SARS and population health technology

Web-based surveillance of respiratory infection outbreaks: retrospective analysis of Italian COVID-19 epidemic waves using Google Trends

Introduction

Large-scale diagnostic testing has been proven insufficient to promptly monitor the spread of the Coronavirus disease 2019. Electronic resources may provide better insight into the early detection of epidemics. We aimed to retrospectively explore whether the Google search volume has been useful in detecting Severe Acute Respiratory Syndrome Coronavirus outbreaks early compared to the swab-based surveillance system.

Methods

The Google Trends website was used by applying the research to three Italian regions (Lombardy, Marche, and Sicily), covering 16 million Italian citizens. An autoregressive-moving-average model was fitted, and residual charts were plotted to detect outliers in weekly searches of five keywords. Signals that occurred during periods labelled as free from epidemics were used to measure Positive Predictive Values and False Negative Rates in anticipating the epidemic wave occurrence.

Results

Signals from "fever," "cough," and "sore throat" showed better performance than those from "loss of smell" and "loss of taste." More than 80% of true epidemic waves were detected early by the occurrence of at least an outlier signal in Lombardy, although this implies a 20% false alarm signals. Performance was poorer for Sicily and Marche.

Conclusion

Monitoring the volume of Google searches can be a valuable tool for early detection of respiratory infectious disease outbreaks, particularly in areas with high access to home internet. The inclusion of web-based syndromic keywords is promising as it could facilitate the containment of COVID-19 and perhaps other unknown infectious diseases in the future.

Online searches of children’s oseltamivir in public primary and specialized care: Detecting influenza outbreaks in Finland using dedicated databases for health care professionals

Introduction

Health care professionals working in primary and specialized care typically search for medical information from Internet sources. In Finland, Physician’s Databases are online portals aimed at professionals seeking medical information. As dosage errors may occur when prescribing medication to children, professionals’ need for reliable medical information has increased in public health care centers and hospitals. Influenza continues to be a public health threat, with young children at risk of developing severe illness and easily transmitting the virus. Oseltamivir is used to treat children with influenza. The objective of this study was to compare searches for children’s oseltamivir and influenza diagnoses in primary and specialized care, and to determine if the searches could aid detection of influenza outbreaks.

Methods

We compared searches in Physician’s Databases for children’s oral suspension of oseltamivir (6 mg/mL) for influenza diagnoses of children under 7 years and laboratory findings of influenza A and B from the National Infectious Disease Register. Searches and diagnoses were assessed in primary and specialized care across Finland by season from 2012–2016. The Moving Epidemic Method (MEM) calculated seasonal starts and ends, and paired differences in the mean compared two indicators. Correlation was tested to compare seasons.

Results

We found that searches and diagnoses in primary and specialized care showed visually similar patterns annually. The MEM-calculated starting weeks in searches appeared mainly in the same week. Oseltamivir searches in primary care preceded diagnoses by −1.0 weeks (95% CI: −3.0, −0.3; p = 0.132) with very high correlation (τ = 0.913). Specialized care oseltamivir searches and diagnoses correlated moderately (τ = 0.667).

Conclusion

Health care professionals’ searches for children’s oseltamivir in online databases linked with the registers of children’s influenza diagnoses in primary and specialized care. Therefore, database searches should be considered as supplementary information in disease surveillance when detecting influenza epidemics.

Online searching trend on Covid-19 using Google trend: infodemiological study in Malaysia

Since January 2020, the emergence of Covid-19 has sparked a worldwide search for information about Covid-19. People frequently use the internet to search the information on the virus. However, the pandemic have triggered the information-seeking trends among public. As a result, large amount of information could lead to infodemic. It will create public concerned such as panic and paranoid because this information spread rapidly. The aim of this study is to analyze information about Covid-19 that has been searched in Malaysia. We investigated online search behavior related to the Covid-19 outbreak among public by using Google Trends to understand public searching behavior on Covid-19. The findings from this study can be used as a tool to monitor public searching activities on Covid-19, which could predict future action regarding the outbreak.

Interest in dentistry in early months of the COVID-19 global pandemic: A Google Trends approach

Background

In early the COVID‐19 pandemic, routine dental treatments have been delayed due to the risk of disease transmission. This delay may lead public to search for information on the Internet for a solution.

Objectives

This study aims to evaluate the public interest in dentistry in the early months of the COVID‐19 global pandemic in the selected countries.

Methods

The daily numbers of new COVID‐19 cases were recorded for China, South Korea, Italy, Germany, Russia, Ukraine and Turkey. For these countries, Internet search interest of the keyword ‘dentistry’, ‘coronavirus’, ‘COVID‐19’, ‘SARS‐CoV‐2’ and ‘pandemic’ in the early months of the COVID‐19 pandemic was evaluated by using Google Trends data.

Results

In most countries included the public Internet search interest in ‘dentistry+coronavirus+COVID‐19+SARS‐CoV‐2+pandemic’ peaked prior to the peak of new COVID‐19 cases. While a statistically significant positive correlation was observed between the number of new cases and Google Trends data in China, South Korea, Italy and Germany, a statistically significant negative correlation was observed in Turkey.

Conclusion

The peak public interest in dentistry has been prior to the peak of COVID‐19 new cases in most countries. The use of Internet data can provide useful information about pandemics and many other diseases.

COVID-19 information received by the Peruvian population, during the first phase of the pandemic, and its association with developing psychological distress: Information about COVID-19 and distress in Peru

ABSTRACT

It is suspected that the information the population has about coronavirus disease 2019 (COVID-19) determines both its preventive measures and its effects on mental health. The internet and social media are the sources that have largely replaced the official and traditional channels of information. The objective of this study is to analyse the influence of the sources used by the population in Peru to obtain information on COVID-19 and its association with developing psychological distress (PD) and preventive measures against contagion.1699 questionnaires were analysed. A previously validated instrument adapted to Peru was used. Participants were questioned about the information received regarding COVID-19, its sources, time of exposition, assessment, or beliefs about it. Mental health was measured with the Goldberg General Health Questionnaire. Descriptive and bivariate analysis were performed, developing a classification and regression tree for PD based on beliefs and information about the pandemic.The most used source of information on COVID-19 in Peru was social media and this is associated with developing PD, both in the general population and among health professionals. The quality of the information about treatments for COVID-19 is associated with PD in the general population, whereas prognosis generates more distress among healthcare professionals. The biggest concern is transmitting the virus to family members, close persons, or patients, with more confidence in health professionals than in the health system.The health authorities should use the social media to transmit quality information about COVID-19 and, at the same time, to gather in real time the opinions on the implemented preventive measures. For all, this it is necessary to have higher credibility in the population to increase the confidence in the health system, looking at basic aspects for compliance with prevention measures and improvement of mental health.

Challenges in the development of digital public health interventions and mapped solutions: Findings from a scoping review

Background

“Digital public health” has emerged from an interest in integrating digital technologies into public health. However, significant challenges which limit the scale and extent of this digital integration in various public health domains have been described. We summarized the literature about these challenges and identified strategies to overcome them.

Methods

We adopted Arksey and O’Malley's framework (2005) integrating adaptations by Levac et al. (2010). OVID Medline, Embase, Google Scholar, and 14 government and intergovernmental agency websites were searched using terms related to “digital” and “public health.” We included conceptual and explicit descriptions of digital technologies in public health published in English between 2000 and June 2020. We excluded primary research articles about digital health interventions. Data were extracted using a codebook created using the European Public Health Association's conceptual framework for digital public health.

Results and analysis

Overall, 163 publications were included from 6953 retrieved articles with the majority (64%, n = 105) published between 2015 and June 2020. Nontechnical challenges to digital integration in public health concerned ethics, policy and governance, health equity, resource gaps, and quality of evidence. Technical challenges included fragmented and unsustainable systems, lack of clear standards, unreliability of available data, infrastructure gaps, and workforce capacity gaps. Identified strategies included securing political commitment, intersectoral collaboration, economic investments, standardized ethical, legal, and regulatory frameworks, adaptive research and evaluation, health workforce capacity building, and transparent communication and public engagement.

Conclusion

Developing and implementing digital public health interventions requires efforts that leverage identified strategies to overcome diverse challenges encountered in integrating digital technologies in public health.

United States Influenza Search Patterns Since the Emergence of COVID-19: Infodemiology Study

Background

The emergence and media coverage of COVID-19 may have affected influenza search patterns, possibly affecting influenza surveillance results using Google Trends.

Objective

We aimed to investigate if the emergence of COVID-19 was associated with modifications in influenza search patterns in the United States.

Methods

We retrieved US Google Trends data (relative number of searches for specified terms) for the topics influenza, Coronavirus disease 2019, and symptoms shared between influenza and COVID-19. We calculated the correlations between influenza and COVID-19 search data for a 1-year period after the first COVID-19 diagnosis in the United States (January 21, 2020 to January 20, 2021). We constructed a seasonal autoregressive integrated moving average model and compared predicted search volumes, using the 4 previous years, with Google Trends relative search volume data. We built a similar model for shared symptoms data. We also assessed correlations for the past 5 years between Google Trends influenza data, US Centers for Diseases Control and Prevention influenza-like illness data, and influenza media coverage data.

Results

We observed a nonsignificant weak correlation (ρ= –0.171; P=0.23) between COVID-19 and influenza Google Trends data. Influenza search volumes for 2020-2021 distinctly deviated from values predicted by seasonal autoregressive integrated moving average models—for 6 weeks within the first 13 weeks after the first COVID-19 infection was confirmed in the United States, the observed volume of searches was higher than the upper bound of 95% confidence intervals for predicted values. Similar results were observed for shared symptoms with influenza and COVID-19 data. The correlation between Google Trends influenza data and CDC influenza-like-illness data decreased after the emergence of COVID-19 (2020-2021: ρ=0.643; 2019-2020: ρ=0.902), while the correlation between Google Trends influenza data and influenza media coverage volume remained stable (2020-2021: ρ=0.746; 2019-2020: ρ=0.707).

Conclusions

Relevant differences were observed between predicted and observed influenza Google Trends data the year after the onset of the COVID-19 pandemic in the United States. Such differences are possibly due to media coverage, suggesting limitations to the use of Google Trends as a flu surveillance tool.

Twenty years of integrated disease surveillance and response in Sub-Saharan Africa: challenges and opportunities for effective management of infectious disease epidemics

INTRODUCTION

This systematic review aimed to analyse the performance of the Integrated Disease Surveillance and Response (IDSR) strategy in Sub-Saharan Africa (SSA) and how its implementation has embraced advancement in information technology, big data analytics techniques and wealth of data sources.

METHODS

HINARI, PubMed, and advanced Google Scholar databases were searched for eligible articles. The review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analysis Protocols.

RESULTS

A total of 1,809 articles were identified and screened at two stages. Forty-five studies met the inclusion criteria, of which 35 were country-specific, seven covered the SSA region, and three covered 3-4 countries. Twenty-six studies assessed the IDSR core functions, 43 the support functions, while 24 addressed both functions. Most of the studies involved Tanzania (9), Ghana (6) and Uganda (5). The routine Health Management Information System (HMIS), which collects data from health care facilities, has remained the primary source of IDSR data. However, the system is characterised by inadequate data completeness, timeliness, quality, analysis and utilisation, and lack of integration of data from other sources. Under-use of advanced and big data analytical technologies in performing disease surveillance and relating multiple indicators minimises the optimisation of clinical and practice evidence-based decision-making.

CONCLUSIONS

This review indicates that most countries in SSA rely mainly on traditional indicator-based disease surveillance utilising data from healthcare facilities with limited use of data from other sources. It is high time that SSA countries consider and adopt multi-sectoral, multi-disease and multi-indicator platforms that integrate other sources of health information to provide support to effective detection and prompt response to public health threats.

Exploring the Utility of Google Mobility Data During the COVID-19 Pandemic in India: Digital Epidemiological Analysis

BACKGROUND

Association between human mobility and disease transmission has been established for COVID-19, but quantifying the levels of mobility over large geographical areas is difficult. Google has released Community Mobility Reports (CMRs) containing data about the movement of people, collated from mobile devices.

OBJECTIVE

The aim of this study is to explore the use of CMRs to assess the role of mobility in spreading COVID-19 infection in India.

METHODS

In this ecological study, we analyzed CMRs to determine human mobility between March and October 2020. The data were compared for the phases before the lockdown (between March 14 and 25, 2020), during lockdown (March 25-June 7, 2020), and after the lockdown (June 8-October 15, 2020) with the reference periods (ie, January 3-February 6, 2020). Another data set depicting the burden of COVID-19 as per various disease severity indicators was derived from a crowdsourced API. The relationship between the two data sets was investigated using the Kendall tau correlation to depict the correlation between mobility and disease severity.

RESULTS

At the national level, mobility decreased from -38% to -77% for all areas but residential (which showed an increase of 24.6%) during the lockdown compared to the reference period. At the beginning of the unlock phase, the state of Sikkim (minimum cases: 7) with a -60% reduction in mobility depicted more mobility compared to -82% in Maharashtra (maximum cases: 1.59 million). Residential mobility was negatively correlated (-0.05 to -0.91) with all other measures of mobility. The magnitude of the correlations for intramobility indicators was comparatively low for the lockdown phase (correlation ≥0.5 for 12 indicators) compared to the other phases (correlation ≥0.5 for 45 and 18 indicators in the prelockdown and unlock phases, respectively). A high correlation coefficient between epidemiological and mobility indicators was observed for the lockdown and unlock phases compared to the prelockdown phase.

CONCLUSIONS

Mobile-based open-source mobility data can be used to assess the effectiveness of social distancing in mitigating disease spread. CMR data depicted an association between mobility and disease severity, and we suggest using this technique to supplement future COVID-19 surveillance.

The Contribution of Digital Health in the Response to Covid-19 in Vietnam

Emerging from early of 2020, the COVID-19 pandemic has become one of the most serious health crisis globally. In response to such threat, a wide range of digital health applications has been deployed in Vietnam to strengthen surveillance, risk communication, diagnosis, and treatment of COVID-19. Digital health has brought enormous benefits to the fight against COVID-19, however, numerous constrains in digital health application remain. Lack of strong governance of digital health development and deployment; insufficient infrastructure and staff capacity for digital health application are among the main drawbacks. Despite several outstanding problems, digital health is expected to contribute to reducing the spread, improving the effectiveness of pandemic control, and adding to the dramatic transformation of the health system the post-COVID era.

Associations of COVID-19 online information sources and information overload with psychological distress symptoms: a population-based study

Using information communication technologies as information sources of COVID-19 was associated with psychological problems, but mechanisms remain uncertain. We examined associations of COVID-19 information sources and information overload with psychological distress symptoms and explored the mediating effect of information overload in Hong Kong. A random sample of Chinese adults (N = 1501; 52.6% females; 55.0% aged 30-59) from dual landline and online surveys in April 2020 reported frequency of using traditional media, internet websites, social networking sites, instant messaging, and online discussion forums as COVID-19 information sources. Psychological distress symptoms were measured using the Patient Health Questionnaire 4-item ([PHQ-4]; 0-12). Information overload defined as a perception of being overwhelmed was measured (1-6). Linear regressions were performed to analyze the associations, and the mediating effect of information overload was examined. Greater PHQ-4 score was observed for frequent use of internet websites (adjusted b = 0.58, 95% CI 0.29, 0.87, adjusted β = 0.12) and online discussion forums (adjusted b = 0.39, 95% CI 0.08, 0.70, adjusted β = 0.08) and information overload (adjusted b = 0.54, 95% CI 0.44, 0.63, adjusted β = 0.28). Information overload mediated 44.9% and 36.9% of associations of frequent use of internet websites and online discussion forums with PHQ-4 score, respectively. Frequent use of social networking sites was associated with lower PHQ-4 score (adjusted b = -0.37, 95% CI -0.69, -0.04, adjusted β = -0.07) partially through lower information overload (37.5%). Psychological distress symptoms were associated with frequent use of internet websites and online discussion forums as COVID-19 information sources, which were mediated through information overload.

Virtually Serving the Underserved: Resident Perceptions of Telemedicine Use While Training During Coronavirus Disease 2019

Background: To adapt during the coronavirus disease 2019 (COVID-19) pandemic, a large safety-net hospital in Atlanta, Georgia and other hospitals across the United Sates, increased telemedicine (TM) for outpatient visits. The impact on resident physicians, including minority providers, had not yet been reported. We aimed to assess how primary care residents perceived increased TM in clinics during the pandemic, and assess resident burnout. Materials and Methods: An online survey was sent to 60 internal medicine (IM) and 18 family medicine (FM) residents who used TM to treat underserved patients, from March to May 2020, at the onset of the COVID-19 pandemic. The questionnaire included questions on resident demographics, satisfaction with TM, screening capability for COVID-19, effectiveness for patient education on COVID-19, impact on patient interactions and time management. Burnout was assessed with the Abbreviated Maslach Burnout Inventory. Results: A majority (62 of 78) eligible residents (79.5%) responded to the survey. Ninety-one percent of residents agreed that TM was a secure alternative to face-to-face encounters. Seventy-nine percent used it to screen for signs/symptoms of COVID-19 and 93% provided patient education on COVID-19. Average visit length decreased by 10-20 min with TM. Post-TM, scores for overall burnout were decreased (p = 0.0003) and less residents in total exhibited burnout (p = 0.0156). Discussion and Conclusions: IM and FM residents viewed TM as an efficient way to screen for and provide education on COVID-19, as well as a secure alternative to face-to-face encounters. With increased used of TM during COVID-19, there was decreased burnout among primary care residents.

Sentiment analysis and its applications in fighting COVID-19 and infectious diseases: A systematic review

The COVID-19 pandemic caused by the novel coronavirus SARS-CoV-2 occurred unexpectedly in China in December 2019. Tens of millions of confirmed cases and more than hundreds of thousands of confirmed deaths are reported worldwide according to the World Health Organisation. News about the virus is spreading all over social media websites. Consequently, these social media outlets are experiencing and presenting different views, opinions and emotions during various outbreak-related incidents. For computer scientists and researchers, big data are valuable assets for understanding people's sentiments regarding current events, especially those related to the pandemic. Therefore, analysing these sentiments will yield remarkable findings. To the best of our knowledge, previous related studies have focused on one kind of infectious disease. No previous study has examined multiple diseases via sentiment analysis. Accordingly, this research aimed to review and analyse articles about the occurrence of different types of infectious diseases, such as epidemics, pandemics, viruses or outbreaks, during the last 10 years, understand the application of sentiment analysis and obtain the most important literature findings. Articles on related topics were systematically searched in five major databases, namely, ScienceDirect, PubMed, Web of Science, IEEE Xplore and Scopus, from 1 January 2010 to 30 June 2020. These indices were considered sufficiently extensive and reliable to cover our scope of the literature. Articles were selected based on our inclusion and exclusion criteria for the systematic review, with a total of n = 28 articles selected. All these articles were formed into a coherent taxonomy to describe the corresponding current standpoints in the literature in accordance with four main categories: lexicon-based models, machine learning-based models, hybrid-based models and individuals. The obtained articles were categorised into motivations related to disease mitigation, data analysis and challenges faced by researchers with respect to data, social media platforms and community. Other aspects, such as the protocol being followed by the systematic review and demographic statistics of the literature distribution, were included in the review. Interesting patterns were observed in the literature, and the identified articles were grouped accordingly. This study emphasised the current standpoint and opportunities for research in this area and promoted additional efforts towards the understanding of this research field.

Telehealth Demand Trends During the COVID-19 Pandemic in the Top 50 Most Affected Countries: Infodemiological Evaluation

Background

The COVID-19 pandemic has led to urgent calls for the adoption of telehealth solutions. However, public interest and demand for telehealth during the pandemic remain unknown.

Objective

We used an infodemiological approach to estimate the worldwide demand for telehealth services during COVID-19, focusing on the 50 most affected countries and comparing the demand for such services with the level of information and communications technology (ICT) infrastructure available.

Methods

We used Google Trends, the Baidu Index (China), and Yandex Keyword Statistics (Russia) to extract data on worldwide and individual countries’ telehealth-related internet searches from January 1 to July 7, 2020, presented as relative search volumes (RSV; range 0-100). Daily COVID-19 cases and deaths were retrieved from the World Health Organization. Individual countries’ ICT infrastructure profiles were retrieved from the World Economic Forum Report.

Results

Across the 50 countries, the mean RSV was 18.5 (SD 23.2), and the mean ICT index was 62.1 (SD 15.0). An overall spike in worldwide telehealth-related RSVs was observed from March 11, 2020 (RSV peaked to 76.0), which then tailed off in June-July 2020 (mean RSV for the period was 25.8), but remained higher than pre-March RSVs (mean 7.29). By country, 42 (84%) manifested increased RSVs over the evaluation period, with the highest observed in Canada (RSV=100) and the United States (RSV=96). When evaluating associations between RSV and the ICT index, both the United States and Canada demonstrated high RSVs and ICT scores (≥70.3). In contrast, European countries had relatively lower RSVs (range 3.4-19.5) despite high ICT index scores (mean 70.3). Several Latin American (Brazil, Chile, Colombia) and South Asian (India, Bangladesh, Pakistan) countries demonstrated relatively higher RSVs (range 13.8-73.3) but low ICT index scores (mean 44.6), indicating that the telehealth demand outstrips the current ICT infrastructure.

Conclusions

There is generally increased interest and demand for telehealth services across the 50 countries most affected by COVID-19, highlighting the need to scale up telehealth capabilities, during and beyond the pandemic.

Characterization of Vaccine Tweets During the Early Stage of the COVID-19 Outbreak in the United States: Topic Modeling Analysis

BACKGROUND

During the early stages of the COVID-19 pandemic, developing safe and effective coronavirus vaccines was considered critical to arresting the spread of the disease. News and social media discussions have extensively covered the issue of coronavirus vaccines, with a mixture of vaccine advocacies, concerns, and oppositions.

OBJECTIVE

This study aimed to uncover the emerging themes in Twitter users' perceptions and attitudes toward vaccines during the early stages of the COVID-19 outbreak.

METHODS

This study employed topic modeling to analyze tweets related to coronavirus vaccines at the start of the COVID-19 outbreak in the United States (February 21 to March 20, 2020). We created a predefined query (eg, "COVID" AND "vaccine") to extract the tweet text and metadata (number of followers of the Twitter account and engagement metrics based on likes, comments, and retweeting) from the Meltwater database. After preprocessing the data, we tested Latent Dirichlet Allocation models to identify topics associated with these tweets. The model specifying 20 topics provided the best overall coherence, and each topic was interpreted based on its top associated terms.

RESULTS

In total, we analyzed 100,209 tweets containing keywords related to coronavirus and vaccines. The 20 topics were further collapsed based on shared similarities, thereby generating 7 major themes. Our analysis characterized 26.3% (26,234/100,209) of the tweets as News Related to Coronavirus and Vaccine Development, 25.4% (25,425/100,209) as General Discussion and Seeking of Information on Coronavirus, 12.9% (12,882/100,209) as Financial Concerns, 12.7% (12,696/100,209) as Venting Negative Emotions, 9.9% (9908/100,209) as Prayers and Calls for Positivity, 8.1% (8155/100,209) as Efficacy of Vaccine and Treatment, and 4.9% (4909/100,209) as Conspiracies about Coronavirus and Its Vaccines. Different themes demonstrated some changes over time, mostly in close association with news or events related to vaccine developments. Twitter users who discussed conspiracy theories, the efficacy of vaccines and treatments, and financial concerns had more followers than those focused on other vaccine themes. The engagement level-the extent to which a tweet being retweeted, quoted, liked, or replied by other users-was similar among different themes, but tweets venting negative emotions yielded the lowest engagement.

CONCLUSIONS

This study enriches our understanding of public concerns over new vaccines or vaccine development at early stages of the outbreak, bearing implications for influencing vaccine attitudes and guiding public health efforts to cope with infectious disease outbreaks in the future. This study concluded that public concerns centered on general policy issues related to coronavirus vaccines and that the discussions were considerably mixed with political views when vaccines were not made available. Only a small proportion of tweets focused on conspiracy theories, but these tweets demonstrated high engagement levels and were often contributed by Twitter users with more influence.

The Recent Technologies to Curb the Second-Wave of COVID-19 Pandemic

Different epidemics, specially Coronavirus, have caused critical misfortunes in various fields like monetary deprivation, survival conditions, thus diminishing the overall individual fulfillment. Various worldwide associations and different hierarchies of government fraternity are endeavoring to offer the necessary assistance in eliminating the infection impacts but unfortunately standing up to the non-appearance of resources and expertise. In contrast to all other pandemics, Coronavirus has proven to exhibit numerous requirements such that curated appropriation and determination of innovations are required to deal with the vigorous undertakings, which include precaution, detection, and medication. Innovative advancements are essential for the subsequent pandemics where-in the forthcoming difficulties can indeed be approached to such a degree that it facilitates constructive solutions more comprehensively. In this study, futuristic and emerging innovations are analyzed, improving COVID-19 effects for the general public. Large data sets need to be advanced so that extensive models related to deep analysis can be used to combat Coronavirus infection, which can be done by applying Artificial intelligence techniques such as Natural Language Processing (NLP), Machine Learning (ML), and Computer vision to varying processing files. This article aims to furnish variation sets of innovations that can be utilized to eliminate COVID-19 and serve as a resource for the coming generations. At last, elaboration associated with future state-of-the-art technologies and the attainable sectors of AI methodologies has been mentioned concerning the post-COVID-19 world to enable the different ideas for dealing with the pandemic-based difficulties.

COVID-19 predictability in the United States using Google Trends time series

During the unprecedented situation that all countries around the globe are facing due to the Coronavirus disease 2019 (COVID-19) pandemic, which has also had severe socioeconomic consequences, it is imperative to explore novel approaches to monitoring and forecasting regional outbreaks as they happen or even before they do so. To that end, in this paper, the role of Google query data in the predictability of COVID-19 in the United States at both national and state level is presented. As a preliminary investigation, Pearson and Kendall rank correlations are examined to explore the relationship between Google Trends data and COVID-19 data on cases and deaths. Next, a COVID-19 predictability analysis is performed, with the employed model being a quantile regression that is bias corrected via bootstrap simulation, i.e., a robust regression analysis that is the appropriate statistical approach to taking against the presence of outliers in the sample while also mitigating small sample estimation bias. The results indicate that there are statistically significant correlations between Google Trends and COVID-19 data, while the estimated models exhibit strong COVID-19 predictability. In line with previous work that has suggested that online real-time data are valuable in the monitoring and forecasting of epidemics and outbreaks, it is evident that such infodemiology approaches can assist public health policy makers in addressing the most crucial issues: flattening the curve, allocating health resources, and increasing the effectiveness and preparedness of their respective health care systems.

Shifting the Paradigm: The Dress-COV Telegram Bot as a Tool for Participatory Medicine

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic management is limited by great uncertainty, for both health systems and citizens. Facing this information gap requires a paradigm shift from traditional approaches to healthcare to the participatory model of improving health. This work describes the design and function of the Doing Risk sElf-assessment and Social health Support for COVID (Dress-COV) system. It aims to establish a lasting link between the user and the tool; thus, enabling modeling of the data to assess individual risk of infection, or developing complications, to improve the individual's self-empowerment. The system uses bot technology of the Telegram application. The risk assessment includes the collection of user responses and the modeling of data by machine learning models, with increasing appropriateness based on the number of users who join the system. The main results reflect: (a) the individual's compliance with the tool; (b) the security and versatility of the architecture; (c) support and promotion of self-management of behavior to accommodate surveillance system delays; (d) the potential to support territorial health providers, e.g., the daily efforts of general practitioners (during this pandemic, as well as in their routine practices). These results are unique to Dress-COV and distinguish our system from classical surveillance applications.

COVID-19 predictability in the United States using Google Trends time series

During the unprecedented situation that all countries around the globe are facing due to the Coronavirus disease 2019 (COVID-19) pandemic, which has also had severe socioeconomic consequences, it is imperative to explore novel approaches to monitoring and forecasting regional outbreaks as they happen or even before they do so. To that end, in this paper, the role of Google query data in the predictability of COVID-19 in the United States at both national and state level is presented. As a preliminary investigation, Pearson and Kendall rank correlations are examined to explore the relationship between Google Trends data and COVID-19 data on cases and deaths. Next, a COVID-19 predictability analysis is performed, with the employed model being a quantile regression that is bias corrected via bootstrap simulation, i.e., a robust regression analysis that is the appropriate statistical approach to taking against the presence of outliers in the sample while also mitigating small sample estimation bias. The results indicate that there are statistically significant correlations between Google Trends and COVID-19 data, while the estimated models exhibit strong COVID-19 predictability. In line with previous work that has suggested that online real-time data are valuable in the monitoring and forecasting of epidemics and outbreaks, it is evident that such infodemiology approaches can assist public health policy makers in addressing the most crucial issues: flattening the curve, allocating health resources, and increasing the effectiveness and preparedness of their respective health care systems.

A review and content analysis of national apps for COVID-19 management using Mobile Application Rating Scale (MARS)

The expansion of mobile health apps for the management of COVID-19 grew exponentially in recent months. However, no study has evaluated these apps. The objective of this study was to develop a reliable measure and rate the quality of COVID-19 mobile health apps, to eventually provide a roadmap for future mHealth app development. In this study, we used COVID-related keywords to identify apps for iOS and Android devices. 13 apps (13.5% of the total number of apps identified) were selected for evaluation. App quality was assessed independently using MARS by two reviewers. Search queries yielded a total of 97 potentially relevant apps, of which 13 met our final inclusion criteria. Kendall's coefficient of concordance value for the inter-rater agreement was 0.93 (p = .03). COVID-19 GOV PK app had the highest average MARS score (4.7/5), and all of the apps had acceptable MARS scores (> 3.0). This study suggests that most COVID-related apps meet acceptable criteria for quality, content, or functionality, and they must highlight esthetic and interesting features for overall quality improvement to be welcomed by users.

Characteristics and Symptoms of App Users Seeking COVID-19-Related Digital Health Information and Remote Services: Retrospective Cohort Study

BACKGROUND

Patient-facing digital health tools have been promoted to help patients manage concerns related to COVID-19 and to enable remote care and self-care during the COVID-19 pandemic. It has also been suggested that these tools can help further our understanding of the clinical characteristics of this new disease. However, there is limited information on the characteristics and use patterns of these tools in practice.

OBJECTIVE

The aims of this study are to describe the characteristics of people who use digital health tools to address COVID-19-related concerns; explore their self-reported symptoms and characterize the association of these symptoms with COVID-19; and characterize the recommendations provided by digital health tools.

METHODS

This study used data from three digital health tools on the K Health app: a protocol-based COVID-19 self-assessment, an artificial intelligence (AI)-driven symptom checker, and communication with remote physicians. Deidentified data were extracted on the demographic and clinical characteristics of adults seeking COVID-19-related health information between April 8 and June 20, 2020. Analyses included exploring features associated with COVID-19 positivity and features associated with the choice to communicate with a remote physician.

RESULTS

During the period assessed, 71,619 individuals completed the COVID-19 self-assessment, 41,425 also used the AI-driven symptom checker, and 2523 consulted with remote physicians. Individuals who used the COVID-19 self-assessment were predominantly female (51,845/71,619, 72.4%), with a mean age of 34.5 years (SD 13.9). Testing for COVID-19 was reported by 2901 users, of whom 433 (14.9%) reported testing positive. Users who tested positive for COVID-19 were more likely to have reported loss of smell or taste (relative rate [RR] 6.66, 95% CI 5.53-7.94) and other established COVID-19 symptoms as well as ocular symptoms. Users communicating with a remote physician were more likely to have been recommended by the self-assessment to undergo immediate medical evaluation due to the presence of severe symptoms (RR 1.19, 95% CI 1.02-1.32). Most consultations with remote physicians (1940/2523, 76.9%) were resolved without need for referral to an in-person visit or to the emergency department.

CONCLUSIONS

Our results suggest that digital health tools can help support remote care and self-management of COVID-19 and that self-reported symptoms from digital interactions can extend our understanding of the symptoms associated with COVID-19.

Assessment of the Impact of Media Coverage on COVID-19-Related Google Trends Data: Infodemiology Study

Background

The influence of media coverage on web-based searches may hinder the role of Google Trends (GT) in monitoring coronavirus disease (COVID-19).

Objective

The aim of this study was to assess whether COVID-19–related GT data, particularly those related to ageusia and anosmia, were primarily related to media coverage or to epidemic trends.

Methods

We retrieved GT query data for searches on coronavirus, cough, anosmia, and ageusia and plotted them over a period of 5 years. In addition, we analyzed the trends of those queries for 17 countries throughout the year 2020 with a particular focus on the rises and peaks of the searches. For anosmia and ageusia, we assessed whether the respective GT data correlated with COVID-19 cases and deaths both throughout 2020 and specifically before March 16, 2020 (ie, the date when the media started reporting that these symptoms can be associated with COVID-19).

Results

Over the last five years, peaks for coronavirus searches in GT were only observed during the winter of 2020. Rises and peaks in coronavirus searches appeared at similar times in the 17 different assessed countries irrespective of their epidemic situations. In 15 of these countries, rises in anosmia and ageusia searches occurred in the same week or 1 week after they were identified in the media as symptoms of COVID-19. When data prior to March 16, 2020 were analyzed, anosmia and ageusia GT data were found to have variable correlations with COVID-19 cases and deaths in the different countries.

Conclusions

Our results indicate that COVID-19–related GT data are more closely related to media coverage than to epidemic trends.

The Role of Health Technology and Informatics in a Global Public Health Emergency: Practices and Implications From the COVID-19 Pandemic

At present, the coronavirus disease (COVID-19) is spreading around the world. It is a critical and important task to take thorough efforts to prevent and control the pandemic. Compared with severe acute respiratory syndrome and Middle East Respiratory Syndrome, COVID-19 spreads more rapidly owing to increased globalization, a longer incubation period, and unobvious symptoms. As the coronavirus has the characteristics of strong transmission and weak lethality, and since the large-scale increase of infected people may overwhelm health care systems, efforts are needed to treat critical patients, track and manage the health status of residents, and isolate suspected patients. The application of emerging health technologies and digital practices in health care, such as artificial intelligence, telemedicine or telehealth, mobile health, big data, 5G, and the Internet of Things, have become powerful "weapons" to fight against the pandemic and provide strong support in pandemic prevention and control. Applications and evaluations of all of these technologies, practices, and health delivery services are highlighted in this study.

Collateral Crises of Gun Preparation and the COVID-19 Pandemic: Infodemiology Study

Background

In the past, national emergencies in the United States have resulted in increased gun preparation (ie, purchasing new guns or removing guns from storage); in turn, these gun actions have effected increases in firearm injuries and deaths.

Objective

The aim of this paper was to assess the extent to which interest in gun preparation has increased amid the coronavirus disease (COVID-19) pandemic using data from Google searches related to purchasing and cleaning guns.

Methods

We fit an Autoregressive Integrated Moving Average (ARIMA) model over Google search data from January 2004 up to the week that US President Donald Trump declared COVID-19 a national emergency. We used this model to forecast Google search volumes, creating a counterfactual of the number of gun preparation searches we would expect if the COVID-19 pandemic had not occurred, and reported observed deviations from this counterfactual.

Results

Google searches related to preparing guns have surged to unprecedented levels, approximately 40% higher than previously reported spikes following the Sandy Hook, CT and Parkland, FL shootings and 158% (95% CI 73-270) greater than would be expected if the COVID-19 pandemic had not occurred. In absolute terms, approximately 2.1 million searches related to gun preparation were performed over just 34 days. States severely affected by COVID-19 appear to have some of the greatest increases in the number of searches.

Conclusions

Our results corroborate media reports that gun purchases are increasing amid the COVID-19 pandemic and provide more precise geographic and temporal trends. Policy makers should invest in disseminating evidence-based educational tools about gun risks and safety procedures to avert a collateral public health crisis.

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.

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

BACKGROUND

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

OBJECTIVE

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

The Use of Digital Health in the Detection and Management of COVID-19

Digital health is uniquely positioned to enhance the way we detect and manage infectious diseases. This commentary explores the potential of implementing digital technologies that can be used at different stages of the COVID-19 outbreak, including data-driven disease surveillance, screening, triage, diagnosis, and monitoring. Methods that could potentially reduce the exposure of healthcare providers to the virus are also discussed.

Tracking COVID-19 in Europe: Infodemiology Approach

BACKGROUND

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

OBJECTIVE

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Intelligence and global health: assessing the role of open source and social media intelligence analysis in infectious disease outbreaks

Purpose

Open Source Intelligence (OSINT) and Signals Intelligence (SIGINT) from the clandestine intelligence sector are being increasingly employed in infectious disease outbreaks. The purpose of this article is to explore how such tools might be employed in the detection, reporting, and control of outbreaks designated as a 'threat' by the global community. It is also intended to analyse previous use of such tools during the Ebola and SARS epidemics and to discuss key questions regarding the ethics and legality of initiatives that further blur the military and humanitarian spaces.

Methods

We undertake qualitative analysis of current discussions on OSINT and SIGINT and their intersection with global health. We also review current literature and describe the debates. We built on quantitative and qualitative research done into current health collection capabilities.

Results

This article presents an argument for the use of OSINT in the detection of infectious disease outbreaks and how this might occur.

Conclusion

We conclude that there is a place for OSINT and SIGINT in the detection and reporting of outbreaks. However, such tools are not sufficient on their own and must be corroborated for the intelligence to be relevant and actionable. Finally, we conclude that further discussion on key ethical issues needs to take place before such research can continue. In particular, this involves questions of jurisdiction, data ownership, and ethical considerations.

Health Information-Seeking Patterns of the General Public and Indications for Disease Surveillance: Register-Based Study Using Lyme Disease

Background

People using the Internet to find information on health issues, such as specific diseases, usually start their search from a general search engine, for example, Google. Internet searches such as these may yield results and data of questionable quality and reliability. Health Library is a free-of-charge medical portal on the Internet providing medical information for the general public. Physician’s Databases, an Internet evidence-based medicine source, provides medical information for health care professionals (HCPs) to support their clinical practice. Both databases are available throughout Finland, but the latter is used only by health professionals and pharmacies. Little is known about how the general public seeks medical information from medical sources on the Internet, how this behavior differs from HCPs’ queries, and what causes possible differences in behavior.

Objective

The aim of our study was to evaluate how the general public’s and HCPs’ information-seeking trends from Internet medical databases differ seasonally and temporally. In addition, we aimed to evaluate whether the general public’s information-seeking trends could be utilized for disease surveillance and whether media coverage could affect these seeking trends.

Methods

Lyme disease, serving as a well-defined disease model with distinct seasonal variation, was chosen as a case study. Two Internet medical databases, Health Library and Physician’s Databases, were used. We compared the general public’s article openings on Lyme disease from Health Library to HCPs’ article openings on Lyme disease from Physician’s Databases seasonally across Finland from 2011 to 2015. Additionally, media publications related to Lyme disease were searched from the largest and most popular media websites in Finland.

Results

Both databases, Health Library and Physician’s Databases, show visually similar patterns in temporal variations of article openings on Lyme disease in Finland from 2011 to 2015. However, Health Library openings show not only an increasing trend over time but also greater fluctuations, especially during peak opening seasons. Outside these seasons, publications in the media coincide with Health Library article openings only occasionally.

Conclusions

Lyme disease–related information-seeking behaviors between the general public and HCPs from Internet medical portals share similar temporal variations, which is consistent with the trend seen in epidemiological data. Therefore, the general public’s article openings could be used as a supplementary source of information for disease surveillance. The fluctuations in article openings appeared stronger among the general public, thus, suggesting that different factors such as media coverage, affect the information-seeking behaviors of the public versus professionals. However, media coverage may also have an influence on HCPs. Not every publication was associated with an increase in openings, but the higher the media coverage by some publications, the higher the general public’s access to Health Library.

Saúde na Copa: The World's First Application of Participatory Surveillance for a Mass Gathering at FIFA World Cup 2014, Brazil

BACKGROUND

The 2005 International Health Regulations (IHRs) established parameters for event assessments and notifications that may constitute public health emergencies of international concern. These requirements and parameters opened up space for the use of nonofficial mechanisms (such as websites, blogs, and social networks) and technological improvements of communication that can streamline the detection, monitoring, and response to health problems, and thus reduce damage caused by these problems. Specifically, the revised IHR created space for participatory surveillance to function, in addition to the traditional surveillance mechanisms of detection, monitoring, and response. Participatory surveillance is based on crowdsourcing methods that collect information from society and then return the collective knowledge gained from that information back to society. The spread of digital social networks and wiki-style knowledge platforms has created a very favorable environment for this model of production and social control of information.

OBJECTIVE

The aim of this study was to describe the use of a participatory surveillance app, Healthy Cup, for the early detection of acute disease outbreaks during the Fédération Internationale de Football Association (FIFA) World Cup 2014. Our focus was on three specific syndromes (respiratory, diarrheal, and rash) related to six diseases that were considered important in a mass gathering context (influenza, measles, rubella, cholera, acute diarrhea, and dengue fever).

METHODS

From May 12 to July 13, 2014, users from anywhere in the world were able to download the Healthy Cup app and record their health condition, reporting whether they were good, very good, ill, or very ill. For users that reported being ill or very ill, a screen with a list of 10 symptoms was displayed. Participatory surveillance allows for the real-time identification of aggregates of symptoms that indicate possible cases of infectious diseases.

RESULTS

From May 12 through July 13, 2014, there were 9434 downloads of the Healthy Cup app and 7155 (75.84%) registered users. Among the registered users, 4706 (4706/7155, 65.77%) were active users who posted a total of 47,879 times during the study period. The maximum number of users that signed up in one day occurred on May 30, 2014, the day that the app was officially launched by the Minister of Health during a press conference. During this event, the Minister of Health announced the special government program Health in the World Cup on national television media. On that date, 3633 logins were recorded, which accounted for more than half of all sign-ups across the entire duration of the study (50.78%, 3633/7155).

CONCLUSIONS

Participatory surveillance through community engagement is an innovative way to conduct epidemiological surveillance. Compared to traditional epidemiological surveillance, advantages include lower costs of data acquisition, timeliness of information collected and shared, platform scalability, and capacity for integration between the population being served and public health services.

Telehealth responses to bio-terrorism and emerging infections

Emerging infectious diseases, such as severe acute respiratory syndrome (SARS), are of huge economic importance. They are difficult to predict. The World Health Organization has a Global Outbreak Alert and Response Network, which was involved at an early stage in the SARS outbreak in 2003. Three major lessons were learned as a result of the SARS epidemic in 2003, involving communication, evidence-based action and global partnerships. It is proposed that a series of broadband global response networks should be developed. At a technical level the networks are essentially in place, such as the Internet2 global network. Suitable peripheral devices also exist. What has not yet been created is the appropriate software to allow the use of these networks, although a number of commercial products are in the process of development.

[Teleconferences for national surveillance of infectious diseases and public health events in Germany. Evaluation after three quarters in 2009]

Public health threats are increasingly triggered by events which span across international, national and state level jurisdictions. Innovative surveillance methods are needed to ensure adequate and timely response to such threats. In January 2009 the Department of Infectious Disease Epidemiology at the Robert Koch Institute (RKI) established a system of weekly telephone conferences with all competent authorities of the German federal states to identify, discuss and respond to infectious disease events in real-time. A regular and structured platform was developed for use between participants from state level public health authorities, the military and the RKI. During the first three quarters, 46 infectious diseases were covered, including mandatory reports of measles and meningococcal meningitis and outbreaks of cowpox, which does not have to be notified in Germany. Results of a targeted evaluation and a consistently high attendance rate both indicate that the teleconference has met additional needs for supplemental information exchange among participants. The telephone conference has proven to be a useful resource for rapid and direct communication, coordination and evaluation of signals for public health events in Germany.

What's unusual in online disease outbreak news?

BACKGROUND

Accurate and timely detection of public health events of international concern is necessary to help support risk assessment and response and save lives. Novel event-based methods that use the World Wide Web as a signal source offer potential to extend health surveillance into areas where traditional indicator networks are lacking. In this paper we address the issue of systematically evaluating online health news to support automatic alerting using daily disease-country counts text mined from real world data using BioCaster. For 18 data sets produced by BioCaster, we compare 5 aberration detection algorithms (EARS C2, C3, W2, F-statistic and EWMA) for performance against expert moderated ProMED-mail postings.

RESULTS

We report sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), mean alerts/100 days and F1, at 95% confidence interval (CI) for 287 ProMED-mail postings on 18 outbreaks across 14 countries over a 366 day period. Results indicate that W2 had the best F1 with a slight benefit for day of week effect over C2. In drill down analysis we indicate issues arising from the granular choice of country-level modeling, sudden drops in reporting due to day of week effects and reporting bias. Automatic alerting has been implemented in BioCaster available from http://born.nii.ac.jp.

CONCLUSIONS

Online health news alerts have the potential to enhance manual analytical methods by increasing throughput, timeliness and detection rates. Systematic evaluation of health news aberrations is necessary to push forward our understanding of the complex relationship between news report volumes and case numbers and to select the best performing features and algorithms.

Infodemiology and infoveillance: framework for an emerging set of public health informatics methods to analyze search, communication and publication behavior on the Internet

Infodemiology can be defined as the science of distribution and determinants of information in an electronic medium, specifically the Internet, or in a population, with the ultimate aim to inform public health and public policy. Infodemiology data can be collected and analyzed in near real time. Examples for infodemiology applications include: the analysis of queries from Internet search engines to predict disease outbreaks (eg. influenza); monitoring peoples' status updates on microblogs such as Twitter for syndromic surveillance; detecting and quantifying disparities in health information availability; identifying and monitoring of public health relevant publications on the Internet (eg. anti-vaccination sites, but also news articles or expert-curated outbreak reports); automated tools to measure information diffusion and knowledge translation, and tracking the effectiveness of health marketing campaigns. Moreover, analyzing how people search and navigate the Internet for health-related information, as well as how they communicate and share this information, can provide valuable insights into health-related behavior of populations. Seven years after the infodemiology concept was first introduced, this paper revisits the emerging fields of infodemiology and infoveillance and proposes an expanded framework, introducing some basic metrics such as information prevalence, concept occurrence ratios, and information incidence. The framework distinguishes supply-based applications (analyzing what is being published on the Internet, eg. on Web sites, newsgroups, blogs, microblogs and social media) from demand-based methods (search and navigation behavior), and further distinguishes passive from active infoveillance methods. Infodemiology metrics follow population health relevant events or predict them. Thus, these metrics and methods are potentially useful for public health practice and research, and should be further developed and standardized.

Surveillance Sans Frontières: Internet-based emerging infectious disease intelligence and the HealthMap project

John Brownstein and colleagues discuss HealthMap, an automated real-time system that monitors and disseminates online information about emerging infectious diseases.

HealthMap: global infectious disease monitoring through automated classification and visualization of Internet media reports

OBJECTIVE

Unstructured electronic information sources, such as news reports, are proving to be valuable inputs for public health surveillance. However, staying abreast of current disease outbreaks requires scouring a continually growing number of disparate news sources and alert services, resulting in information overload. Our objective is to address this challenge through the HealthMap.org Web application, an automated system for querying, filtering, integrating and visualizing unstructured reports on disease outbreaks.

DESIGN

This report describes the design principles, software architecture and implementation of HealthMap and discusses key challenges and future plans.

MEASUREMENTS

We describe the process by which HealthMap collects and integrates outbreak data from a variety of sources, including news media (e.g., Google News), expert-curated accounts (e.g., ProMED Mail), and validated official alerts. Through the use of text processing algorithms, the system classifies alerts by location and disease and then overlays them on an interactive geographic map. We measure the accuracy of the classification algorithms based on the level of human curation necessary to correct misclassifications, and examine geographic coverage.

RESULTS

As part of the evaluation of the system, we analyzed 778 reports with HealthMap, representing 87 disease categories and 89 countries. The automated classifier performed with 84% accuracy, demonstrating significant usefulness in managing the large volume of information processed by the system. Accuracy for ProMED alerts is 91% compared to Google News reports at 81%, as ProMED messages follow a more regular structure.

CONCLUSION

HealthMap is a useful free and open resource employing text-processing algorithms to identify important disease outbreak information through a user-friendly interface.

Developing Internet-Based eHealth Promotion Programs: The Spiral Technology Action Research (STAR) Model

Health education and health promotion have a tradition of using information and communication technology (ICT). In recent years, the rapid growth of the Internet has created innovative opportunities for Web-based health education and behavior change applications—termed eHealth promotion. However, many eHealth promotion applications are developed without an explicit model to guide the design, evaluation, and ongoing improvement of the program. The spiral technology action research (STAR) model was developed to address this need. The model comprises five cycles (listen, plan, do, study, act) that weave together technological development, community involvement, and continuous improvement. The model is illustrated by a case study describing the development of the Smoking Zine (www.SmokingZine.org), a youth smoking prevention and cessation Web site.

Infodemiology: tracking flu-related searches on the web for syndromic surveillance

BACKGROUND

Syndromic surveillance uses health-related data that precede diagnosis and signal a sufficient probability of a case or an outbreak to warrant further public health response.

OBJECTIVE

While most syndromic surveillance systems rely on data from clinical encounters with health professionals, I started to explore in 2004 whether analysis of trends in Internet searches can be useful to predict outbreaks such as influenza epidemics and prospectively gathered data on Internet search trends for this purpose.

RESULTS

There is an excellent correlation between the number of clicks on a keyword-triggered link in Google with epidemiological data from the flu season 2004/2005 in Canada (Pearson correlation coefficient of current week clicks with the following week influenza cases r=.91). The "Google ad sentinel method" proved to be more timely, more accurate and - with a total cost of Can$365.64 for the entire flu-season - considerably cheaper than the traditional method of reports on influenza-like illnesses observed in clinics by sentinel physicians.

CONCLUSION

Systematically collecting and analyzing health information demand data from the Internet has considerable potential to be used for syndromic surveillance. Tracking web searches on the Internet has the potential to predict population-based events relevant for public health purposes, such as real outbreaks, but may also be confounded by "epidemics of fear". Data from such "infodemiology studies" should also include longitudinal data on health information supply.

What Internet services would patients like from hospitals during an epidemic? Lessons from the SARS outbreak in Toronto

BACKGROUND

International health organizations and officials are bracing for a pandemic. Although the 2003 severe acute respiratory syndrome (SARS) outbreak in Toronto did not reach such a level, it created a unique opportunity to identify the optimal use of the Internet to promote communication with the public and to preserve health services during an epidemic.

OBJECTIVE

The aim of the study was to explore patients' attitudes regarding the health services that might be provided through the Internet to supplement those traditionally available in the event of a future mass emergency situation.

METHODS

We conducted "mask-to-mask" surveys of patients at three major teaching hospitals in Toronto during the second outbreak of SARS. Patients were surveyed at the hospital entrances and selected clinics. Descriptive statistics and logistic regression models were used for the analysis.

RESULTS

In total, 1019 of 1130 patients responded to the survey (90% overall response rate). With respect to Internet use, 70% (711/1019) used the Internet by themselves and 57% (578/1019) with the help of a friend or family member. Of the Internet users, 68% (485/711) had already searched the World Wide Web for health information, and 75% (533/711) were interested in communicating with health professionals using the Internet as part of their ongoing care. Internet users expressed interest in using the Web for the following reasons: to learn about their health condition through patient education materials (84%), to obtain information about the status of their clinic appointments (83%), to send feedback to the hospital about how to improve its services (77%), to access screening tools to help determine if they were potentially affected by the infectious agent responsible for the outbreak (77%), to renew prescriptions (75%), to consult with their health professional about nonurgent matters (75%), and to access laboratory test results (75%). Regression results showed that younger age, higher education, and English as a first language were predictors of patients' interest in using Internet services in the event of an epidemic.

CONCLUSION

Most patients are willing and able to use the Internet as a means to maintain communication with the hospital during an outbreak of an infectious disease such as SARS. Hospitals should explore new ways to interact with the public, to provide relevant health information, and to ensure continuity of care when they are forced to restrict their services.

Cancer Internet search activity on a major search engine, United States 2001-2003

Background

To locate online health information, Internet users typically use a search engine, such as Yahoo! or Google. We studied Yahoo! search activity related to the 23 most common cancers in the United States.

Objective

The objective was to test three potential correlates of Yahoo! cancer search activity—estimated cancer incidence, estimated cancer mortality, and the volume of cancer news coverage—and to study the periodicity of and peaks in Yahoo! cancer search activity.

Methods

Yahoo! cancer search activity was obtained from a proprietary database called the Yahoo! Buzz Index. The American Cancer Society's estimates of cancer incidence and mortality were used. News reports associated with specific cancer types were identified using the LexisNexis “US News” database, which includes more than 400 national and regional newspapers and a variety of newswire services.

Results

The Yahoo! search activity associated with specific cancers correlated with their estimated incidence (Spearman rank correlation, ρ = 0.50, P = .015), estimated mortality (ρ = 0.66, P = .001), and volume of related news coverage (ρ = 0.88, P < .001). Yahoo! cancer search activity tended to be higher on weekdays and during national cancer awareness months but lower during summer months; cancer news coverage also tended to follow these trends. Sharp increases in Yahoo! search activity scores from one day to the next appeared to be associated with increases in relevant news coverage.

Conclusions

Media coverage appears to play a powerful role in prompting online searches for cancer information. Internet search activity offers an innovative tool for passive surveillance of health information–seeking behavior.

Creating a framework for online cancer services research to facilitate timely and interdisciplinary applications

Researchers from a wide array of disciplines have conducted engaging and informative studies in recent years concerning the use of the Internet for cancer-related services. Typically, these publications provide key data related to utilization statistics, how online information can be used, what users want or expect from the Internet, outcomes or impacts, and quality and credibility of websites. These are important themes for understanding online cancer issues. However, this special issue of the Journal of Medical Internet Research seeks to recast these themes in a way that will facilitate pragmatic and applied means of employing data in prescriptive and interdisciplinary ways. This issue includes 14 papers that exemplify applications for the research framework recommended in this paper. This framework includes an expanded focus on the development and design of online cancer services, online consumer behavior/communication, behavior change, and living with cancer.

Estimation of the impact of providing outpatients with information about SARS infection control on their intention of outpatient visit

To examine the effect of provision of information about the infection control in the specific infection disease treatment unit in a city hospital on the outpatient’s intention of outpatient service use, respondents who underwent outpatient medical care at the hospital (N = 821) were asked whether or not they intended to continue the outpatient visit at the hospital if a severe acute respiratory syndrome (SARS) patient was admitted to the unit. Although 56% of respondents replied that they could continue to visit the department if a SARS patient was admitted to the unit in the hospital before they read the information, the proportion of those who intended to continue outpatient care significantly increased by 15% after they read it. The logistic regression analyses revealed that respondents who had frequently visited the outpatient department (P < 0.001), those who felt relieved by reading the information about the unit (P < 0.001), and those who did not worry about nosocomial SARS infection inside the hospital (P < 0.001) were significantly more likely to reply that they would continue outpatient visits. We estimated that admission of a SARS patient to the unit would result in a 20% decrease in the cumulative total number of outpatients in the hospital during a 180-day interval after admission of a SARS patient to the unit, and the cumulative total number of outpatients increased by 7% after they read the information. This study suggests that providing outpatients with appropriate information about SARS infection control in the hospital had a statistically significant and substantial impact on the outpatients’ intention to continue outpatient visits at the hospital.