The Application of Internet-Based Sources for Public Health Surveillance (Infoveillance): Systematic Review

eHealth in pediatric respiratory allergy

PURPOSE OF REVIEW

This review explores the relevance of eHealth technologies to address unmet needs in pediatric respiratory allergies, particularly allergic rhinitis (AR) and asthma. Given the increasing burden of these conditions, there is a pressing need for effective solutions to enhance disease surveillance, diagnosis, and management.

RECENT FINDINGS

Recent literature highlights the potential of eHealth tools to transform pediatric respiratory allergy care. The use of digital data for infodemiology, application of machine learning models to improve diagnostic sensitivity, smartphone apps with digital patient reported outcome measure (PROMs) and embedded sensors to monitor disease, healthcare professional dashboards with real-time data monitoring and clinical decision support systems (CDSS) are advances emerging to optimize pediatric respiratory allergy care.

SUMMARY

Integrating eHealth technologies into the pediatric respiratory allergy care pathway is a potential solution for current healthcare challenges to better meet the needs of children with AR and asthma. However, while the potential of eHealth is evident, its widespread implementation in real-world practice requires continued research, collaboration, and efforts to overcome existing barriers.

Mapping the Characteristics of Respiratory Infectious Disease Epidemics in China Based on the Baidu Index from November 2022 to January 2023

Introduction

Infectious diseases pose a significant global health and economic burden, underscoring the critical need for precise predictive models. The Baidu index provides enhanced real-time surveillance capabilities that augment traditional systems.

Methods

Baidu search engine data on the keyword "fever" were extracted from 255 cities in China from November 2022 to January 2023. Onset and peak dates for influenza epidemics were identified by testing various criteria that combined thresholds and consecutive days.

Results

The most effective scenario for indicating epidemic commencement involved a 90th percentile threshold exceeded for seven consecutive days, minimizing false starts. Peak detection was optimized using a 7-day moving average, balancing stability and precision.

Discussion

The use of internet search data, such as the Baidu index, significantly improves the timeliness and accuracy of disease surveillance models. This innovative approach supports faster public health interventions and demonstrates its potential for enhancing epidemic monitoring and response efforts.

Electronic Case Reporting Development, Implementation, and Expansion in the United States

INTRODUCTION

The COVID-19 pandemic highlighted the need for a nationwide health information technology solution that could improve upon manual case reporting and decrease the clinical and administrative burden on the US health care system. We describe the development, implementation, and nationwide expansion of electronic case reporting (eCR), including its effect on public health surveillance and pandemic readiness.

METHODS

Multidisciplinary teams developed and implemented a standards-based, shared, scalable, and interoperable eCR infrastructure during 2014-2020. From January 27, 2020, to January 7, 2023, the team conducted a nationwide scale-up effort and determined the number of eCR-capable electronic health record (EHR) products, the number of reportable conditions available within the infrastructure, and technical connections of health care organizations (HCOs) and jurisdictional public health agencies (PHAs) to the eCR infrastructure. The team also conducted data quality studies to determine whether HCOs were discontinuing manual case reporting and early results of eCR timeliness.

RESULTS

During the study period, the number of eCR-capable EHR products developed or in development increased 11-fold (from 3 to 33), the number of reportable conditions available increased 28-fold (from 6 to 173), the number of HCOs connected to the eCR infrastructure increased 143-fold (from 153 to 22 000), and the number of jurisdictional PHAs connected to the eCR infrastructure increased 2.75-fold (from 24 to 66). Data quality reviews with PHAs resulted in select HCOs discontinuing manual case reporting and using eCR-exclusive case reporting in 13 PHA jurisdictions. The timeliness of eCR was <1 minute.

PRACTICE IMPLICATIONS

The growth of eCR can revolutionize public health case surveillance by producing data that are more timely and complete than manual case reporting while reducing reporting burden.

Google trends as an early indicator of African swine fever outbreaks in Southeast Asia

African Swine Fever (ASF) is a reportable disease of swine that causes far-reaching losses to affected countries and regions. Early detection is critically important to contain and mitigate the impact of ASF outbreaks, for which timely available data is essential. This research examines the potential use of Google Trends data as an early indicator of ASF outbreaks in Southeast Asia, focusing on the three largest swine producing countries, namely, Vietnam, the Philippines, and Thailand. Cross-correlation and Kullback-Leibler (KL) divergence indicators were used to evaluate the association between Google search trends and the number of ASF outbreaks reported. Our analysis indicate strong and moderate correlations between Google search trends and number of ASF outbreaks reported in Vietnam and the Philippines, respectively. In contrast, Thailand, the country of this group in which outbreaks were reported last, exhibits the weakest correlation (KL = 2.64), highlighting variations in public awareness and disease dynamics. These findings suggest that Google search trends are valuable for early detection of ASF. As the disease becomes endemic, integrating trends with other epidemiological data may support the design and implementation of surveillance strategies for transboundary animal diseases in Southeast Asia.

Infodemiology and infoveillance: framework for contagious exanthematous diseases, of childhood in Italy

BACKGROUND

Contagious exanthematous diseases are becoming a major public health problem. The purpose of this study was to evaluate the potential epidemiological trend of four infectious exanthematous diseases in Italy through the searches on the internet.

METHODS

We used the following Italian search term: 'Sesta malattia' (Sixth Disease, in English), 'Eritema Infettivo' (also knows 'Quinta malattia' in Italian; Fifth Disease in English), 'Quarta malattia' (Fourth Disease in English) and 'Scarlattina' (Scarlet fever in English). We overlapped Google Trends and Wikipedia data to perform a linear regression and correlation analysis. Statistical analyses were performed using the Spearman's rank correlation coefficient (rho). The study period is between July 2015 and December 2022.

RESULTS

The diseases considered have a seasonal trend and the search peaks between GT and Wikipedia overlap. A temporal correlation was observed between GT and Wikipedia search trends. Google Trends Internet search data showed strong correlation with Wikipedia with a rho statistically significant for Fifth disease (rho = 0.78), Fourth disease (rho = 0.76) and Scarlet-fever (rho = 0.77), moderate correlation for Sixth disease (rho = 0.32).

CONCLUSIONS

Infectious disease searches using Google and Wikipedia can be useful for public health surveillance and help policy makers implement prevention and information programs for the population, in addition to the fact that increases in searches could represent an early warning in the detection of outbreaks.

A Taxonomy for Health Information Systems

The health sector is highly digitized, which is enabling the collection of vast quantities of electronic data about health and well-being. These data are collected by a diverse array of information and communication technologies, including systems used by health care organizations, consumer and community sources such as information collected on the web, and passively collected data from technologies such as wearables and devices. Understanding the breadth of IT that collect these data and how it can be actioned is a challenge for the significant portion of the digital health workforce that interact with health data as part of their duties but are not for informatics experts. This viewpoint aims to present a taxonomy categorizing common information and communication technologies that collect electronic data. An initial classification of key information systems collecting electronic health data was undertaken via a rapid review of the literature. Subsequently, a purposeful search of the scholarly and gray literature was undertaken to extract key information about the systems within each category to generate definitions of the systems and describe the strengths and limitations of these systems.

Emerging Trends in Information-Seeking Behavior for Alpha-Gal Syndrome: Infodemiology Study Using Time Series and Content Analysis

BACKGROUND

Alpha-gal syndrome is an emerging allergy characterized by an immune reaction to the carbohydrate molecule alpha-gal found in red meat. This unique food allergy is likely triggered by a tick bite. Cases of the allergy are on the rise, but prevalence estimates do not currently exist. Furthermore, varying symptoms and limited awareness of the allergy among health care providers contribute to delayed diagnosis, leading individuals to seek out their own information and potentially self-diagnose.

OBJECTIVE

The study aimed to (1) describe the volume and patterns of information-seeking related to alpha-gal, (2) explore correlations between alpha-gal and lone star ticks, and (3) identify specific areas of interest that individuals are searching for in relation to alpha-gal.

METHODS

Google Trends Supercharged-Glimpse, a new extension of Google Trends, provides estimates of the absolute volume of searches and related search queries. This extension was used to assess trends in searches for alpha-gal and lone star ticks (lone star tick, alpha gal, and meat allergy, as well as food allergy for comparison) in the United States. Time series analyses were used to examine search volume trends over time, and Spearman correlation matrices and choropleth maps were used to explore geographic and temporal correlations between alpha-gal and lone star tick searches. Content analysis was performed on related search queries to identify themes and subcategories that are of interest to information seekers.

RESULTS

Time series analysis revealed a rapidly increasing trend in search volumes for alpha-gal beginning in 2015. After adjusting for long-term trends, seasonal trends, and media coverage, from 2015 to 2022, the predicted adjusted average annual percent change in search volume for alpha-gal was 33.78%. The estimated overall change in average search volume was 627%. In comparison, the average annual percent change was 9.23% for lone star tick, 7.34% for meat allergy, and 2.45% for food allergy during this time. Geographic analysis showed strong significant correlations between alpha-gal and lone star tick searches especially in recent years (ρ=0.80; P<.001), with primary overlap and highest search rates found in the southeastern region of the United States. Content analysis identified 10 themes of primary interest: diet, diagnosis or testing, treatment, medications or contraindications of medications, symptoms, tick related, specific sources of information and locations, general education information, alternative words for alpha-gal, and unrelated or other.

CONCLUSIONS

The study provides insights into the changing information-seeking patterns for alpha-gal, indicating growing awareness and interest. Alpha-gal search volume is increasing at a rapid rate. Understanding specific questions and concerns can help health care providers and public health educators to tailor communication strategies. The Google Trends Supercharged-Glimpse tool offers enhanced features for analyzing information-seeking behavior and can be valuable for infodemiology research. Further research is needed to explore the evolving prevalence and impact of alpha-gal syndrome.

Enhancing Public Health Surveillance: Outbreak Detection Algorithms Deployed for Syndromic Surveillance During Arbaeenia Mass Gatherings in Iraq

BACKGROUND

Large gatherings often involve extended and intimate contact among individuals, creating environments conducive to the spread of infectious diseases. Despite this, there is limited research utilizing outbreak detection algorithms to analyze real syndrome data from such events. This study sought to address this gap by examining the implementation and efficacy of outbreak detection algorithms for syndromic surveillance during mass gatherings in Iraq.

METHODS

For the study, 10 data collectors conducted field data collection over 10 days from August 25, 2023, to September 3, 2023. Data were gathered from 10 healthcare clinics situated along Ya Hussein Road, a major route from Najaf to Karbala in Iraq. Various outbreak detection algorithms, such as moving average, cumulative sum, and exponentially weighted moving average, were applied to analyze the reported syndromes.

RESULTS

During the 10 days from August 25, 2023, to September 3, 2023, 12202 pilgrims visited 10 health clinics along a route in Iraq. Most pilgrims were between 20 and 59 years old (77.4%, n=9444), with more than half being foreigners (58.1%, n=7092). Among the pilgrims, 40.5% (n=4938) exhibited syndromes, with influenza-like illness (ILI) being the most common (48.8%, n=2411). Other prevalent syndromes included food poisoning (21.2%, n=1048), heatstroke (17.7%, n=875), febrile rash (9.0%, n=446), and gastroenteritis (3.2%, n=158). The cumulative sum (CUSUM) algorithm was more effective than exponentially weighted moving average (EWMA) and moving average (MA) algorithms for detecting small shifts.

CONCLUSION

Effective public health surveillance systems are crucial during mass gatherings to swiftly identify and address emerging health risks. Utilizing advanced algorithms and real-time data analysis can empower authorities to improve their readiness and response capacity, thereby ensuring the protection of public health during these gatherings.

DF-DM: A foundational process model for multimodal data fusion in the artificial intelligence era

In the big data era, integrating diverse data modalities poses significant challenges, particularly in complex fields like healthcare. This paper introduces a new process model for multimodal Data Fusion for Data Mining, integrating embeddings and the Cross-Industry Standard Process for Data Mining with the existing Data Fusion Information Group model. Our model aims to decrease computational costs, complexity, and bias while improving efficiency and reliability. We also propose "disentangled dense fusion," a novel embedding fusion method designed to optimize mutual information and facilitate dense inter-modality feature interaction, thereby minimizing redundant information. We demonstrate the model's efficacy through three use cases: predicting diabetic retinopathy using retinal images and patient metadata, domestic violence prediction employing satellite imagery, internet, and census data, and identifying clinical and demographic features from radiography images and clinical notes. The model achieved a Macro F1 score of 0.92 in diabetic retinopathy prediction, an R-squared of 0.854 and sMAPE of 24.868 in domestic violence prediction, and a macro AUC of 0.92 and 0.99 for disease prediction and sex classification, respectively, in radiological analysis. These results underscore the Data Fusion for Data Mining model's potential to significantly impact multimodal data processing, promoting its adoption in diverse, resource-constrained settings.

GLP-1 Receptor Agonists and Related Mental Health Issues; Insights from a Range of Social Media Platforms Using a Mixed-Methods Approach

The emergence of glucagon-like peptide-1 receptor agonists (GLP-1 RAs; semaglutide and others) now promises effective, non-invasive treatment of obesity for individuals with and without diabetes. Social media platforms' users started promoting semaglutide/Ozempic as a weight-loss treatment, and the associated increase in demand has contributed to an ongoing worldwide shortage of the drug associated with levels of non-prescribed semaglutide intake. Furthermore, recent reports emphasized some GLP-1 RA-associated risks of triggering depression and suicidal thoughts. Consistent with the above, we aimed to assess the possible impact of GLP-1 RAs on mental health as being perceived and discussed in popular open platforms with the help of a mixed-methods approach. Reddit posts yielded 12,136 comments, YouTube videos 14,515, and TikTok videos 17,059, respectively. Out of these posts/entries, most represented matches related to sleep-related issues, including insomnia (n = 620 matches); anxiety (n = 353); depression (n = 204); and mental health issues in general (n = 165). After the initiation of GLP-1 RAs, losing weight was associated with either a marked improvement or, in some cases, a deterioration, in mood; increase/decrease in anxiety/insomnia; and better control of a range of addictive behaviors. The challenges of accessing these medications were a hot topic as well. To the best of our knowledge, this is the first study documenting if and how GLP-1 RAs are perceived as affecting mood, mental health, and behaviors. Establishing a clear cause-and-effect link between metabolic diseases, depression and medications is difficult because of their possible reciprocal relationship, shared underlying mechanisms and individual differences. Further research is needed to better understand the safety profile of these molecules and their putative impact on behavioral and non-behavioral addictions.

Capturing Emerging Experiential Knowledge for Vaccination Guidelines Through Natural Language Processing: Proof-of-Concept Study

BACKGROUND

Experience-based knowledge and value considerations of health professionals, citizens, and patients are essential to formulate public health and clinical guidelines that are relevant and applicable to medical practice. Conventional methods for incorporating such knowledge into guideline development often involve a limited number of representatives and are considered to be time-consuming. Including experiential knowledge can be crucial during rapid guidance production in response to a pandemic but it is difficult to accomplish.

OBJECTIVE

This proof-of-concept study explored the potential of artificial intelligence (AI)-based methods to capture experiential knowledge and value considerations from existing data channels to make these insights available for public health guideline development.

METHODS

We developed and examined AI-based methods in relation to the COVID-19 vaccination guideline development in the Netherlands. We analyzed Dutch messages shared between December 2020 and June 2021 on social media and on 2 databases from the Dutch National Institute for Public Health and the Environment (RIVM), where experiences and questions regarding COVID-19 vaccination are reported. First, natural language processing (NLP) filtering techniques and an initial supervised machine learning model were developed to identify this type of knowledge in a large data set. Subsequently, structural topic modeling was performed to discern thematic patterns related to experiences with COVID-19 vaccination.

RESULTS

NLP methods proved to be able to identify and analyze experience-based knowledge and value considerations in large data sets. They provide insights into a variety of experiential knowledge that is difficult to obtain otherwise for rapid guideline development. Some topics addressed by citizens, patients, and professionals can serve as direct feedback to recommendations in the guideline. For example, a topic pointed out that although travel was not considered as a reason warranting prioritization for vaccination in the national vaccination campaign, there was a considerable need for vaccines for indispensable travel, such as cross-border informal caregiving, work or study, or accessing specialized care abroad. Another example is the ambiguity regarding the definition of medical risk groups prioritized for vaccination, with many citizens not meeting the formal priority criteria while being equally at risk. Such experiential knowledge may help the early identification of problems with the guideline's application and point to frequently occurring exceptions that might initiate a revision of the guideline text.

CONCLUSIONS

This proof-of-concept study presents NLP methods as viable tools to access and use experience-based knowledge and value considerations, possibly contributing to robust, equitable, and applicable guidelines. They offer a way for guideline developers to gain insights into health professionals, citizens, and patients' experience-based knowledge, especially when conventional methods are difficult to implement. AI-based methods can thus broaden the evidence and knowledge base available for rapid guideline development and may therefore be considered as an important addition to the toolbox of pandemic preparedness.

Impact on online research on celebrities' uncommon diseases: the curious case of Justin Bieber and Ramsay Hunt syndrome

Aim

We investigated how to use Internet user searches to gauge the impact of a celebrity illness on global public interest.

Methods

The study design is cross-sectional. Data on Internet searches were obtained from Google Trends (GT) for the period between 2017-2022 using the search words "Ramsay Hunt syndrome" (RHS), "Ramsay Hunt syndrome type 2," "Herpes zoster," and "Justin Bieber." The frequency of specific page views for "Ramsay Hunt syndrome," "Ramsay Hunt syndrome type 1," Ramsay Hunt syndrome type 2," Ramsay Hunt syndrome type 3," "Herpes zoster," and "Justin Bieber" were collected via a Wikipedia analysis tool that shows the number of times a specific page is viewed. Statistical analyses were performed using the Pearson (r) and Spearman's rank correlation coefficient (rho).

Results

GT data, in 2022, show a strong correlation for Justin Bieber and RHS or RHS type 2 (r = 0.75); similarly, Wikipedia data show a strong correlation for Justin Bieber and the others explored terms (r > 0.75). Furthermore, the correlation was strong between GT and Wikipedia for RHS (rho = 0.89) and RHS type 2 (rho = 0.88).

Conclusions

The peak search times for the GT and Wikipedia pages were during the same period. Useful new tools and analyses of Internet traffic data may be effective in assessing the impact of announced celebrity uncommon illnesses on global public interest.

Using Baidu Index Data to Improve Chickenpox Surveillance in Yunnan, China: Infodemiology Study

BACKGROUND

Chickenpox is an old but easily neglected infectious disease. Although chickenpox is preventable by vaccines, vaccine breakthroughs often occur, and the chickenpox epidemic is on the rise. Chickenpox is not included in the list of regulated communicable diseases that must be reported and controlled by public and health departments; therefore, it is crucial to rapidly identify and report varicella outbreaks during the early stages. The Baidu index (BDI) can supplement the traditional surveillance system for infectious diseases, such as brucellosis and dengue, in China. The number of reported chickenpox cases and internet search data also showed a similar trend. BDI can be a useful tool to display the outbreak of infectious diseases.

OBJECTIVE

This study aimed to develop an efficient disease surveillance method that uses BDI to assist in traditional surveillance.

METHODS

Chickenpox incidence data (weekly from January 2017 to June 2021) reported by the Yunnan Province Center for Disease Control and Prevention were obtained to evaluate the relationship between the incidence of chickenpox and BDI. We applied a support vector machine regression (SVR) model and a multiple regression prediction model with BDI to predict the incidence of chickenpox. In addition, we used the SVR model to predict the number of chickenpox cases from June 2021 to the first week of April 2022.

RESULTS

The analysis showed that there was a close correlation between the weekly number of newly diagnosed cases and the BDI. In the search terms we collected, the highest Spearman correlation coefficient was 0.747. Most BDI search terms, such as "chickenpox," "chickenpox treatment," "treatment of chickenpox," "chickenpox symptoms," and "chickenpox virus," trend consistently. Some BDI search terms, such as "chickenpox pictures," "symptoms of chickenpox," "chickenpox vaccine," and "is chickenpox vaccine necessary," appeared earlier than the trend of "chickenpox virus." The 2 models were compared, the SVR model performed better in all the applied measurements: fitting effect, R²=0.9108, root mean square error (RMSE)=96.2995, and mean absolute error (MAE)=73.3988; and prediction effect, R²=0.548, RMSE=189.1807, and MAE=147.5412. In addition, we applied the SVR model to predict the number of reported cases weekly in Yunnan from June 2021 to April 2022 using the same period of the BDI. The results showed that the fluctuation of the time series from July 2021 to April 2022 was similar to that of the last year and a half with no change in the level of prevention and control.

CONCLUSIONS

These findings indicated that the BDI in Yunnan Province can predict the incidence of chickenpox in the same period. Thus, the BDI is a useful tool for monitoring the chickenpox epidemic and for complementing traditional monitoring systems.

Content and sentiment surveillance (CSI): A critical component for modeling modern epidemics

Comprehensive surveillance systems are the key to provide accurate data for effective modeling. Traditional symptom-based case surveillance has been joined with recent genomic, serologic, and environment surveillance to provide more integrated disease surveillance systems. A major gap in comprehensive disease surveillance is to accurately monitor potential population behavioral changes in real-time. Population-wide behaviors such as compliance with various interventions and vaccination acceptance significantly influence and drive the overall epidemic dynamics in the society. Original infoveillance utilizes online query data (e.g., Google and Wikipedia search of a specific content topic such as an epidemic) and later focuses on large volumes of online discourse data about the from social media platforms and further augments epidemic modeling. It mainly uses number of posts to approximate public awareness of the disease, and further compares with observed epidemic dynamics for better projection. The current COVID-19 pandemic shows that there is an urgency to further harness the rich, detailed content and sentiment information, which can provide more accurate and granular information on public awareness and perceptions toward multiple aspects of the disease, especially various interventions. In this perspective paper, we describe a novel conceptual analytical framework of content and sentiment infoveillance (CSI) and integration with epidemic modeling. This CSI framework includes data retrieval and pre-processing; information extraction via natural language processing to identify and quantify detailed time, location, content, and sentiment information; and integrating infoveillance with common epidemic modeling techniques of both mechanistic and data-driven methods. CSI complements and significantly enhances current epidemic models for more informed decision by integrating behavioral aspects from detailed, instantaneous infoveillance from massive social media data.

Digital public health leadership in the global fight for health security

The COVID-19 pandemic highlighted the need to prioritise mature digital health and data governance at both national and supranational levels to guarantee future health security. The Riyadh Declaration on Digital Health was a call to action to create the infrastructure needed to share effective digital health evidence-based practices and high-quality, real-time data locally and globally to provide actionable information to more health systems and countries. The declaration proposed nine key recommendations for data and digital health that need to be adopted by the global health community to address future pandemics and health threats. Here, we expand on each recommendation and provide an evidence-based roadmap for their implementation. This policy document serves as a resource and toolkit that all stakeholders in digital health and disaster preparedness can follow to develop digital infrastructure and protocols in readiness for future health threats through robust digital public health leadership.

Who tweets what about personalised medicine? Promises and concerns from Twitter discussions in Denmark

Digital health data are seen as valuable resources for the development of better and more efficient treatments, for instance through personalised medicine. However, health data are information about individuals who hold opinions and can challenge how data about them are used. Therefore it is important to understand public discussions around reuse of digital health data. Social media have been heralded as enabling new forms of public engagement and as a place to study social issues. In this paper, we study a public debate on Twitter about personalised medicine. We explore who participates in discussions about personalised medicine on Twitter and what they tweet about. Based on user-generated biographies we categorise users as having a 'Professional interest in personalised medicine' or as 'Private' users. We describe how users within the field tweet about the promises of personalised medicine, while users unaffiliated with the field tweet about the concrete realisation of these ambitions in the form of a new infrastructure and express concerns about the conditions for the implementation. Our study serves to remind people interested in public opinion that Twitter is a platform used for multiple purposes by different actors and not simply a bottom-up democratic forum. This study contributes with insights relevant to policymakers wishing to expand infrastructures for reuse of health data. First, by providing insights into what is discussed about health data reuse. Second, by exploring how Twitter can be used as a platform to study public discussions about reuse of health data.

Tackling the COVID-19 infodemic among Syrian refugees in Lebanon: Development and evaluation of the "Wikaytek" tool

Objective

The COVID-19 infodemic has been a global public health challenge, especially affecting vulnerable populations such as Syrian refugees with limited internet access and functional, health, digital, and media literacies. To address this problem, we developed Wikaytek, a software to diffuse reliable COVID-19 information using WhatsApp, the preferred communication channel among Syrian refugees. In this paper, we describe the systematic development of the tool.

Methods

We undertook a pilot study guided by the Humanitarian Engineering Initiative (HEI)'s user-centered design framework, comprising five stages: (a) user research, including needs assessment and desk review of interventions with target users; (b) concept design based on platform and source selection, message format, concept testing, and architecture design; (c) prototyping and implementation, encompassing software development and system operation; (d) user testing (alpha and beta); and (e) evaluation through software analytics and user interviews. We reported a qualitative process evaluation.

Results

Wikaytek scrapes validated and reliable COVID-19-related information from reputable sources on Twitter, automatically translates it into Arabic, attaches relevant media (images/video), and generates an audio format using Google text-to-speech. Then, messages are broadcast to WhatsApp. Our evaluation shows that users appreciate receiving "push" information from reliable sources they can trust and prefer the audio format over text.

Conclusions

Wikaytek is a useful and well-received software for diffusing credible information on COVID-19 among Syrian refugees with limited literacy, as it complements the texts with audio messages. The tool can be adapted to diffuse messages about other public health issues among vulnerable communities, extending its scope and reach in humanitarian settings.

Patterns and predictors of adolescent life change during the COVID-19 pandemic: a person-centered approach

The present study investigated patterns of adolescent life changes across multiple life domains and utilized a holistic-interactionistic perspective to examine their individual, familial, and societal correlates with a sample of 2544 Chinese parent-adolescent dyads. Adolescents were aged from 10 to 19 years old (50.16% girls). Latent profile analysis revealed five life change profiles, including three improved profiles at various degrees, one unchanged profile, and one worsened profile. The majority of adolescents had an improved or unchanged life. Multinomial logistic regression analyses found that most of the individual, familial, and societal factors predicted the group memberships. Notably, parent-adolescent conflict was a significant factor that predicted memberships of all patterns. These findings show the resilience of adolescents and indicate the need for policies and interventions that consider the holistic nature of adolescents' person-context system, especially during a global crisis such as the COVID-19 pandemic.

Effectiveness of using e-government platform "Absher" as a tool for noncommunicable diseases survey in Saudi Arabia 2019-2020: A cross-sectional study

Background

E-government platforms provide an opportunity to use a novel data source for population health surveillance (also known as e-health). Absher is a Saudi e-government platform with 23 million authenticated users, including residents and citizens in Saudi Arabia. All Absher users were invited to participate in a web-based survey to estimate the prevalence of noncommunicable diseases and their risk factors in Saudi Arabia.

Objective

To assess the potential of using an e-government platform (Absher) to administer web-based health surveys.

Methods

A cross-sectional, web-based health survey was administered to Absher users between April 2019 and March 2020. The survey instrument included eight items and took <5 min to complete. The respondents' data were compared to Saudi Arabia's 2016 census. Descriptive summary statistics of the prevalence of major noncommuncable diseases are presented and compared to population-based prevalence data from Saudi Arabia's World Health Survey (WHS) 2019. All analysis was conducted using Stata 13.0.

Results

Overall, the Absher health survey had a 24.6% response rate, with most respondents being male (84%), Saudi (67%), and between 30 and 44 years of age (49%). Overall, the prevalence of noncommunicable diseases and risk factors among respondents was high for overweight (35%) and obesity (30%) and low for asthma (6%). The prevalence of diabetes, dyslipidemia, and hypertension was between 15 and 17% on average, and 26.5% were smokers. In comparison to population-based World Health Survey estimates, the Absher survey overestimated obesity, diabetes, dyslipidemia, hypertension, and smoking rates, and underestimated overweight, whereas asthma prevalence was similar for Absher and the WHS.

Conclusions

With improvements in the study design, the use of e-government platforms can provide a useful and potentially low-cost data source for public health research.

Spontaneously generated online patient experience data - how and why is it being used in health research: an umbrella scoping review

PURPOSE

Social media has led to fundamental changes in the way that people look for and share health related information. There is increasing interest in using this spontaneously generated patient experience data as a data source for health research. The aim was to summarise the state of the art regarding how and why SGOPE data has been used in health research. We determined the sites and platforms used as data sources, the purposes of the studies, the tools and methods being used, and any identified research gaps.

METHODS

A scoping umbrella review was conducted looking at review papers from 2015 to Jan 2021 that studied the use of SGOPE data for health research. Using keyword searches we identified 1759 papers from which we included 58 relevant studies in our review.

RESULTS

Data was used from many individual general or health specific platforms, although Twitter was the most widely used data source. The most frequent purposes were surveillance based, tracking infectious disease, adverse event identification and mental health triaging. Despite the developments in machine learning the reviews included lots of small qualitative studies. Most NLP used supervised methods for sentiment analysis and classification. Very early days, methods need development. Methods not being explained. Disciplinary differences - accuracy tweaks vs application. There is little evidence of any work that either compares the results in both methods on the same data set or brings the ideas together.

CONCLUSION

Tools, methods, and techniques are still at an early stage of development, but strong consensus exists that this data source will become very important to patient centred health research.

Deep neural networks for simultaneously capturing public topics and sentiments during a pandemic. Application to a COVID-19 tweet dataset

Background

Public engagement is a key element for mitigating pandemics, and a good understanding of public opinion could help to encourage the successful adoption of public health measures by the population. In past years, deep learning has been increasingly applied to the analysis of text from social networks. However, most of the developed approaches can only capture topics or sentiments alone but not both together.

Objective

Here, we aimed to develop a new approach, based on deep neural networks, for simultaneously capturing public topics and sentiments and applied it to tweets sent just after the announcement of the COVID-19 pandemic by the World Health Organization (WHO).

Methods

A total of 1,386,496 tweets were collected, preprocessed, and split with a ratio of 80:20 into training and validation sets, respectively. We combined lexicons and convolutional neural networks to improve sentiment prediction. The trained model achieved an overall accuracy of 81% and a precision of 82% and was able to capture simultaneously the weighted words associated with a predicted sentiment intensity score. These outputs were then visualized via an interactive and customizable web interface based on a word cloud representation. Using word cloud analysis, we captured the main topics for extreme positive and negative sentiment intensity scores.

Results

In reaction to the announcement of the pandemic by the WHO, 6 negative and 5 positive topics were discussed on Twitter. Twitter users seemed to be worried about the international situation, economic consequences, and medical situation. Conversely, they seemed to be satisfied with the commitment of medical and social workers and with the collaboration between people.

Conclusions

We propose a new method based on deep neural networks for simultaneously extracting public topics and sentiments from tweets. This method could be helpful for monitoring public opinion during crises such as pandemics.

In.To. COVID-19 socio-epidemiological co-causality

Social media can forecast disease dynamics, but infoveillance remains focused on infection spread, with little consideration of media content reliability and its relationship to behavior-driven epidemiological outcomes. Sentiment-encoded social media indicators have been poorly developed for expressed text to forecast healthcare pressure and infer population risk-perception patterns. Here we introduce Infodemic Tomography (InTo) as the first web-based interactive infoveillance cybertechnology that forecasts and visualizes spatio-temporal sentiments and healthcare pressure as a function of social media positivity (i.e., Twitter here), considering both epidemic information and potential misinformation. Information spread is measured on volume and retweets, and the Value of Misinformation (VoMi) is introduced as the impact on forecast accuracy where misinformation has the highest dissimilarity in information dynamics. We validated InTo for COVID-19 in New Delhi and Mumbai by inferring distinct socio-epidemiological risk-perception patterns. We forecast weekly hospitalization and cases using ARIMA models and interpolate spatial hospitalization using geostatistical kriging on inferred risk perception curves between tweet positivity and epidemiological outcomes. Geospatial tweet positivity tracks accurately [Formula: see text]60[Formula: see text] of hospitalizations and forecasts hospitalization risk hotspots along risk aversion gradients. VoMi is higher for risk-prone areas and time periods, where misinformation has the highest non-linear predictability, with high incidence and positivity manifesting popularity-seeking social dynamics. Hospitalization gradients, VoMi, effective healthcare pressure and spatial model-data gaps can be used to predict hospitalization fluxes, misinformation, healthcare capacity gaps and surveillance uncertainty. Thus, InTo is a participatory instrument to better prepare and respond to public health crises by extracting and combining salient epidemiological and social surveillance at any desired space-time scale.

Machine and cognitive intelligence for human health: systematic review

Brain informatics is a novel interdisciplinary area that focuses on scientifically studying the mechanisms of human brain information processing by integrating experimental cognitive neuroscience with advanced Web intelligence-centered information technologies. Web intelligence, which aims to understand the computational, cognitive, physical, and social foundations of the future Web, has attracted increasing attention to facilitate the study of brain informatics to promote human health. A large number of articles created in the recent few years are proof of the investment in Web intelligence-assisted human health. This study systematically reviews academic studies regarding article trends, top journals, subjects, countries/regions, and institutions, study design, artificial intelligence technologies, clinical tasks, and performance evaluation. Results indicate that literature is especially welcomed in subjects such as medical informatics and health care sciences and service. There are several promising topics, for example, random forests, support vector machines, and conventional neural networks for disease detection and diagnosis, semantic Web, ontology mining, and topic modeling for clinical or biomedical text mining, artificial neural networks and logistic regression for prediction, and convolutional neural networks and support vector machines for monitoring and classification. Additionally, future research should focus on algorithm innovations, additional information use, functionality improvement, model and system generalization, scalability, evaluation, and automation, data acquirement and quality improvement, and allowing interaction. The findings of this study help better understand what and how Web intelligence can be applied to promote healthcare procedures and clinical outcomes. This provides important insights into the effective use of Web intelligence to support informatics-enabled brain studies.

Quality of information of websites dedicated to obesity: a systematic search to promote high level of information for Internet users and professionals

BACKGROUND

The Internet is increasingly used as a source of information. This study investigates with a multidimensional methodology the quality of information of websites dedicated to obesity treatment and weight-loss interventions. We compared websites in English, a language that it is used for the international scientific divulgation, and in Italian, a popular local language.

METHODS

Level of Evidence: Level I, systematic review search on four largely used search engines. Duplicated and unrelated websites were excluded. We checked: popularity with PageRank; technological quality with Nibbler; readability with the Flesch Reading Ease test or the Gulpease readability index; quality of information with the DISCERN scale, the JAMA benchmark criteria, and the adherence to the Health on the Net Code.

RESULTS

63 Italian websites and 41 English websites were evaluated. English websites invested more in the technological quality especially for the marketing, experience of the user, and mobile accessibility. Both the Italian and English websites were of poor quality and readability.

CONCLUSIONS

These results can inform guidelines for the improvement of health information and help Internet users to achieve a higher level of information. Users must find benefits of treatment, support to the shared decision-making, the sources used, the medical editor's supervision, and the risk of postponing the treatment.

Impact of a health campaign on Chinese public awareness of stroke: evidence from internet search data

INTRODUCTION

Health campaigns have the potential to improve public awareness, but their impact can be difficult to assess. Internet search data provide information concerning online health information-seeking behaviour in the population and may serve as a proxy for public awareness to evaluate health campaigns. This study aimed to measure the impact of World Stroke Day (WSD) in China using Baidu search data.

METHODS

Daily search index values (SIV) for the term 'stroke' were collected from January 2011 to December 2019 using the Baidu Index platform. We examined the mean difference in SIV between the 4 weeks surrounding WSD (period of interest) and the rest of the year (control period) for each year by t-test analysis. The mean difference between the period of interest and the control period was also calculated. The joinpoint regression model was used to analyse the trends of internet search activity 30 days before and after WSD for each year (2011-2019). Finally, the top and rising queries related to stroke during the week of the campaign in 2020 were summarised.

RESULTS

A significant mean increase in SIV of 418.5 (95% CI: 298.8 to 538.2) for the period of interest surrounding WSD was observed, 36.2% greater than the SIV during the control period (2011-2019). Short-term joinpoint analysis showed a significant increase in SIV 3 days before WSD, a peak on WSD and a decrease to the precampaign level 3 days after WSD. The rising related queries suggested that the public had increasing concerns about stroke warning signs, stroke prevention and stroke recovery during the campaign.

CONCLUSIONS

The WSD campaign increased internet search activity. These research techniques can be applied to evaluation of other health campaigns. Advancing understanding of public demand will enable tailoring of the campaign and strengthen health management.

Digital Public Health Surveillance Tools for Alcohol Use and HIV Risk Behaviors

There is a need for real-time and predictive data on alcohol use both broadly and specific to HIV. However, substance use and HIV data often suffer from lag times in reporting as they are typically measured from surveys, clinical case visits and other methods requiring extensive time for collection and analysis. Social big data might help to address this problem and be used to provide near real-time assessments of people's alcohol use and/or alcohol. This manuscript describes three types of social data sources (i.e., social media data, internet search data, and wearable device data) that might be used in surveillance of alcohol and HIV, and then discusses the implications and potential of implementing them as additional tools for public health surveillance.

A Pipeline to Understand Emerging Illness Via Social Media Data Analysis: Case Study on Breast Implant Illness

Background

A new illness can come to public attention through social media before it is medically defined, formally documented, or systematically studied. One example is a condition known as breast implant illness (BII), which has been extensively discussed on social media, although it is vaguely defined in the medical literature.

Objective

The objective of this study is to construct a data analysis pipeline to understand emerging illnesses using social media data and to apply the pipeline to understand the key attributes of BII.

Methods

We constructed a pipeline of social media data analysis using natural language processing and topic modeling. Mentions related to signs, symptoms, diseases, disorders, and medical procedures were extracted from social media data using the clinical Text Analysis and Knowledge Extraction System. We mapped the mentions to standard medical concepts and then summarized these mapped concepts as topics using latent Dirichlet allocation. Finally, we applied this pipeline to understand BII from several BII-dedicated social media sites.

Results

Our pipeline identified topics related to toxicity, cancer, and mental health issues that were highly associated with BII. Our pipeline also showed that cancers, autoimmune disorders, and mental health problems were emerging concerns associated with breast implants, based on social media discussions. Furthermore, the pipeline identified mentions such as rupture, infection, pain, and fatigue as common self-reported issues among the public, as well as concerns about toxicity from silicone implants.

Conclusions

Our study could inspire future studies on the suggested symptoms and factors of BII. Our study provides the first analysis and derived knowledge of BII from social media using natural language processing techniques and demonstrates the potential of using social media information to better understand similar emerging illnesses.

Forecasting the future number of pertussis cases using data from Google Trends

Background

Alternative methods could be used to enhance the monitoring and forecasting of re-emerging conditions such as pertussis. Here, whether data on the volume of Internet searching on pertussis could complement traditional modeling based solely on reported case numbers was assessed.

Methods

SARIMA models were fitted to describe reported weekly pertussis case numbers over a four-year period in Germany. Pertussis-related Google Trends data (GTD) was added as an external regressor. Predictions were made by the models, both with and without GTD, and compared with values within the validation dataset over a one-year and for a two-weeks period.

Results

Predictions of the traditional model using solely reported case numbers resulted in an RMSE (residual mean squared error) of 192.65 and 207.8, a mean absolute percentage error (MAPE) of 58.59 and 72.1, and a mean absolute error (MAE) 169.53 and 190.53 for the one-year and for the two-weeks period, respectively. The GTD expanded model achieved better forecasting accuracy (RMSE: 144.22 and 201.78), a MAPE 43.86, and 68.54 and a MAE of 124.46 and 178.96. Corrected Akaike Information Criteria also favored the GTD expanded model (1750.98 vs. 1746.73). The difference between the predictive performances was significant when using a two-sided Diebold-Mariano test (DM value: 6.86, p < 0.001) for the one-year period.

Conclusion

Internet-based surveillance data enhanced the predictive ability of a traditionally based model and should be considered as a method to enhance future disease modeling.

•

Pertussis-related Google Trends Data (GTD) showed a weak but significant correlation with the reported weekly number of pertussis cases.

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We fitted a SARIMA models to estimate reported weekly pertussis case numbers

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The GTD-expanded models achieved significantly better predictive accuracy than the traditional model over a one-year-period.

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Corrected Akaike Information Criteria also favored the GTD-Expanded SARIMA model.

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The use of GTD should be considered as a method to enhance pertussis forecasting.

Predicting New Daily COVID-19 Cases and Deaths Utilizing Search Engine Query Data in South Korea from 2020 to 2021: Infodemiology Study

Background

Given the ongoing COVID-19 pandemic situation, accurate predictions could greatly help in the health resource management for future waves. However, as a new entity, COVID-19’s disease dynamics seemed difficult to predict. External factors, such as internet search data, need to be included in the models to increase their accuracy. However, it remains unclear whether incorporating online search volumes into models leads to better predictive performances for long-term prediction.

Objective

The aim of this study was to analyze whether search engine query data are important variables that should be included in the models predicting new daily COVID-19 cases and deaths in short- and long-term periods.

Methods

We used country-level case-related data, NAVER search volumes, and mobility data obtained from Google and Apple for the period of January 20, 2020, to July 31, 2021, in South Korea. Data were aggregated into four subsets: 3, 6, 12, and 18 months after the first case was reported. The first 80% of the data in all subsets were used as the training set, and the remaining data served as the test set. Generalized linear models (GLMs) with normal, Poisson, and negative binomial distribution were developed, along with linear regression (LR) models with lasso, adaptive lasso, and elastic net regularization. Root mean square error values were defined as a loss function and were used to assess the performance of the models. All analyses and visualizations were conducted in SAS Studio, which is part of the SAS OnDemand for Academics.

Results

GLMs with different types of distribution functions may have been beneficial in predicting new daily COVID-19 cases and deaths in the early stages of the outbreak. Over longer periods, as the distribution of cases and deaths became more normally distributed, LR models with regularization may have outperformed the GLMs. This study also found that models performed better when predicting new daily deaths compared to new daily cases. In addition, an evaluation of feature effects in the models showed that NAVER search volumes were useful variables in predicting new daily COVID-19 cases, particularly in the first 6 months of the outbreak. Searches related to logistical needs, particularly for “thermometer” and “mask strap,” showed higher feature effects in that period. For longer prediction periods, NAVER search volumes were still found to constitute an important variable, although with a lower feature effect. This finding suggests that search term use should be considered to maintain the predictive performance of models.

Conclusions

NAVER search volumes were important variables in short- and long-term prediction, with higher feature effects for predicting new daily COVID-19 cases in the first 6 months of the outbreak. Similar results were also found for death predictions.

Artificial intelligence applications in social media for depression screening: A systematic review protocol for content validity processes

BACKGROUND

The popularization of social media has led to the coalescing of user groups around mental health conditions; in particular, depression. Social media offers a rich environment for contextualizing and predicting users' self-reported burden of depression. Modern artificial intelligence (AI) methods are commonly employed in analyzing user-generated sentiment on social media. In the forthcoming systematic review, we will examine the content validity of these computer-based health surveillance models with respect to standard diagnostic frameworks. Drawing from a clinical perspective, we will attempt to establish a normative judgment about the strengths of these modern AI applications in the detection of depression.

METHODS

We will perform a systematic review of English and German language publications from 2010 to 2020 in PubMed, APA PsychInfo, Science Direct, EMBASE Psych, Google Scholar, and Web of Science. The inclusion criteria span cohort, case-control, cross-sectional studies, randomized controlled studies, in addition to reports on conference proceedings. The systematic review will exclude some gray source materials, specifically editorials, newspaper articles, and blog posts. Our primary outcome is self-reported depression, as expressed on social media. Secondary outcomes will be the types of AI methods used for social media depression screen, and the clinical validation procedures accompanying these methods. In a second step, we will utilize the evidence-strengthening Population, Intervention, Comparison, Outcomes, Study type (PICOS) tool to refine our inclusion and exclusion criteria. Following the independent assessment of the evidence sources by two authors for the risk of bias, the data extraction process will culminate in a thematic synthesis of reviewed studies.

DISCUSSION

We present the protocol for a systematic review which will consider all existing literature from peer reviewed publication sources relevant to the primary and secondary outcomes. The completed review will discuss depression as a self-reported health outcome in social media material. We will examine the computational methods, including AI and machine learning techniques which are commonly used for online depression surveillance. Furthermore, we will focus on standard clinical assessments, as indicating content validity, in the design of the algorithms. The methodological quality of the clinical construct of the algorithms will be evaluated with the COnsensus-based Standards for the selection of health status Measurement Instruments (COSMIN) framework. We conclude the study with a normative judgment about the current application of AI to screen for depression on social media.

SYSTEMATIC REVIEW REGISTRATION

International Prospective Register of Systematic Reviews PROSPERO (registration number CRD42020187874).

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.

High variability in model performance of Google relative search volumes in spatially clustered COVID-19 areas of the USA

Objective: Incorporating spatial analyses and online health information queries may be beneficial in understanding the role of Google relative search volume (RSV) data as a secondary public health surveillance tool during pandemics. This study identified coronavirus disease 2019 (COVID-19) clustering and defined the predictability performance of Google RSV models in clustered and non-clustered areas of the USA.

Methods: Getis-Ord General and local G statistics were used to identify monthly clustering patterns. Monthly country- and state-level correlations between new daily COVID-19 cases and Google RSVs were assessed using Spearman's rank correlation coefficients and Poisson regression models for January–December 2020.

Results: Huge clusters involving multiple states were found, which resulted from various control measures in each state. This demonstrates the importance of state-to-state coordination in implementing control measures to tackle the spread of outbreaks. Variability in Google RSV model performance was found among states and time periods, possibly suggesting the need to use different frameworks for Google RSV data in each state. Moreover, the sign of correlation can be utilized to understand public responses to control and preventive measures, as well as in communicating risk.

Conclusion: COVID-19 Google RSV model accuracy in the USA may be influenced by COVID-19 transmission dynamics, policy-driven community awareness and past outbreak experiences.

Enhancing Influenza Epidemics Forecasting Accuracy in China with Both Official and Unofficial Online News Articles, 2019-2020

Real-time online data sources have contributed to timely and accurate forecasting of influenza activities while also suffered from instability and linguistic noise. Few previous studies have focused on unofficial online news articles, which are abundant in their numbers, rich in information, and relatively low in noise. This study examined whether monitoring both official and unofficial online news articles can improve influenza activity forecasting accuracy during influenza outbreaks. Data were retrieved from a Chinese commercial online platform and the website of the Chinese National Influenza Center. We modeled weekly fractions of influenza-related online news articles and compared them against weekly influenza-like illness (ILI) rates using autoregression analyses. We retrieved 153,958,695 and 149,822,871 online news articles focusing on the south and north of mainland China separately from 6 October 2019 to 17 May 2020. Our model based on online news articles could significantly improve the forecasting accuracy, compared to other influenza surveillance models based on historical ILI rates (p = 0.002 in the south; p = 0.000 in the north) or adding microblog data as an exogenous input (p = 0.029 in the south; p = 0.000 in the north). Our finding also showed that influenza forecasting based on online news articles could be 1-2 weeks ahead of official ILI surveillance reports. The results revealed that monitoring online news articles could supplement traditional influenza surveillance systems, improve resource allocation, and offer models for surveillance of other emerging diseases.

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.

Insights From the SmokeFree.gov Initiative Regarding the Use of Smoking Cessation Digital Platforms During the COVID-19 Pandemic: Cross-sectional Trends Analysis Study

BACKGROUND

Smoking is a plausible risk factor for COVID-19 progression and complications. Smoking cessation digital platforms transcend pandemic-driven social distancing and lockdown measures in terms of assisting smokers in their quit attempts.

OBJECTIVE

This study aims to examine trends in the number of visitors, followers, and subscribers on smoking cessation digital platforms from January to April 2020 and to compare these traffic data to those observed during the same 4-month period in 2019. The examination of prepandemic and postpandemic trends in smoking cessation digital platform traffic can reveal whether interest in smoking cessation among smokers is attributable to the COVID-19 pandemic.

METHODS

We obtained cross-sectional data from daily visitors on the SmokeFree website; the followers of six SmokeFree social media accounts; and subscribers to the SmokeFree SMS text messaging and mobile app interventions of the National Cancer Institute's SmokeFree.gov initiative platforms, which are publicly available to US smokers. Average daily percentage changes (ADPCs) were used to measure trends for the entire 2020 and 2019 study periods, whereas daily percentage changes (DPCs) were used to measure trends for each time segment of change within each 4-month period. Data analysis was conducted in May and June 2020.

RESULTS

The number of new daily visitors on the SmokeFree website (between days 39 and 44: DPC=18.79%; 95% CI 5.16% to 34.19%) and subscribers to the adult-focused interventions QuitGuide (between days 11 and 62: DPC=1.11%; 95% CI 0.80% to 1.43%) and SmokeFreeTXT (between days 11 and 89: DPC=0.23%; 95% CI 0.004% to 0.47%) increased, but this was followed by declines in traffic. No comparable peaks were observed in 2019. The number of new daily subscribers to quitSTART (ie, the teen-focused intervention) trended downward in 2020 (ADPC=-1.02%; 95% CI -1.88% to -0.15%), whereas the overall trend in the number of subscribers in 2019 was insignificant (P=.07). The number of SmokeFree social media account followers steadily increased by <0.1% over the 4-month study periods in 2019 and 2020.

CONCLUSIONS

Peaks in traffic on the SmokeFree website and adult-focused intervention platforms in 2020 could be attributed to an increased interest in smoking cessation among smokers during the COVID-19 pandemic. Coordinated campaigns, especially those for adolescents, should emphasize the importance of smoking cessation as a preventive measure against SARS-CoV-2 infection and raise awareness of digital smoking cessation platforms to capitalize on smokers' heightened interest during the pandemic.

A predictive internet-based model for COVID-19 hospitalization census

The COVID-19 pandemic has strained hospital resources and necessitated the need for predictive models to forecast patient care demands in order to allow for adequate staffing and resource allocation. Recently, other studies have looked at associations between Google Trends data and the number of COVID-19 cases. Expanding on this approach, we propose a vector error correction model (VECM) for the number of COVID-19 patients in a healthcare system (Census) that incorporates Google search term activity and healthcare chatbot scores. The VECM provided a good fit to Census and very good forecasting performance as assessed by hypothesis tests and mean absolute percentage prediction error. Although our study and model have limitations, we have conducted a broad and insightful search for candidate Internet variables and employed rigorous statistical methods. We have demonstrated the VECM can potentially be a valuable component to a COVID-19 surveillance program in a healthcare system.

A call for better understanding of social media in surveillance and management of noncommunicable diseases

Using social media for health purposes has attracted much attention over the past decade. Given the challenges of population ageing and changes in national health profile and disease patterns following the epidemiologic transition, researchers and policy-makers should pay attention to the potential of social media in chronic disease surveillance, management and support. This commentary overviews the evidence base for this inquiry and outlines the key challenges to research laying ahead. The authors provide concrete suggestions and recommendations for developing a research agenda to guide future investigation and action on this topic.

Digital technologies for monitoring infected people, identifying contacts and tracking transmission chains in the corona virus disease 2019 pandemic: A protocol for a systematic review

BACKGROUND

In times of the corona virus disease 2019 (COVID-19) pandemic, due to the urgent need to respond quickly to the challenges posed by the introduction of a new etiological agent and the peculiarity of the disease, which poses risks to people's lives and health, the use of digital technologies for monitoring and surveillance have been used as a means of fighting coronavirus. Thus, this study will identify the use of digital technologies to monitor, identify contacts and track transmission chains of COVID-19 worldwide.

METHODS

The systematic review of this protocol will follow the guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-Analyzes Protocols. We will include studies that present digital technologies used in the monitoring of infected people, contact identification and the transmission chain of COVID-19 developed worldwide. For the selection of articles, the following databases will be consulted: PubMed, EMBASE, LILACS, Web of Science, Science Direct, Scopus, Livivo and CINAHL. In addition, we will conduct extensive research on selected sources of gray literature, including bibliographic databases, web-based search engines, practice-oriented magazines and government websites. Data extraction will take place in 2 stages (1- title and abstract screening and 2- full-text screening) and will be carried out independently by 2 reviewers, using the Mendeley software and the Rayyan QCRI application. The studies will be characterized as to the type and design of the study in relation to the ease in demonstrating the technologies used and the type of information produced. If it is necessary to synthesize quantitative data, the heterogeneity assessment will be performed using I2 statistics, and the meta-analysis will be processed using Review Manager 5.3.

RESULTS

The development of this research will allow the knowledge of how these technologies were applied according to each territory and their effectiveness in reducing cases of COVID-19.

CONCLUSION

The results of this review can reveal the importance of modern technologies for reducing cases of COVID-19 and that these measures can be adopted by governments, organizations and for everyone.

RECORD OF SYSTEMATIC REVIEW

CRD42020211744.

The role of social media in spreading panic among primary and secondary school students during the COVID-19 pandemic: An online questionnaire study from the Gaza Strip, Palestine

BACKGROUND

The rapid outbreak of the COVID-19 pandemic has opened up various issues on social media platforms among school students. The dangerous issue is that misinformation, fake news, and rumours spread on social media faster than reliable information, and also faster than the virus itself, damaging the health systems and affecting the mental health of social media users.

OBJECTIVE

The current study aims at determining how social media affects the spread of panic about COVID-19 among primary and secondary school students in the Gaza Strip, Palestine.

METHODS

The data were collected through an online questionnaire. By utilizing convenience sampling, we have reached a total of 1067 school students, aged between 6 and 18 years, from 56 schools located in the Gaza Strip, Palestine. Independent Samples T-test, ANOVA, and chi-square tests were used to compare the data.

RESULTS

The results showed that social media has a significant impact on spreading panic about COVID-19 among school students, with a potential negative impact on their mental health and psychological well-being. Facebook was the most common social media platform among students (81.8%), where female students had a higher likelihood than male students to use it to get news about COVID-19 (p < 0.001). Health news was the most frequently topic seen, read, or heard (n = 529, 56.2%) during the COVID-19 pandemic, where males were more likely to follow health news than females (p < 0.001). The majority of the students (n = 736, 78.1%) were psychologically affected, whereas those physically affected were the lowest (n = 12, 1.3%). Female students were psychologically affected and experienced greater fear significantly more than male students (p < 0.001). The effect of social media panic depending on a student's age and gender. (p < 0.001). This study showed a significant positive correlation between social media and spreading panic about COVID-19 (R = 0.891).

CONCLUSIONS

During the closure of schools, students are using social media to continue their learning as well as to know more information about the COVID-19 outbreak. Social media has a main role in rapidly spreading of panic about the COVID-19 pandemic among students in the Gaza Strip.

What triggers online help-seeking retransmission during the COVID-19 period? Empirical evidence from Chinese social media

The past nine months witnessed COVID-19's fast-spreading at the global level. Limited by medical resources shortage and uneven facilities distribution, online help-seeking becomes an essential approach to cope with public health emergencies for many ordinaries. This study explores the driving forces behind the retransmission of online help-seeking posts. We built an analytical framework that emphasized content characteristics, including information completeness, proximity, support seeking type, disease severity, and emotion of help-seeking messages. A quantitative content analysis was conducted with a probability sample consisting of 727 posts. The results illustrate the importance of individual information completeness, high proximity, instrumental support seeking. This study also demonstrates slight inconformity with the severity principle but stresses the power of anger in help-seeking messages dissemination. As one of the first online help-seeking diffusion analyses in the COVID-19 period, our research provides a reference for constructing compelling and effective help-seeking posts during a particular period. It also reveals further possibilities for harnessing social media's power to promote reciprocal and cooperative actions as a response to this deepening global concern.

Online Public Attention During the Early Days of the COVID-19 Pandemic: Infoveillance Study Based on Baidu Index

BACKGROUND

The COVID-19 pandemic has become a global public health event, attracting worldwide attention. As a tool to monitor public awareness, internet search engines have been widely used in public health emergencies.

OBJECTIVE

This study aims to use online search data (Baidu Index) to monitor the public's attention and verify internet search engines' function in public attention monitoring of public health emergencies.

METHODS

We collected the Baidu Index and the case monitoring data from January 20, 2020, to April 20, 2020. We combined the Baidu Index of keywords related to COVID-19 to describe the public attention's temporal trend and spatial distribution, and conducted the time lag cross-correlation analysis.

RESULTS

The Baidu Index temporal trend indicated that the changes of the Baidu Index had a clear correspondence with the development time node of the pandemic. The Baidu Index spatial distribution showed that in the regions of central and eastern China, with denser populations, larger internet user bases, and higher economic development levels, the public was more concerned about COVID-19. In addition, the Baidu Index was significantly correlated with six case indicators of new confirmed cases, new death cases, new cured discharge cases, cumulative confirmed cases, cumulative death cases, and cumulative cured discharge cases. Moreover, the Baidu Index was 0-4 days earlier than new confirmed and new death cases, and about 20 days earlier than new cured and discharged cases while 3-5 days later than the change of cumulative cases.

CONCLUSIONS

The national public's demand for epidemic information is urgent regardless of whether it is located in the hardest hit area. The public was more sensitive to the daily new case data that represents the progress of the epidemic, but the public's attention to the epidemic situation in other areas may lag behind. We could set the Baidu Index as the sentinel and the database in the online infoveillance system for infectious disease and public health emergencies. According to the monitoring data, the government needs to prevent and control the possible outbreak in advance and communicate the risks to the public so as to ensure the physical and psychological health of the public in the epidemic.

Systematic analysis of the scientific literature on population surveillance

Introduction

Population surveillance provides data on the health status of the population through continuous scrutiny of different indicators. Identifying risk factors is essential for the quickly detecting and controlling of epidemic outbreaks and reducing the incidence of cross-infections and non-communicable diseases. The objective of the present study is to analyze research on population surveillance, identifying the main topics of interest for investigators in the area.

Methodology

We included documents indexed in the Web of Science Core Collection in the period from 2000 to 2019 and assigned with the generic Medical Subject Heading (MeSH) “population surveillance” or its related terms (“public health surveillance,” “sentinel surveillance” or “biosurveillance”). A co-occurrence analysis was undertaken to identify the document clusters comprising the main research topics. Scientific production, collaboration, and citation patterns in each of the clusters were characterized bibliometrically. We also analyzed research on coronaviruses, relating the results obtained to the management of the COVID-19 pandemic.

Results

We included 39,184 documents, which reflected a steady growth in scientific output driven by papers on “Public, Environmental & Occupational Health” (21.62% of the documents) and “Infectious Diseases” (10.49%). Research activity was concentrated in North America (36.41%) and Europe (32.09%). The USA led research in the area (40.14% of documents). Ten topic clusters were identified, including “Disease Outbreaks,” which is closely related to two other clusters (“Genetics” and “Influenza”). Other clusters of note were “Cross Infections” as well as one that brought together general public health concepts and topics related to non-communicable diseases (cardiovascular and coronary diseases, mental diseases, diabetes, wound and injuries, stroke, and asthma). The rest of the clusters addressed “Neoplasms,” “HIV,” “Pregnancy,” “Substance Abuse/Obesity,” and “Tuberculosis.” Although research on coronavirus has focused on population surveillance only occasionally, some papers have analyzed and collated guidelines whose relevance to the dissemination and management of the COVID-19 pandemic has become obvious. Topics include tracing the spread of the virus, limiting mass gatherings that would facilitate its propagation, and the imposition of quarantines. There were important differences in the scientific production and citation of different clusters: the documents on mental illnesses, stroke, substance abuse/obesity, and cross-infections had much higher citations than the clusters on disease outbreaks, tuberculosis, and especially coronavirus, where these values are substantially lower.

Conclusions

The role of population surveillance should be strengthened, promoting research and the development of public health surveillance systems in countries whose contribution to the area is limited.

Dynamic Panel Estimate-Based Health Surveillance of SARS-CoV-2 Infection Rates to Inform Public Health Policy: Model Development and Validation

BACKGROUND

SARS-CoV-2, the novel coronavirus that causes COVID-19, is a global pandemic with higher mortality and morbidity than any other virus in the last 100 years. Without public health surveillance, policy makers cannot know where and how the disease is accelerating, decelerating, and shifting. Unfortunately, existing models of COVID-19 contagion rely on parameters such as the basic reproduction number and use static statistical methods that do not capture all the relevant dynamics needed for surveillance. Existing surveillance methods use data that are subject to significant measurement error and other contaminants.

OBJECTIVE

The aim of this study is to provide a proof of concept of the creation of surveillance metrics that correct for measurement error and data contamination to determine when it is safe to ease pandemic restrictions. We applied state-of-the-art statistical modeling to existing internet data to derive the best available estimates of the state-level dynamics of COVID-19 infection in the United States.

METHODS

Dynamic panel data (DPD) models were estimated with the Arellano-Bond estimator using the generalized method of moments. This statistical technique enables control of various deficiencies in a data set. The validity of the model and statistical technique was tested.

RESULTS

A Wald chi-square test of the explanatory power of the statistical approach indicated that it is valid (χ210=1489.84, P<.001), and a Sargan chi-square test indicated that the model identification is valid (χ2946=935.52, P=.59). The 7-day persistence rate for the week of June 27 to July 3 was 0.5188 (P<.001), meaning that every 10,000 new cases in the prior week were associated with 5188 cases 7 days later. For the week of July 4 to 10, the 7-day persistence rate increased by 0.2691 (P=.003), indicating that every 10,000 new cases in the prior week were associated with 7879 new cases 7 days later. Applied to the reported number of cases, these results indicate an increase of almost 100 additional new cases per day per state for the week of July 4-10. This signifies an increase in the reproduction parameter in the contagion models and corroborates the hypothesis that economic reopening without applying best public health practices is associated with a resurgence of the pandemic.

CONCLUSIONS

DPD models successfully correct for measurement error and data contamination and are useful to derive surveillance metrics. The opening of America involves two certainties: the country will be COVID-19-free only when there is an effective vaccine, and the "social" end of the pandemic will occur before the "medical" end. Therefore, improved surveillance metrics are needed to inform leaders of how to open sections of the United States more safely. DPD models can inform this reopening in combination with the extraction of COVID-19 data from existing websites.

Evaluation of Korean-Language COVID-19-Related Medical Information on YouTube: Cross-Sectional Infodemiology Study

Background

In South Korea, the number of coronavirus disease (COVID-19) cases has declined rapidly and much sooner than in other countries. South Korea is one of the most digitalized countries in the world, and YouTube may have served as a rapid delivery mechanism for increasing public awareness of COVID-19. Thus, the platform may have helped the South Korean public fight the spread of the disease.

Objective

The aim of this study is to compare the reliability, overall quality, title–content consistency, and content coverage of Korean-language YouTube videos on COVID-19, which have been uploaded by different sources.

Methods

A total of 200 of the most viewed YouTube videos from January 1, 2020, to April 30, 2020, were screened, searching in Korean for the terms “Coronavirus,” “COVID,” “Corona,” “Wuhan virus,” and “Wuhan pneumonia.” Non-Korean videos and videos that were duplicated, irrelevant, or livestreamed were excluded. Source and video metrics were collected. The videos were scored based on the following criteria: modified DISCERN index, Journal of the American Medical Association Score (JAMAS) benchmark criteria, global quality score (GQS), title–content consistency index (TCCI), and medical information and content index (MICI).

Results

Of the 105 total videos, 37.14% (39/105) contained misleading information; independent user–generated videos showed the highest proportion of misleading information at 68.09% (32/47), while all of the government-generated videos were useful. Government agency–generated videos achieved the highest median score of DISCERN (5.0, IQR 5.0-5.0), JAMAS (4.0, IQR 4.0-4.0), GQS (4.0, IQR 3.0-4.5), and TCCI (5.0, IQR 5.0-5.0), while independent user–generated videos achieved the lowest median score of DISCERN (2.0, IQR 1.0-3.0), JAMAS (2.0, IQR 1.5-2.0), GQS (2.0, IQR 1.5-2.0), and TCCI (3.0, IQR 3.0-4.0). However, the total MICI was not significantly different among sources. “Transmission and precautionary measures” were the most commonly covered content by government agencies, news agencies, and independent users. In contrast, the most mentioned content by news agencies was “prevalence,” followed by “transmission and precautionary measures.”

Conclusions

Misleading videos had more likes, fewer comments, and longer running times than useful videos. Korean-language YouTube videos on COVID-19 uploaded by different sources varied significantly in terms of reliability, overall quality, and title–content consistency, but the content coverage was not significantly different. Government-generated videos had higher reliability, overall quality, and title–content consistency than independent user–generated videos.

How to Fight an Infodemic: The Four Pillars of Infodemic Management

In this issue of the Journal of Medical Internet Research, the World Health Organization (WHO) is presenting a framework for managing the coronavirus disease (COVID-19) infodemic. Infodemiology is now acknowledged by public health organizations and the WHO as an important emerging scientific field and critical area of practice during a pandemic. From the perspective of being the first “infodemiolgist” who originally coined the term almost two decades ago, I am positing four pillars of infodemic management: (1) information monitoring (infoveillance); (2) building eHealth Literacy and science literacy capacity; (3) encouraging knowledge refinement and quality improvement processes such as fact checking and peer-review; and (4) accurate and timely knowledge translation, minimizing distorting factors such as political or commercial influences. In the current COVID-19 pandemic, the United Nations has advocated that facts and science should be promoted and that these constitute the antidote to the current infodemic. This is in stark contrast to the realities of infodemic mismanagement and misguided upstream filtering, where social media platforms such as Twitter have advertising policies that sideline science organizations and science publishers, treating peer-reviewed science as “inappropriate content.”

Online Information Exchange and Anxiety Spread in the Early Stage of the Novel Coronavirus (COVID-19) Outbreak in South Korea: Structural Topic Model and Network Analysis

Background

In case of a population-wide infectious disease outbreak, such as the novel coronavirus disease (COVID-19), people’s online activities could significantly affect public concerns and health behaviors due to difficulty in accessing credible information from reliable sources, which in turn causes people to seek necessary information on the web. Therefore, measuring and analyzing online health communication and public sentiment is essential for establishing effective and efficient disease control policies, especially in the early stage of an outbreak.

Objective

This study aimed to investigate the trends of online health communication, analyze the focus of people’s anxiety in the early stages of COVID-19, and evaluate the appropriateness of online information.

Methods

We collected 13,148 questions and 29,040 answers related to COVID-19 from Naver, the most popular Korean web portal (January 20, 2020, to March 2, 2020). Three main methods were used in this study: (1) the structural topic model was used to examine the topics in the online questions; (2) word network analysis was conducted to analyze the focus of people’s anxiety and worry in the questions; and (3) two medical doctors assessed the appropriateness of the answers to the questions, which were primarily related to people’s anxiety.

Results

A total of 50 topics and 6 cohesive topic communities were identified from the questions. Among them, topic community 4 (suspecting COVID-19 infection after developing a particular symptom) accounted for the largest portion of the questions. As the number of confirmed patients increased, the proportion of topics belonging to topic community 4 also increased. Additionally, the prolonged situation led to a slight increase in the proportion of topics related to job issues. People’s anxieties and worries were closely related with physical symptoms and self-protection methods. Although relatively appropriate to suspect physical symptoms, a high proportion of answers related to self-protection methods were assessed as misinformation or advertisements.

Conclusions

Search activity for online information regarding the COVID-19 outbreak has been active. Many of the online questions were related to people’s anxieties and worries. A considerable portion of corresponding answers had false information or were advertisements. The study results could contribute reference information to various countries that need to monitor public anxiety and provide appropriate information in the early stage of an infectious disease outbreak, including COVID-19. Our research also contributes to developing methods for measuring public opinion and sentiment in an epidemic situation based on natural language data on the internet.

Using Reports of Symptoms and Diagnoses on Social Media to Predict COVID-19 Case Counts in Mainland China: Observational Infoveillance Study

BACKGROUND

Coronavirus disease (COVID-19) has affected more than 200 countries and territories worldwide. This disease poses an extraordinary challenge for public health systems because screening and surveillance capacity is often severely limited, especially during the beginning of the outbreak; this can fuel the outbreak, as many patients can unknowingly infect other people.

OBJECTIVE

The aim of this study was to collect and analyze posts related to COVID-19 on Weibo, a popular Twitter-like social media site in China. To our knowledge, this infoveillance study employs the largest, most comprehensive, and most fine-grained social media data to date to predict COVID-19 case counts in mainland China.

METHODS

We built a Weibo user pool of 250 million people, approximately half the entire monthly active Weibo user population. Using a comprehensive list of 167 keywords, we retrieved and analyzed around 15 million COVID-19-related posts from our user pool from November 1, 2019 to March 31, 2020. We developed a machine learning classifier to identify "sick posts," in which users report their own or other people's symptoms and diagnoses related to COVID-19. Using officially reported case counts as the outcome, we then estimated the Granger causality of sick posts and other COVID-19 posts on daily case counts. For a subset of geotagged posts (3.10% of all retrieved posts), we also ran separate predictive models for Hubei province, the epicenter of the initial outbreak, and the rest of mainland China.

RESULTS

We found that reports of symptoms and diagnosis of COVID-19 significantly predicted daily case counts up to 14 days ahead of official statistics, whereas other COVID-19 posts did not have similar predictive power. For the subset of geotagged posts, we found that the predictive pattern held true for both Hubei province and the rest of mainland China regardless of the unequal distribution of health care resources and the outbreak timeline.

CONCLUSIONS

Public social media data can be usefully harnessed to predict infection cases and inform timely responses. Researchers and disease control agencies should pay close attention to the social media infosphere regarding COVID-19. In addition to monitoring overall search and posting activities, leveraging machine learning approaches and theoretical understanding of information sharing behaviors is a promising approach to identify true disease signals and improve the effectiveness of infoveillance.

Correlations of Online Search Engine Trends With Coronavirus Disease (COVID-19) Incidence: Infodemiology Study

BACKGROUND

The coronavirus disease (COVID-19) is the latest pandemic of the digital age. With the internet harvesting large amounts of data from the general population in real time, public databases such as Google Trends (GT) and the Baidu Index (BI) can be an expedient tool to assist public health efforts.

OBJECTIVE

The aim of this study is to apply digital epidemiology to the current COVID-19 pandemic to determine the utility of providing adjunctive epidemiologic information on outbreaks of this disease and evaluate this methodology in the case of future pandemics.

METHODS

An epidemiologic time series analysis of online search trends relating to the COVID-19 pandemic was performed from January 9, 2020, to April 6, 2020. BI was used to obtain online search data for China, while GT was used for worldwide data, the countries of Italy and Spain, and the US states of New York and Washington. These data were compared to real-world confirmed cases and deaths of COVID-19. Chronologic patterns were assessed in relation to disease patterns, significant events, and media reports.

RESULTS

Worldwide search terms for shortness of breath, anosmia, dysgeusia and ageusia, headache, chest pain, and sneezing had strong correlations (r>0.60, P<.001) to both new daily confirmed cases and deaths from COVID-19. GT COVID-19 (search term) and GT coronavirus (virus) searches predated real-world confirmed cases by 12 days (r=0.85, SD 0.10 and r=0.76, SD 0.09, respectively, P<.001). Searches for symptoms of diarrhea, fever, shortness of breath, cough, nasal obstruction, and rhinorrhea all had a negative lag greater than 1 week compared to new daily cases, while searches for anosmia and dysgeusia peaked worldwide and in China with positive lags of 5 days and 6 weeks, respectively, corresponding with widespread media coverage of these symptoms in COVID-19.

CONCLUSIONS

This study demonstrates the utility of digital epidemiology in providing helpful surveillance data of disease outbreaks like COVID-19. Although certain online search trends for this disease were influenced by media coverage, many search terms reflected clinical manifestations of the disease and showed strong correlations with real-world cases and deaths.