Monitoring Interest in Herpes Zoster Vaccination: Analysis of Google Search Data

Public interest trends for COVID-19 and pandemic trajectory: A time-series analysis of US state-level data

Google Trends provides spatiotemporal data for user-specific terms scaled from less than 1 (lowest relative popularity) to 100 (highest relative popularity) as a proxy for the public interest. Here we use US state-level data for COVID-19 to examine popularity trends during the pandemic evolution. We used "coronavirus" and "covid" search terms and set the period up from January 1st, 2020, to November 12, 2022. We measured the agreement on web rankings between states using the nonparametric Kendall's W (0 for no concordance to 1 for perfect agreement). We compiled state-level weekly data on COVID-19 incidence and mortality and scaled state curves from 0 to 100 through a min-max normalization process. We used a dynamic time-warping algorithm to calculate similarities between the popularity, mortality, and incidence of COVID-19. The methodology is a pattern recognition process between time series by distance optimization. The similarity was mapped from 0 to 1, with 1 indicating perfect similarity and 0 indicating no similarity. The peak in popularity was in March 2020, succeeded by a decline and a prolonged period of fluctuation around 20%. Public interest rose briefly at the end of 2021, to fall to a low activity of around 10%. This pattern was remarkably consistent across states (Kendal's W 0.94, p < 0.001). Web search trends were an impression of contagion growth: Overall, popularity-mortality trajectories yielded higher similarity indices (median 0.78; interquartile range 0.75-0.82) compared to popularity-incidence trajectories (median 0.74; interquartile range 0.72-0.76, Wilcoxon's exact p<0.001). The popularity-mortality trajectories had a very strong similarity (>0.80) in 19/51 (37%) regions, as opposed to only 4/51 (8%) for popularity-incidence trajectories. State-level data show a fading public concern about COVID-19, and web-search popularity patterns may reflect the COVID-19 trajectory in terms of cases and mortality.

Public interest trends for Covid-19 and alignment with the disease trajectory: A time-series analysis of national-level data

Data from web search engines have become a valuable adjunct in epidemiology and public health, specifically during epidemics. We aimed to explore the concordance of web search popularity for Covid-19 across 6 Western nations (United Kingdom, United States, France, Italy, Spain and Germany) and how timeline changes align with the pandemic waves, Covid-19 mortality, and incident case trajectories. We used the Google Trends tool for web-search popularity, and "Our World in Data" on Covid-19 reported cases, deaths, and administrative responses (measured by stringency index) to analyze country-level data. The Google Trends tool provides spatiotemporal data, scaled to a range of <1 (lowest relative popularity) to 100 (highest relative popularity), for the selected search terms, timeframe, and region. We used "coronavirus" and "covid" as search terms and set the timeframe up to November 12, 2022. We obtained multiple consecutive samples using the same terms to validate against sampling bias. We consolidated national-level incident cases and deaths weekly and transformed them to a range between 0 to 100 through the min-max normalization algorithm. We calculated the concordance of relative popularity rankings between regions, using the non-parametric Kendall's W, which maps concordance between 0 (lack of agreement) to 1 (perfect match). We used a dynamic time-warping algorithm to explore the similarity between Covid-19 relative popularity, mortality, and incident case trajectories. This methodology can recognize the similarity of shapes between time-series through a distance optimization process. The peak popularity was recorded on March 2020, to be followed by a decline below 20% in the subsequent three months and a long-standing period of variation around that level. At the end of 2021, public interest spiked shortly to fade away to a low level of around 10%. This pattern was highly concordant across the six regions (Kendal's W 0.88, p< .001). In dynamic time warping analysis, national-level public interest yielded a high similarity with the Covid-19 mortality trajectory (Similarity indices range 0.60-0.79). Instead, public interest was less similar with incident cases (0.50-0.76) and stringency index trajectories (0.33-0.64). We demonstrated that public interest is better intertwined with population mortality, rather than incident case trajectory and administrative responses. As the public interest in Covid-19 gradually subsides, these observations could help predict future public interest in pandemic events.

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.

Willingness to Vaccinate Against Herpes Zoster and Its Associated Factors Across WHO Regions: Global Systematic Review and Meta-Analysis

BACKGROUND

A life-course immunization approach would enhance the quality of life across all age groups and improve societal well-being. The herpes zoster (HZ) vaccine is highly recommended for older adults to prevent HZ infection and related complications. The proportions of willingness to receive the HZ vaccine varies across countries, and various kinds of factors, including sociodemographics and individual perceptions, influence the willingness to vaccinate.

OBJECTIVE

We aim to estimate the HZ vaccination willingness rate and identify factors associated with vaccine uptake willingness across all World Health Organization (WHO) regions.

METHODS

A global systematic search was performed on PubMed, Web of Science, and the Cochrane Library for all papers related to the HZ vaccine published until June 20, 2022. Study characteristics were extracted for each included study. Using double arcsine transformation, vaccination willingness rates with 95% CIs were pooled and reported. The willingness rate and associated factors were analyzed by geographical context. Associated factors were also summarized based on Health Belief Model (HBM) constructs.

RESULTS

Of the 26,942 identified records, 13 (0.05%) papers were included, covering 14,066 individuals from 8 countries in 4 WHO regions (Eastern Mediterranean Region, European Region, Region of the Americas, and Western Pacific Region). The pooled vaccination willingness rate was 55.74% (95% CI 40.85%-70.13%). Of adults aged ≥50 years, 56.06% were willing to receive the HZ vaccine. After receiving health care workers' (HCWs) recommendations, 75.19% of individuals were willing to get the HZ vaccine; without HCWs' recommendations, the willingness rate was only 49.39%. The willingness rate was more than 70% in the Eastern Mediterranean Region and approximately 55% in the Western Pacific Region. The willingness rate was the highest in the United Arab Emirates and the lowest in China and the United Kingdom. The perception of HZ severity and susceptibility was positively associated with vaccination willingness. The perceived barriers to vaccination willingness (main reasons for unwillingness) included low trust in the effectiveness of the HZ vaccine, concerns about safety, financial concerns, and being unaware of the HZ vaccine's availability. Older individuals, those having lower education, or those having lower income levels were less likely to willing to be vaccinated.

CONCLUSIONS

Only 1 in 2 individuals showed a willingness to be vaccinated against HZ. The willingness rate was the highest in the Eastern Mediterranean Region. Our findings show the critical role HCWs play in promoting HZ vaccination. Monitoring HZ vaccination willingness is necessary to inform public health decision-making. These findings provide critical insights for designing future life-course immunization programs.

A Google Trends Approach to Identify Distinct Diurnal and Day-of-Week Web-Based Search Patterns Related to Conjunctivitis and Other Common Eye Conditions: Infodemiology Study

BACKGROUND

Studies suggest diurnal patterns of occurrence of some eye conditions. Leveraging new information sources such as web-based search data to learn more about such patterns could improve the understanding of patients' eye-related conditions and well-being, better inform timing of clinical and remote eye care, and improve precision when targeting web-based public health campaigns toward underserved populations.

OBJECTIVE

To investigate our hypothesis that the public is likely to consistently search about different ophthalmologic conditions at different hours of the day or days of week, we conducted an observational study using search data for terms related to ophthalmologic conditions such as conjunctivitis. We assessed whether search volumes reflected diurnal or day-of-week patterns and if those patterns were distinct from each other.

METHODS

We designed a study to analyze and compare hourly search data for eye-related and control search terms, using time series regression models with trend and periodicity terms to remove outliers and then estimate diurnal effects. We planned a Google Trends setting, extracting data from 10 US states for the entire year of 2018. The exposure was internet search, and the participants were populations who searched through Google's search engine using our chosen study terms. Our main outcome measures included cyclical hourly and day-of-week web-based search patterns. For statistical analyses, we considered P<.001 to be statistically significant.

RESULTS

Distinct diurnal (P<.001 for all search terms) and day-of-week search patterns for eye-related terms were observed but with differing peak time periods and cyclic strengths. Some diurnal patterns represented those reported from prior clinical studies. Of the eye-related terms, "pink eye" showed the largest diurnal amplitude-to-mean ratios. Stronger signal was restricted to and peaked in mornings, and amplitude was higher on weekdays. By contrast, "dry eyes" had a higher amplitude diurnal pattern on weekends, with stronger signal occurring over a broader evening-to-morning period and peaking in early morning.

CONCLUSIONS

The frequency of web-based searches for various eye conditions can show cyclic patterns according to time of the day or week. Further studies to understand the reasons for these variations may help supplement the current clinical understanding of ophthalmologic symptom presentation and improve the timeliness of patient messaging and care interventions.

Decreased online hepatitis information seeking during the COVID-19 pandemic: an Infodemiology study

Introduction

Viral hepatitis remains a public health concern worldwide, mainly in developing countries. The public's awareness and interest in viral hepatitis information are essential in preventing and controlling this disease. Infodemiology has been used as a surrogate to assess the general understanding of disease and measure public awareness of health topics. However, this analysis has not been applied to viral hepatitis. Thus, this study investigated the online global search interest for viral hepatitis in the last decade, focusing on the period before and during the COVID-19 pandemic.

Methods

Global online search interest for hepatitis was measured using the Google Trends™ database. Spearman's rank-order correlation correlated country-specific characteristics and prevalence data with search volume index.

Results

There was a significant reduction in online search interest for hepatitis during the COVID-19 pandemic (2020). People searching for hepatitis are also interested in hepatitis vaccination. Search volume index is positively correlated with viral hepatitis and HIV prevalence and negatively correlated with GDP. This correlation mirrors the high burden of viral hepatitis in developing countries and their citizens' desire to be informed about this disease.

Conclusions

Our study found decreased global online interest in viral hepatitis during the pandemic. Moreover, higher online interest in hepatitis was observed in countries with a lower gross domestic product and high viral hepatitis and HIV prevalence. We demonstrated that global online interest toward viral hepatitis could be assessed through the infodemiologic approach using Google Trends™.

Sustained Reductions in Online Search Interest for Communicable Eye and Other Conditions During the COVID-19 Pandemic: Infodemiology Study

Background

In a prior study at the start of the pandemic, we reported reduced numbers of Google searches for the term “conjunctivitis” in the United States in March and April 2020 compared with prior years. As one explanation, we conjectured that reduced information-seeking may have resulted from social distancing reducing contagious conjunctivitis cases. Here, after 1 year of continued implementation of social distancing, we asked if there have been persistent reductions in searches for “conjunctivitis,” and similarly for other communicable disease terms, compared to control terms.

Objective

The aim of this study was to determine if reduction in searches in the United States for terms related to conjunctivitis and other common communicable diseases occurred in the spring-winter season of the COVID-19 pandemic, and to compare this outcome to searches for terms representing noncommunicable conditions, COVID-19, and to seasonality.

Methods

Weekly relative search frequency volume data from Google Trends for 68 search terms in English for the United States were obtained for the weeks of March 2011 through February 2021. Terms were classified a priori as 16 terms related to COVID-19, 29 terms representing communicable conditions, and 23 terms representing control noncommunicable conditions. To reduce bias, all analyses were performed while masked to term names, classifications, and locations. To test for the significance of changes during the pandemic, we detrended and compared postpandemic values to those expected based on prepandemic trends, per season, computing one- and two-sided P values. We then compared these P values between term groups using Wilcoxon rank-sum and Fisher exact tests to assess if non-COVID-19 terms representing communicable diseases were more likely to show significant reductions in searches in 2020-2021 than terms not representing such diseases. We also assessed any relationship between a term’s seasonality and a reduced search trend for the term in 2020-2021 seasons. P values were subjected to false discovery rate correction prior to reporting. Data were then unmasked.

Results

Terms representing conjunctivitis and other communicable conditions showed a sustained reduced search trend in the first 4 seasons of the 2020-2021 COVID-19 pandemic compared to prior years. In comparison, the search for noncommunicable condition terms was significantly less reduced (Wilcoxon and Fisher exact tests, P<.001; summer, autumn, winter). A significant correlation was also found between reduced search for a term in 2020-2021 and seasonality of that term (Theil-Sen, P<.001; summer, autumn, winter). Searches for COVID-19–related conditions were significantly elevated compared to those in prior years, and searches for influenza-related terms were significantly lower than those for prior years in winter 2020-2021 (P<.001).

Conclusions

We demonstrate the low-cost and unbiased use of online search data to study how a wide range of conditions may be affected by large-scale interventions or events such as social distancing during the COVID-19 pandemic. Our findings support emerging clinical evidence implicating social distancing and the COVID-19 pandemic in the reduction of communicable disease and on ocular conditions.

Estimated Public Health Impact of the Recombinant Zoster Vaccine

OBJECTIVE

To investigate the potential public health impact of adult herpes zoster (HZ) vaccination with the adjuvanted recombinant zoster vaccine (RZV) in the United States in the first 15 years after launch.

METHODS

We used a publicly available model accounting for national population characteristics and HZ epidemiological data, vaccine characteristics from clinical studies, and anticipated vaccine coverage with RZV after launch in 2018. Two scenarios were modeled: a scenario with RZV implemented with 65% coverage after 15 years and a scenario continuing with zoster vaccine live (ZVL) with coverage increasing 10% over the same period. We estimated the numbers vaccinated, and the clinical outcomes and health care use avoided yearly, from January 1, 2018, to December 31, 2032. We varied RZV coverage and investigated the associated impact on HZ cases, complications, and health care resource use.

RESULTS

With RZV adoption, the numbers of individuals affected by HZ was predicted to progressively decline with an additional 4.6 million cumulative cases avoided if 65% vaccination with RZV was reached within 15 years. In the year 2032, it was predicted that an additional 1.3 million physicians' visits and 14.4 thousand hospitalizations could be avoided, compared with continuing with ZVL alone. These numbers could be reached 2 to 5 years earlier with 15% higher RZV vaccination rates.

CONCLUSION

Substantial personal and health care burden can be alleviated when vaccination with RZV is adopted. The predicted numbers of HZ cases, complications, physicians' visits, and hospitalizations avoided, compared with continued ZVL vaccination, depends upon the RZV vaccination coverage achieved.

Vaccination for quality of life: herpes-zoster vaccines

Current vaccination policy in most high-income countries aims to counteract the decline in cell-mediated immunity to varicella zoster virus that occurs with advancing age or immunosuppression. The aim of this review was to describe the burden of illness associated with herpes zoster (HZ) and post-herpetic neuralgia (PHN) risks and their impact on the social and common life in infected people. The effectiveness/efficacy and cost effectiveness of the immunization strategy will be presented through the review of the literature relevant to the live attenuated HZ vaccine (ZLV) licensed in 2006 and the recombinant HZ vaccine (RZV). The latter has very recently been approved to protect aged people aged ≥ 50 years against HZ morbidity including its complications, and associated health-care costs. Finally, this review also provides data with respect of precautions of using and safety of ZVL and RVZ.

Using Application Programming Interfaces to Access Google Data for Health Research: Protocol for a Methodological Framework

Background

Individuals are increasingly turning to search engines like Google to obtain health information and access resources. Analysis of Google search queries offers a novel approach, which is part of the methodological toolkit for infodemiology or infoveillance researchers, to understanding population health concerns and needs in real time or near-real time. While searches predominantly have been examined with the Google Trends website tool, newer application programming interfaces (APIs) are now available to academics to draw a richer landscape of searches. These APIs allow users to write code in languages like Python to retrieve sample data directly from Google servers.

Objective

The purpose of this paper is to describe a novel protocol to determine the top queries, volume of queries, and the top sites reached by a population searching on the web for a specific health term. The protocol retrieves Google search data obtained from three Google APIs: Google Trends, Google Health Trends (also referred to as Flu Trends), and Google Custom Search.

Methods

Our protocol consisted of four steps: (1) developing a master list of top search queries for an initial search term using Google Trends, (2) gathering information on relative search volume using Google Health Trends, (3) determining the most popular sites using Google Custom Search, and (4) calculating estimated total search volume. We tested the protocol following key procedures at each step and verified its usefulness by examining search traffic on birth control in 2017 in the United States. Two separate programmers working independently achieved similar results with insignificant variation due to sample variability.

Results

We successfully tested the methodology on the initial search term birth control. We identified top search queries for birth control, of which birth control pill was the most popular and obtained the relative and estimated total search volume for the top queries: relative search volume was 0.54 for the pill, corresponding to an estimated 9.3-10.7 million searches. We used the estimates of the proportion of search activity for the top queries to arrive at a generated list of the most popular websites: for the pill, the Planned Parenthood website was the top site.

Conclusions

The proposed methodological framework demonstrates how to retrieve Google query data from multiple Google APIs and provides thorough documentation required to systematically identify search queries and websites, as well as estimate relative and total search volume of queries in real time or near-real time in specific locations and time periods. Although the protocol needs further testing, it allows researchers to replicate the steps and shows promise in advancing our understanding of population-level health concerns.

International Registered Report Identifier (IRRID)

RR1-10.2196/16543

Web search popularity, publicity, and utilization of direct oral anticoagulants in the United States, 2008-2018: A STROBE-compliant study

We aimed to study the changing popularity of oral anticoagulants and the potential association between media coverage and real-world utilization practice, using time series analysis.In this STROBE-compliant study, we used Google Trends data to study public interest for direct oral anticoagulants (DOACs) (dabigatran, rivaroxaban, apixaban, and edoxaban) and warfarin in the United States (10-year coverage, beginning July 1st, 2008 ending June 30th, 2018). We validated our findings on a sample of 50 consecutive datasets (accumulated between July 6th, 2018 and October 19th, 2018), using the same search criteria. We used the LexisNexis Academic database to quantify monthly media coverage for DOACs and explored its association with interest by the public, using the cross-correlation coefficient function. Finally, we studied the association between public interest and real-world utilization data, including published US-wide data on ambulatory anticoagulation visits.The approval of dabigatran in 2010 marked an increasing public interest for DOACs. Dabigatran exhibited a steep rise early after Food and Drug Administration approval that peaks in 2011, to be surpassed sequentially by rivaroxaban (2012) and apixaban (2014). Apixaban has outperformed its competitors in popularity since mid-2017, and, by the end of the observation period, was close to warfarin that is on first place. Media coverage was low before approval of the first oral DOAC (dabigatran), increased thereafter (median 13 news articles per month vs 64, P < .001), with peaks on the approval dates (81 vs 48, P = .003). Media coverage had a weak immediate impact on DOACs public interest and public interest patterns preceded changes in ambulatory anticoagulation visits by up to 5 months.For a long-run observation period, a single Google Trends search will suffice to produce robust estimations of the relative popularity between treatment options, such as oral anticoagulants. Media coverage has limited immediate impact and relative public interest is a potential lead indicator of changes in actual utilization.

Infodemiology and Infoveillance: Scoping Review

BACKGROUND

Web-based sources are increasingly employed in the analysis, detection, and forecasting of diseases and epidemics, and in predicting human behavior toward several health topics. This use of the internet has come to be known as infodemiology, a concept introduced by Gunther Eysenbach. Infodemiology and infoveillance studies use web-based data and have become an integral part of health informatics research over the past decade.

OBJECTIVE

The aim of this paper is to provide a scoping review of the state-of-the-art in infodemiology along with the background and history of the concept, to identify sources and health categories and topics, to elaborate on the validity of the employed methods, and to discuss the gaps identified in current research.

METHODS

The PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines were followed to extract the publications that fall under the umbrella of infodemiology and infoveillance from the JMIR, PubMed, and Scopus databases. A total of 338 documents were extracted for assessment.

RESULTS

Of the 338 studies, the vast majority (n=282, 83.4%) were published with JMIR Publications. The Journal of Medical Internet Research features almost half of the publications (n=168, 49.7%), and JMIR Public Health and Surveillance has more than one-fifth of the examined studies (n=74, 21.9%). The interest in the subject has been increasing every year, with 2018 featuring more than one-fourth of the total publications (n=89, 26.3%), and the publications in 2017 and 2018 combined accounted for more than half (n=171, 50.6%) of the total number of publications in the last decade. The most popular source was Twitter with 45.0% (n=152), followed by Google with 24.6% (n=83), websites and platforms with 13.9% (n=47), blogs and forums with 10.1% (n=34), Facebook with 8.9% (n=30), and other search engines with 5.6% (n=19). As for the subjects examined, conditions and diseases with 17.2% (n=58) and epidemics and outbreaks with 15.7% (n=53) were the most popular categories identified in this review, followed by health care (n=39, 11.5%), drugs (n=40, 10.4%), and smoking and alcohol (n=29, 8.6%).

CONCLUSIONS

The field of infodemiology is becoming increasingly popular, employing innovative methods and approaches for health assessment. The use of web-based sources, which provide us with information that would not be accessible otherwise and tackles the issues arising from the time-consuming traditional methods, shows that infodemiology plays an important role in health informatics research.

Google Medical Update: Why Is the Search Engine Decreasing Visibility of Health and Medical Information Websites?

The Google search engine answers many health and medical information queries every day. People have become used to searching for this type of information. This paper presents a study which examined the visibility of health and medical information websites. The purpose of this study was to find out why Google is decreasing the visibility of such websites and how to measure this decrease. Since August 2018, Google has been more rigorously rating these websites, since they can potentially impact people’s health. The method of the study was to collect data about the visibility of health and medical information websites in sequential time snapshots. Visibility consists of combined data of unique keywords, positions, and URL results. The sample under study was made up of 21 websites selected from 10 European countries. The findings reveal that in sequential time snapshots, search visibility decreased. The decrease was not dependent on the country or the language. The main reason why Google is decreasing the visibility of such websites is that they do not meet high ranking criteria.

Google Trends in Infodemiology and Infoveillance: Methodology Framework

Internet data are being increasingly integrated into health informatics research and are becoming a useful tool for exploring human behavior. The most popular tool for examining online behavior is Google Trends, an open tool that provides information on trends and the variations of online interest in selected keywords and topics over time. Online search traffic data from Google have been shown to be useful in analyzing human behavior toward health topics and in predicting disease occurrence and outbreaks. Despite the large number of Google Trends studies during the last decade, the literature on the subject lacks a specific methodology framework. This article aims at providing an overview of the tool and data and at presenting the first methodology framework in using Google Trends in infodemiology and infoveillance, including the main factors that need to be taken into account for a strong methodology base. We provide a step-by-step guide for the methodology that needs to be followed when using Google Trends and the essential aspects required for valid results in this line of research. At first, an overview of the tool and the data are presented, followed by an analysis of the key methodological points for ensuring the validity of the results, which include selecting the appropriate keyword(s), region(s), period, and category. Overall, this article presents and analyzes the key points that need to be considered to achieve a strong methodological basis for using Google Trends data, which is crucial for ensuring the value and validity of the results, as the analysis of online queries is extensively integrated in health research in the big data era.

Social Media Surveillance for Outbreak Projection via Transmission Models: Longitudinal Observational Study

Background

Although dynamic models are increasingly used by decision makers as a source of insight to guide interventions in order to control communicable disease outbreaks, such models have long suffered from a risk of rapid obsolescence due to failure to keep updated with emerging epidemiological evidence. The application of statistical filtering algorithms to high-velocity data streams has recently demonstrated effectiveness in allowing such models to be automatically regrounded by each new set of incoming observations. The attractiveness of such techniques has been enhanced by the emergence of a new generation of geospatially specific, high-velocity data sources, including daily counts of relevant searches and social media posts. The information available in such electronic data sources complements that of traditional epidemiological data sources.

Objective

This study aims to evaluate the degree to which the predictive accuracy of pandemic projection models regrounded via machine learning in daily clinical data can be enhanced by extending such methods to leverage daily search counts.

Methods

We combined a previously published influenza A (H1N1) pandemic projection model with the sequential Monte Carlo technique of particle filtering, to reground the model bu using confirmed incident case counts and search volumes. The effectiveness of particle filtering was evaluated using a norm discrepancy metric via predictive and dataset-specific cross-validation.

Results

Our results suggested that despite the data quality limitations of daily search volume data, the predictive accuracy of dynamic models can be strongly elevated by inclusion of such data in filtering methods.

Conclusions

The predictive accuracy of dynamic models can be notably enhanced by tapping a readily accessible, publicly available, high-velocity data source. This work highlights a low-cost, low-burden avenue for strengthening model-based outbreak intervention response planning using low-cost public electronic datasets.

Seasonality Patterns of Internet Searches on Mental Health: Exploratory Infodemiology Study

BACKGROUND

The study of seasonal patterns of public interest in psychiatric disorders has important theoretical and practical implications for service planning and delivery. The recent explosion of internet searches suggests that mining search databases yields unique information on public interest in mental health disorders, which is a significantly more affordable approach than population health studies.

OBJECTIVE

This study aimed to investigate seasonal patterns of internet mental health queries in Ontario, Canada.

METHODS

Weekly data on health queries in Ontario from Google Trends were downloaded for a 5-year period (2012-2017) for the terms "schizophrenia," "autism," "bipolar," "depression," "anxiety," "OCD" (obsessive-compulsive disorder), and "suicide." Control terms were overall search results for the terms "health" and "how." Time-series analyses using a continuous wavelet transform were performed to isolate seasonal components in the search volume for each term.

RESULTS

All mental health queries showed significant seasonal patterns with peak periodicity occurring over the winter months and troughs occurring during summer, except for "suicide." The comparison term "health" also exhibited seasonal periodicity, while the term "how" did not, indicating that general information seeking may not follow a seasonal trend in the way that mental health information seeking does.

CONCLUSIONS

Seasonal patterns of internet search volume in a wide range of mental health terms were observed, with the exception of "suicide." Our study demonstrates that monitoring internet search trends is an affordable, instantaneous, and naturalistic method to sample public interest in large populations and inform health policy planners.

Google Searches and Detection of Conjunctivitis Epidemics Worldwide

PURPOSE

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

DESIGN

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

PARTICIPANTS

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

MAIN OUTCOME MEASURES

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

RESULTS

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

CONCLUSIONS

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