Monitoring public interest toward pertussis outbreaks: an extensive Google Trends-based analysis

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Using Google Trends and Wikipedia to Investigate the Global Public's Interest in the Pancreatic Cancer Diagnosis of a Celebrity

A cross-sectional study was designed to assess the impact of a celebrity's announcement of having been diagnosed with pancreatic cancer on the volume of cancer-related research on the Internet. Global searches were carried out on Google Trends (GT) for the period from 1 January 2004 to 20 November 2022 (since data prior to 2004 were not available) using the search words Tumore del Pancreas (pancreatic cancer), Tumore neuroendocrino (neuroendocrine tumor), and Fedez (the name of a popular Italian rapper). The frequency of specific page views for Fedez, Tumore del pancreas, and Tumore neuroendocrino was collected via Wikipedia Trends data. Statistical analyses were carried out using the Pearson correlation coefficient (r). The GT data revealed a strong correlation (r = 0.83) while the Wikipedia Trends data indicated a moderate correlation (r = 0.37) for Tumore neuroendocrino and Tumore del pancreas. The search peaks for the GT and Wikipedia pages occur during the same time period. An association was found between the celebrity's announcement of his pancreatic cancer diagnosis and the volume of pancreatic-cancer-related online searches. Our findings demonstrate that media events and media coverage of health-related news can raise people's curiosity and desire for health information.

Infodemiology of RSV in Italy (2017-2022): An Alternative Option for the Surveillance of Incident Cases in Pediatric Age?

The aim of this study was to evaluate whether or not online queries for Respiratory Syncytial Virus (RSV) retrieved by means of Google Trends™ and the Italian Wikipedia analysis program mirror the occurrence of influenza-like illnesses (ILI), as reported by the Italian Influenza Surveillance network (InfluNet). Estimated rates for ILI in the general population and in the age groups 0−4 years and 5−14 years were obtained for the influenza seasons 2017−2018 to 2020−2021. Similarly, a weekly fraction of online searches was retrieved for a series of terms associated with Respiratory Syncytial Virus. Next, trends for daily visualization of Italian Wikipedia Pages for Human Respiratory Syncytial Virus, Pneumonia, Bronchiolitis, Influenza, and Respiratory Failure were similarly retrieved. The correlation of all search terms with ILI was analyzed by means of Spearman’s rank correlation analysis. Among search terms associated with the clinical diagnosis of Respiratory Syncytial Virus infections, the occurrence of ILI was highly correlated only with Bronchiolitis in the age group 0−4 years (β 0.210, p = 0.028), while more generic search terms, such as Bronchitis, fever, influenza, and Pneumonia, were identified as effective predictors of ILI, in general and by age groups. In a regression analysis modeled with ILIs as the outcome variable, daily visualizations for the Wikipedia pages on Bronchiolitis were identified as negative predictors for ILI in general (β = −0.152, p = 0.032), ILI in age group 0−4 years (β = −0.264, p = 0.001) and 5−14 years (β = −0.202, p = 0.006), while Influenza was characterized as a positive effector for ILIs in the age group 5−14 years (β = 0.245, p = 0.001). Interestingly, not only were the search terms extensively correlated with one another, but all of them were also characterized by autocorrelation through a Durbin-Watson test (all estimates DW < 2.0) In summary, our study identified a complicated pattern of data visualization as no clear association between rates of ILI in pediatric age group 0−4 and 5 to 14 years was actually found. Finally, our data stress that the infodemiology option may be quite problematic for assessing the time trend of RSV infections in Italy until more appropriate reporting will be made available, by sharing estimates of Lower Respiratory Tract Infections, and through a more accurate characterization of younger age groups.

Epidemiology, prevention and control of pertussis in Spain: New vaccination strategies for lifelong protection

Pertussis is a highly contagious, vaccine-preventable respiratory tract infection, with high morbidity and mortality and a particularly severe effect on newborns and infants under 2 months. The first pertussis vaccines were introduced in the 1940s. Since 1980, however, the incidence of cases has risen despite the extensive vaccination programmes and antibiotic adjuvant treatments available. Transition from the use of whole-cell vaccines to acellular vaccines and the antigenic modifications of Bordetella pertussis have contributed, among other factors, to a reduction in vaccine-acquired immunity and reemergence of the disease. Today, there are still unmet needs not covered by conventional prevention measures and existing antibiotic treatments. This review aims to update the available data, and to discuss which vaccine strategies might contribute to better disease control and prevention.

COVID-19 concerns in cyberspace predict human reduced dispersal in the real world: Meta-regression analysis of time series relationships across American states and 115 countries/territories

On the basis of parasite-stress theory of sociality and behavioral immune system theory, this research examined how concerns regarding the Coronavirus disease 2019 (COVID-19) in cyberspace (i.e., online search volume for coronavirus-related keywords) would predict human reduced dispersal in the real world (i.e., human mobility trends throughout the pandemic) between January 05, 2020 and May 22, 2021. Multiple regression analyses controlling for COVID-19 cases per million, case fatality rate, death-thought accessibility, government stringency index, yearly trends, season, religious holidays, and reduced dispersal in the preceding week were conducted. Meta-regression analysis of the multiple regression results showed that when there were high levels of COVID-19 concerns in cyberspace in a given week, the amount of time people spent at home increased from the previous week across American states (Study 1) and 115 countries/territories (Study 2). Across studies, the associations between COVID-19 concerns and reduced dispersal were stronger in areas of higher historical risks of infectious-disease contagion. Compared with actual coronavirus threat, COVID-19 concerns in cyberspace had significantly larger effects on predicting human reduced dispersal in the real world. Thus, online query data have invaluable implications for predicting large-scale behavioral changes in response to life-threatening events in the real world and are indispensable for COVID-19 surveillance.

Infodemiology of flu: Google trends-based analysis of Italians' digital behavior and a focus on SARS-CoV-2, Italy

Introduction

The aim of the current study was to assess if the frequency of internet searches for influenza are aligned with Italian National Institute of Health (ISS) cases and deaths. Also, we evaluate the distribution over time and the correlation between search volume of flu and flu symptoms with reported new cases of SARS-CoV-2.

Materials and methods

The reported cases and deaths of flu and the reported cases of SARS-CoV-2 were selected from the reports of ISS, the data have been aggregated by week. The search volume provided by Google Trends (GT) has a relative nature and is calculated as a percentage of query related to a specific term in connection with a determined place and time-frame.

Results

The strongest correlation between GT search and influenza cases was found at a lag of +1 week particularly for the period 2015-2019. A strong correlation was also found at a lag of +1 week between influenza death and GT search. About the correlation between GT search and SARS-CoV-2 new cases the strongest correlation was found at a lag of +3 weeks for the term flu.

Conclusion

In the last years research in health care has used GT data to explore public interest in various fields of medicine. Caution should be used when interpreting the findings of digital surveillance.

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

Background

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

Methods

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

Results

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

Conclusion

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

•

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

•

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

•

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

•

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

•

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

What can internet users' behaviours reveal about the mental health impacts of the COVID-19 pandemic? A systematic review

OBJECTIVES

At the end of 2019, an acute infectious pneumonia (coronavirus disease 2019 [COVID-19]) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) began in Wuhan, China, and subsequently spread around the world starting a pandemic. Globally, to date, there have been >118 million confirmed cases, including >2 million deaths. In this context, it has been shown that the psychological impact of the pandemic is important and that it can be associated with an increase in internet searches related to fear, anxiety, depression, as well as protective behaviours, health knowledge and even maladaptive behaviours.

STUDY DESIGN

This is a systematic review.

METHODS

This review aims to collect, analyse and synthesise available evidence on novel data streams for surveillance purposes and/or their potential for capturing the public reaction to epidemic outbreaks, particularly focusing on mental health effects and emotions.

RESULTS

At the end of the screening process, 19 articles were included in this systematic review. Our results show that the COVID-19 pandemic had a great impact on internet searches for mental health of entire populations, which manifests itself in a significant increase of depressed, anxious and stressed internet users' emotions.

CONCLUSIONS

Novel data streams can support public health experts and policymakers in establishing priorities and setting up long-term strategies to mitigate symptoms and tackle mental health disorders.

Temporary Fertility Decline after Large Rubella Outbreak, Japan

Japan experienced 2 large rubella epidemics in 2004 and 2012–2014. Because of suboptimal immunization levels, the country has been experiencing a third major outbreak during 2018–2020. We conducted time series analyses to evaluate the effect of the 2012–2014 nationwide rubella epidemic on prefecture-level natality in Japan. We identified a statistically significant decline in fertility rates associated with rubella epidemic activity and increased Google searches for the term “rubella.” We noted that the timing of fertility declines in 2014 occurred 9–13 months after peak rubella incidence months in 2013 in 4 prefectures with the highest rubella incidence. Public health interventions should focus on enhancing vaccination campaigns against rubella, not only to protect pregnant women from infection but also to mitigate declines in population size and birth rates.

Epidemiology, prevention and control of pertussis in Spain: New vaccination strategies for lifelong protection

Pertussis is a highly contagious, vaccine-preventable respiratory tract infection, with high morbidity and mortality and a particularly severe effect on newborns and infants under 2 months. The first pertussis vaccines were introduced in the 1940s. Since 1980, however, the incidence of cases has risen despite the extensive vaccination programmes and antibiotic adjuvant treatments available. Transition from the use of whole-cell vaccines to acellular vaccines and the antigenic modifications of Bordetella pertussis have contributed, among other factors, to a reduction in vaccine-acquired immunity and reemergence of the disease. Today, there are still unmet needs not covered by conventional prevention measures and existing antibiotic treatments. This review aims to update the available data, and to discuss which vaccine strategies might contribute to better disease control and prevention.

Insight the data: Wikipedia's researches and real cases of arboviruses in Italy

OBJECTIVES

The primary aim of this study was to evaluate the temporal correlation between Wikitrends and conventional surveillance data generated for Chikungunya, Dengue, Zika, and West Nile Virus infection reported by bulletin of Italian National Institute of Health (Istituto Superiore di Sanità in italian, ISS).

STUDY DESIGN

A cross-sectional study design was used.

METHODS

The reported cases of Dengue and Chikungunya were selected from July 2015 to December 2019. For West Nile Virus, the bulletins are issued in the period June-November (6 months) of the years 2015-2019, and for Zika virus, the data reported in the ISS bulletin start from January 2016. From Wikipedia Trends, we extracted the number of monthly views by users from the July 2015 to December 2019 of the pages Chikungunya, Dengue, Zika virus, and West Nile Virus.

RESULTS

A correlation was observed between the bulletin of ISS and Wikipedia Wikitrends, the correlation was strong for Chikungunya and West Nile Virus (r = 0.9605; r = 0.9556, respectively), and highly statistically significant with P-values <0.001. The correlation was moderate for Dengue and Zika virus (r = 0.6053; r = 0.5888, respectively), but highly statistically significant with P-values <0.001.

CONCLUSIONS

Classical surveillance system should be integrated with the tools of digital epidemiology that have potential role in public health for the dynamic information and provide near real-time indicators of the spread of infectious disease.

Correlation between flu and Wikipedia's pages visualization

Introduction:

This study aimed to assess if the frequency of the Italian general public searches for influenza, using the Wikipedia web-page, are aligned with Istituto Superiore di Sanità (ISS) influenza cases.

Materials and Methods:

The reported cases of flu were selected from October 2015 to May 2019. Wikipedia Trends was used to assess how many times a specific page was read by users; data were extracted as daily data and aggregated on a weekly basis. The following data were extracted: number of weekly views by users from the October 2015 to May 2019 of the pages: Influenza, Febbre and Tosse (Flu, Fever and Cough, in English). Cross-correlation results are obtained as product-moment correlations between the two times series.

Results:

Regarding the database with weekly data, temporal correlation was observed between the bulletin of ISS and Wikipedia search trends. The strongest correlation was at a lag of 0 for number of cases and Flu (r=0.7571), Fever and Cough (r=0.7501). The strongest correlation was at a lag of -1 for Fever and Cough (r=0.7501). The strongest correlation was at a lag of 1 for number of cases and Flu (r=0.7559), Fever and Cough (r=0.7501).

Conclusions:

A possible future application for programming and management interventions of Public Health is proposed.

Big data analytics as a tool for fighting pandemics: a systematic review of literature

Infectious and contagious diseases represent a major challenge for health systems worldwide, either in private or public sectors. More recently, with the increase in cases related to these problems, combined with the recent global pandemic of COVID-19, the need to study strategies to treat these health disturbs is even more latent. Big Data, as well as Big Data Analytics techniques, have been addressed in this context with the possibility of predicting, mapping, tracking, monitoring, and raising awareness about these epidemics and pandemics. Thus, the purpose of this study is to identify how BDA can help in cases of pandemics and epidemics. To achieve this purpose, a systematic review of literature was carried out using the methodology Methodi Ordinatio. The rigorous search resulted in a portfolio of 45 articles, retrived from scientific databases. For the collection and analysis of data, the softwares NVivo 12 and VOSviewer were used. The content analysis sought to identify how Big Data and Big Data Analytics can help fighting epidemics and pandemics. The types and sources of data used in cases of previous epidemics and pandemics were identified, as well as techniques for treating these data. The results showed that the main sources of data come from social media and Internet search engines. The most common techniques for analyzing these data involve the use of statistics, such as correlation and regression, combined with other techniques. Results shows that there is a fruitiful field of study to be explored by both areas, Big Data and Health.

Resources for assessing parents' vaccine hesitancy: a systematic review of the literature

The concept of Vaccine Hesitancy has begun to appear in the scientific landscape, referring to the reluctance of a growing proportion of people to accept the vaccination offer. A variety of factors were identified as being associated with vaccine hesitancy but there was no universal algorithm and currently there aren’t any established metrics to assess either the presence or impact of vaccine hesitancy. The aim of this study was to systematically review the published questionnaires evaluating parental vaccine hesitancy, to highlight the differences among these surveys and offer a general overview on this matter. This study offers a deeper perspective on the available questionnaires, helping future researches to identify the most suitable one according to their own aim and study setting.

Sentiment Analysis Methods for HPV VaccinesRelated Tweets Based on Transfer Learning

The widespread use of social media provides a large amount of data for public sentimentanalysis. Based on social media data, researchers can study public opinions on humanpapillomavirus (HPV) vaccines on social media using machine learning-based approaches that willhelp us understand the reasons behind the low vaccine coverage. However, social media data isusually unannotated, and data annotation is costly. The lack of an abundant annotated dataset limitsthe application of deep learning methods in effectively training models. To tackle this problem, wepropose three transfer learning approaches to analyze the public sentiment on HPV vaccines onTwitter. One was transferring static embeddings and embeddings from language models (ELMo)and then processing by bidirectional gated recurrent unit with attention (BiGRU-Att), called DWEBiGRU-Att. The others were fine-tuning pre-trained models with limited annotated data, called finetuninggenerative pre-training (GPT) and fine-tuning bidirectional encoder representations fromtransformers (BERT). The fine-tuned GPT model was built on the pre-trained generative pretraining(GPT) model. The fine-tuned BERT model was constructed with BERT model. Theexperimental results on the HPV dataset demonstrated the efficacy of the three methods in thesentiment analysis of the HPV vaccination task. The experimental results on the HPV datasetdemonstrated the efficacy of the methods in the sentiment analysis of the HPV vaccination task. Thefine-tuned BERT model outperforms all other methods. It can help to find strategies to improvevaccine uptake.

Causality Analysis of Google Trends and Dengue Incidence in Bandung, Indonesia With Linkage of Digital Data Modeling: Longitudinal Observational Study

Background

The popularity of dengue can be inferred from Google Trends that summarizes Google searches of related topics. Both the disease and its Google Trends have a similar source of causation in the dengue virus, leading us to hypothesize that dengue incidence and Google Trends results have a long-run equilibrium.

Objective

This research aimed to investigate the properties of this long-run equilibrium in the hope of using the information derived from Google Trends for the early detection of upcoming dengue outbreaks.

Methods

This research used the cointegration method to assess a long-run equilibrium between dengue incidence and Google Trends results. The long-run equilibrium was characterized by their linear combination that generated a stationary process. The Dickey-Fuller test was adopted to check the stationarity of the processes. An error correction model (ECM) was then adopted to measure deviations from the long-run equilibrium to examine the short-term and long-term effects. The resulting models were used to determine the Granger causality between the two processes. Additional information about the two processes was obtained by examining the impulse response function and variance decomposition.

Results

The Dickey-Fuller test supported an implicit null hypothesis that the dengue incidence and Google Trends results are nonstationary processes (P=.01). A further test showed that the processes were cointegrated (P=.01), indicating that their particular linear combination is a stationary process. These results permitted us to construct ECMs. The model showed the direction of causality of the two processes, indicating that Google Trends results will Granger-cause dengue incidence (not in the reverse order).

Conclusions

Various hypothesis testing results in this research concluded that Google Trends results can be used as an initial indicator of upcoming dengue outbreaks.

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

Google Trends-based non-English language query data and epidemic diseases: a cross-sectional study of the popular search behaviour in Taiwan

OBJECTIVE

This study developed a surveillance system suitable for monitoring epidemic outbreaks and assessing public opinion in non-English-speaking countries. We evaluated whether social media reflects social uneasiness and fear during epidemic outbreaks and natural catastrophes.

DESIGN

Cross-sectional study.

SETTING

Freely available epidemic data in Taiwan.

MAIN OUTCOME MEASURE

We used weekly epidemic incidence data obtained from the Taiwan Centers for Disease Control and online search query data obtained from Google Trends between 4 October 2015 and 2 April 2016. To validate whether non-English query keywords were useful surveillance tools, we estimated the correlation between online query data and epidemic incidence in Taiwan.

RESULTS

With our approach, we noted that keywords ('common cold'), ('fever') and ('cough') exhibited good to excellent correlation between Google Trends query data and influenza incidence (r=0.898, p<0.001; r=0.773, p<0.001; r=0.796, p<0.001, respectively). They also displayed high correlation with influenza-like illness emergencies (r=0.900, p<0.001; r=0.802, p<0.001; r=0.886, p<0.001, respectively) and outpatient visits (r=0.889, p<0.001; r=0.791, p<0.001; r=0.870, p<0.001, respectively). We noted that the query ('enterovirus') exhibited excellent correlation with the number of enterovirus-infected patients in emergency departments (r=0.914, p<0.001).

CONCLUSIONS

These results suggested that Google Trends can be a good surveillance tool for epidemic outbreaks, even in Taiwan, the non-English-speaking country. Online search activity indicates that people are concerned about epidemic diseases, even if they do not visit hospitals. This prompted us to develop useful tools to monitor social media during an epidemic because such media usage reflects infectious disease trends more quickly than does traditional reporting.

Silver lining of COVID-19: Heightened global interest in pneumococcal and influenza vaccines, an infodemiology study

BACKGROUND

Health-seeking behaviors change during pandemics and may increase with regard to illnesses with symptoms similar to the pandemic. The global reaction to COVID-19 may drive interest in vaccines for other diseases.

OBJECTIVES

Our study investigated the correlation between global online interest in COVID-19 and interest in CDC-recommended routine vaccines.

DESIGN, SETTINGS, MEASUREMENTS

This infodemiology study used Google Trends data to quantify worldwide interest in COVID-19 and CDC-recommended vaccines using the unit search volume index (SVI), which estimates volume of online search activity relative to highest volume of searches within a specified period. SVIs from December 30, 2019 to March 30, 2020 were collected for "coronavirus (Virus)" and compared with SVIs of search terms related to CDC-recommended adult vaccines. To account for seasonal variation, we compared SVIs from December 30, 2019 to March 30, 2020 with SVIs from the same months in 2015 to 2019. We performed country-level analyses in ten COVID-19 hotspots and ten countries with low disease burden.

RESULTS

There were significant positive correlations between SVIs for "coronavirus (Virus)" and search terms for pneumococcal (R = 0.89, p < 0.0001) and influenza vaccines (R = 0.93, p < 0.0001) in 2020, which were greater than SVIs for the same terms in 2015-2019 (p = 0.005, p < 0.0001, respectively). Eight in ten COVID-19 hotspots demonstrated significant positive correlations between SVIs for coronavirus and search terms for pneumococcal and influenza vaccines.

LIMITATIONS

SVIs estimate relative changes in online interest and do not represent the interest of people with no Internet access.

CONCLUSION

A peak in worldwide interest in pneumococcal and influenza vaccines coincided with the COVID-19 pandemic in February and March 2020. Trends are likely not seasonal in origin and may be driven by COVID-19 hotspots. Global events may change public perception about the importance of vaccines. Our findings may herald higher demand for pneumonia and influenza vaccines in the upcoming season.

Machine Learning to Detect Self-Reporting of Symptoms, Testing Access, and Recovery Associated With COVID-19 on Twitter: Retrospective Big Data Infoveillance Study

Background

The coronavirus disease (COVID-19) pandemic is a global health emergency with over 6 million cases worldwide as of the beginning of June 2020. The pandemic is historic in scope and precedent given its emergence in an increasingly digital era. Importantly, there have been concerns about the accuracy of COVID-19 case counts due to issues such as lack of access to testing and difficulty in measuring recoveries.

Objective

The aims of this study were to detect and characterize user-generated conversations that could be associated with COVID-19-related symptoms, experiences with access to testing, and mentions of disease recovery using an unsupervised machine learning approach.

Methods

Tweets were collected from the Twitter public streaming application programming interface from March 3-20, 2020, filtered for general COVID-19-related keywords and then further filtered for terms that could be related to COVID-19 symptoms as self-reported by users. Tweets were analyzed using an unsupervised machine learning approach called the biterm topic model (BTM), where groups of tweets containing the same word-related themes were separated into topic clusters that included conversations about symptoms, testing, and recovery. Tweets in these clusters were then extracted and manually annotated for content analysis and assessed for their statistical and geographic characteristics.

Results

A total of 4,492,954 tweets were collected that contained terms that could be related to COVID-19 symptoms. After using BTM to identify relevant topic clusters and removing duplicate tweets, we identified a total of 3465 (<1%) tweets that included user-generated conversations about experiences that users associated with possible COVID-19 symptoms and other disease experiences. These tweets were grouped into five main categories including first- and secondhand reports of symptoms, symptom reporting concurrent with lack of testing, discussion of recovery, confirmation of negative COVID-19 diagnosis after receiving testing, and users recalling symptoms and questioning whether they might have been previously infected with COVID-19. The co-occurrence of tweets for these themes was statistically significant for users reporting symptoms with a lack of testing and with a discussion of recovery. A total of 63% (n=1112) of the geotagged tweets were located in the United States.

Conclusions

This study used unsupervised machine learning for the purposes of characterizing self-reporting of symptoms, experiences with testing, and mentions of recovery related to COVID-19. Many users reported symptoms they thought were related to COVID-19, but they were not able to get tested to confirm their concerns. In the absence of testing availability and confirmation, accurate case estimations for this period of the outbreak may never be known. Future studies should continue to explore the utility of infoveillance approaches to estimate COVID-19 disease severity.

Data Mining and Content Analysis of the Chinese Social Media Platform Weibo During the Early COVID-19 Outbreak: Retrospective Observational Infoveillance Study

BACKGROUND

The coronavirus disease (COVID-19) pandemic, which began in Wuhan, China in December 2019, is rapidly spreading worldwide with over 1.9 million cases as of mid-April 2020. Infoveillance approaches using social media can help characterize disease distribution and public knowledge, attitudes, and behaviors critical to the early stages of an outbreak.

OBJECTIVE

The aim of this study is to conduct a quantitative and qualitative assessment of Chinese social media posts originating in Wuhan City on the Chinese microblogging platform Weibo during the early stages of the COVID-19 outbreak.

METHODS

Chinese-language messages from Wuhan were collected for 39 days between December 23, 2019, and January 30, 2020, on Weibo. For quantitative analysis, the total daily cases of COVID-19 in Wuhan were obtained from the Chinese National Health Commission, and a linear regression model was used to determine if Weibo COVID-19 posts were predictive of the number of cases reported. Qualitative content analysis and an inductive manual coding approach were used to identify parent classifications of news and user-generated COVID-19 topics.

RESULTS

A total of 115,299 Weibo posts were collected during the study time frame consisting of an average of 2956 posts per day (minimum 0, maximum 13,587). Quantitative analysis found a positive correlation between the number of Weibo posts and the number of reported cases from Wuhan, with approximately 10 more COVID-19 cases per 40 social media posts (P<.001). This effect size was also larger than what was observed for the rest of China excluding Hubei Province (where Wuhan is the capital city) and held when comparing the number of Weibo posts to the incidence proportion of cases in Hubei Province. Qualitative analysis of 11,893 posts during the first 21 days of the study period with COVID-19-related posts uncovered four parent classifications including Weibo discussions about the causative agent of the disease, changing epidemiological characteristics of the outbreak, public reaction to outbreak control and response measures, and other topics. Generally, these themes also exhibited public uncertainty and changing knowledge and attitudes about COVID-19, including posts exhibiting both protective and higher-risk behaviors.

CONCLUSIONS

The results of this study provide initial insight into the origins of the COVID-19 outbreak based on quantitative and qualitative analysis of Chinese social media data at the initial epicenter in Wuhan City. Future studies should continue to explore the utility of social media data to predict COVID-19 disease severity, measure public reaction and behavior, and evaluate effectiveness of outbreak communication.

Vaccines are underused in pregnancy: what about knowledge, attitudes and practices of providers?

Introduction.

To investigate actual knowledge of official recommendations towards seasonal influenza (SID), and Tetanus-diphtheria acellular-pertussis (Tdap) vaccines in obstetrics/gynecologists (OBGYN).

Methods.

PubMed and EMBASE databases were searched. A meta-analysis was performed to calculate odds ratio (OR) and 95% confidence interval (CI) among case controls, cross-sectional studies, either questionnaire or laboratory exams based.

Results.

A total of 6 studies met inclusion criteria, including 1323 OBGYN from 5 different countries. Overall, around 99% of sampled professionals were aware that official recommendations towards SID in pregnancy do exist, compared to 92% for Tdap, with significant heterogeneity (I² > 95%, p < 0.001). Concerns about vaccine safety was reported by 10% of respondents for Tdap, and by 6.0% for SID, but again available studies were substantially heterogenous (I² = 86.7% and 86.0%, p < 0.001). Eventually, 93% of respondents actively recommended SID in pregnancy, compared to 88% for Tdap (I² 98.8% and I² 95.9%, respectively p < 0.001). The evidence of significant publication bias was initially subjectively identified from the funnel plot, and then objectively confirmed through the regression test for all analyses.

Conclusions.

These results suggest an appropriated understanding of official recommendation among sampled OBGYN, with high shares of professionals actively promoting vaccination practices among their patients. Despite the high heterogeneity and the significant publication bias we identified, our results also hint towards extensive knowledge gaps of OBGYN, and particularly regarding unmotivated concerns about vaccine safety. As a consequence, appropriate information and formation campaigns should be appropriately tailored. (www.actabiomedica.it)

Burden of measles using disability-adjusted life years, Umbria 2013-2018

Background and aim:

The low measles vaccination coverage contributes to the re-emerging of measles in Italy. This study aimed to estimate the measles burden, expressed in Disability Adjusted Life Years (DALYs), in Umbria, for the period 2013-2018.

Methods:

Data on measles cases in Umbria were obtained from the MoRoNet. While data related to the resident population, were obtained from the website of the National Institute of Statistics. The estimated DALYs was calculated using the Burden of Communicable Diseases in Europe toolkit. The results are expressed in DALYs per year, per case and per 100,000 subjects, for acute illness and for sequelae.

Results:

The estimated incidence in mean for the entire period was 52.50 cases per year. Resulting in an average loss of 3.10 DALYs per year.

Conclusions:

The data obtained from this analysis provide important information on the impact of measles in the Umbria region, and offer useful data to the Health Authorities that can be used to reduce measles incidence in the region.

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

Background

Public health surveillance is based on the continuous and systematic collection, analysis, and interpretation of data. This informs the development of early warning systems to monitor epidemics and documents the impact of intervention measures. The introduction of digital data sources, and specifically sources available on the internet, has impacted the field of public health surveillance. New opportunities enabled by the underlying availability and scale of internet-based sources (IBSs) have paved the way for novel approaches for disease surveillance, exploration of health communities, and the study of epidemic dynamics. This field and approach is also known as infodemiology or infoveillance.

Objective

This review aimed to assess research findings regarding the application of IBSs for public health surveillance (infodemiology or infoveillance). To achieve this, we have presented a comprehensive systematic literature review with a focus on these sources and their limitations, the diseases targeted, and commonly applied methods.

Methods

A systematic literature review was conducted targeting publications between 2012 and 2018 that leveraged IBSs for public health surveillance, outbreak forecasting, disease characterization, diagnosis prediction, content analysis, and health-topic identification. The search results were filtered according to previously defined inclusion and exclusion criteria.

Results

Spanning a total of 162 publications, we determined infectious diseases to be the preferred case study (108/162, 66.7%). Of the eight categories of IBSs (search queries, social media, news, discussion forums, websites, web encyclopedia, and online obituaries), search queries and social media were applied in 95.1% (154/162) of the reviewed publications. We also identified limitations in representativeness and biased user age groups, as well as high susceptibility to media events by search queries, social media, and web encyclopedias.

Conclusions

IBSs are a valuable proxy to study illnesses affecting the general population; however, it is important to characterize which diseases are best suited for the available sources; the literature shows that the level of engagement among online platforms can be a potential indicator. There is a necessity to understand the population’s online behavior; in addition, the exploration of health information dissemination and its content is significantly unexplored. With this information, we can understand how the population communicates about illnesses online and, in the process, benefit public health.

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.

Current status of use of big data and artificial intelligence in RMDs: a systematic literature review informing EULAR recommendations

Objective

To assess the current use of big data and artificial intelligence (AI) in the field of rheumatic and musculoskeletal diseases (RMDs).

Methods

A systematic literature review was performed in PubMed MEDLINE in November 2018, with key words referring to big data, AI and RMDs. All original reports published in English were analysed. A mirror literature review was also performed outside of RMDs on the same number of articles. The number of data analysed, data sources and statistical methods used (traditional statistics, AI or both) were collected. The analysis compared findings within and beyond the field of RMDs.

Results

Of 567 articles relating to RMDs, 55 met the inclusion criteria and were analysed, as well as 55 articles in other medical fields. The mean number of data points was 746 million (range 2000–5 billion) in RMDs, and 9.1 billion (range 100 000–200 billion) outside of RMDs. Data sources were varied: in RMDs, 26 (47%) were clinical, 8 (15%) biological and 16 (29%) radiological. Both traditional and AI methods were used to analyse big data (respectively, 10 (18%) and 45 (82%) in RMDs and 8 (15%) and 47 (85%) out of RMDs). Machine learning represented 97% of AI methods in RMDs and among these methods, the most represented was artificial neural network (20/44 articles in RMDs).

Conclusions

Big data sources and types are varied within the field of RMDs, and methods used to analyse big data were heterogeneous. These findings will inform a European League Against Rheumatism taskforce on big data in RMDs.

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.

Google Trends Predicts Present and Future Plague Cases During the Plague Outbreak in Madagascar: Infodemiological Study

BACKGROUND

Plague is a highly infectious zoonotic disease caused by the bacillus Yersinia pestis. Three major forms of the disease are known: bubonic, septicemic, and pneumonic plague. Though highly related to the past, plague still represents a global public health concern. Cases of plague continue to be reported worldwide. In recent months, pneumonic plague cases have been reported in Madagascar. However, despite such a long-standing and rich history, it is rather difficult to get a comprehensive overview of the general situation. Within the framework of electronic health (eHealth), in which people increasingly search the internet looking for health-related material, new information and communication technologies could enable researchers to get a wealth of data, which could complement traditional surveillance of infectious diseases.

OBJECTIVE

In this study, we aimed to assess public reaction regarding the recent plague outbreak in Madagascar by quantitatively characterizing the public's interest.

METHODS

We captured public interest using Google Trends (GT) and correlated it to epidemiological real-world data in terms of incidence rate and spread pattern.

RESULTS

Statistically significant positive correlations were found between GT search data and confirmed (R2=0.549), suspected (R2=0.265), and probable (R2=0.518) cases. From a geospatial standpoint, plague-related GT queries were concentrated in Toamasina (100%), Toliara (68%), and Antananarivo (65%). Concerning the forecasting models, the 1-day lag model was selected as the best regression model.

CONCLUSIONS

An earlier digital Web search reaction could potentially contribute to better management of outbreaks, for example, by designing ad hoc interventions that could contain the infection both locally and at the international level, reducing its spread.