activities increased markedly during the epidemic period with respect to pre-epidemic period when no web activity was detected. Correlations between data from all these web platforms resulted very high and statistically significant. The countries in which web searches were particularly concentrated are mainly from Central and South Americas. The majority of queries concerned the symptoms of the Zika virus, its vector of transmission, and its possible effect to babies, including microcephaly. No statistically significant correlation was found between novel data streams and global real-world epidemiological data. At country level, a correlation between the digital interest towards the Zika virus and Zika incidence rate or microcephaly cases has been detected.

Conclusions

An increasing public interest and reaction to the current Zika virus outbreak was documented by all web-data sources and a similar pattern of web reactions has been detected. The public opinion seems to be particularly worried by the alert of teratogenicity of the Zika virus. Stakeholders and health authorities could usefully exploited these internet tools for collecting the concerns of public opinion and reply to them, disseminating key information.

Early detection of perceived risk among users of a UK travel health website compared with internet search activity and media coverage during the 2015-2016 Zika virus outbreak: an observational study

OBJECTIVES

The Zika virus (ZIKV) outbreak in the Americas in 2015-2016 posed a novel global threat due to the association with congenital malformations and its rapid spread. Timely information about the spread of the disease was paramount to public health bodies issuing travel advisories. This paper looks at the online interaction with a national travel health website during the outbreak and compares this to trends in internet searches and news media output.

METHODS

Time trends were created for weekly views of ZIKV-related pages on a UK travel health website, relative search volumes for 'Zika' on Google UK, ZIKV-related items aggregated by Google UK News and rank of ZIKV travel advisories among all other pages between 15 November 2015 and 20 August 2016.

RESULTS

Time trends in traffic to the travel health website corresponded with Google searches, but less so with media items due to intense coverage of the Rio Olympics. Travel advisories for pregnant women were issued from 7 December 2015 and began to increase in popularity (rank) from early January 2016, weeks before a surge in interest as measured by Google searches/news items at the end of January 2016.

CONCLUSIONS

The study showed an amplification of perceived risk among users of a national travel health website weeks before the initial surge in public interest. This suggests a potential value for tools to detect changes in online information seeking behaviours for predicting periods of high demand where the routine capability of travel health services could be exceeded.

Utility and potential of rapid epidemic intelligence from internet-based sources

OBJECTIVES

Rapid epidemic detection is an important objective of surveillance to enable timely intervention, but traditional validated surveillance data may not be available in the required timeframe for acute epidemic control. Increasing volumes of data on the Internet have prompted interest in methods that could use unstructured sources to enhance traditional disease surveillance and gain rapid epidemic intelligence. We aimed to summarise Internet-based methods that use freely-accessible, unstructured data for epidemic surveillance and explore their timeliness and accuracy outcomes.

METHODS

Steps outlined in the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) checklist were used to guide a systematic review of research related to the use of informal or unstructured data by Internet-based intelligence methods for surveillance.

RESULTS

We identified 84 articles published between 2006-2016 relating to Internet-based public health surveillance methods. Studies used search queries, social media posts and approaches derived from existing Internet-based systems for early epidemic alerts and real-time monitoring. Most studies noted improved timeliness compared to official reporting, such as in the 2014 Ebola epidemic where epidemic alerts were generated first from ProMED-mail. Internet-based methods showed variable correlation strength with official datasets, with some methods showing reasonable accuracy.

CONCLUSION

The proliferation of publicly available information on the Internet provided a new avenue for epidemic intelligence. Methodologies have been developed to collect Internet data and some systems are already used to enhance the timeliness of traditional surveillance systems. To improve the utility of Internet-based systems, the key attributes of timeliness and data accuracy should be included in future evaluations of surveillance systems.