Influenza-Like Illness Surveillance on Twitter through Automated Learning of Naïve Language

<div><p>Twitter has the potential to be a timely and cost-effective source of data for syndromic surveillance. When speaking of an illness, Twitter users often report a combination of symptoms, rather than a suspected or final diagnosis, using naïve, everyday language. We developed a minimally trained algorithm that exploits the abundance of health-related web pages to identify all jargon expressions related to a specific technical term. We then translated an influenza case definition into a Boolean query, each symptom being described by a technical term and all related jargon expressions, as identified by the algorithm. Subsequently, we monitored all tweets that reported a combination of symptoms satisfying the case definition query. In order to geolocalize messages, we defined 3 localization strategies based on codes associated with each tweet. We found a high correlation coefficient between the trend of our influenza-positive tweets and ILI trends identified by US traditional surveillance systems.</p> </div

Weekly reported ILI (CDC) and tweets including the words “flu” or “influenza”.

<p>The blue line represents in all graphs the z-scores of CDC’s reported ILI for the 14-week period starting in week 5 (January 2013) through week 18 (May 2013). The red line represents the z-scores of tweets including the words “flu” or “influenza”, geolocalized with the extended narrow localization pattern.</p

Weekly reported ILI (CDC) and tweets satisfying ILI query.

<p>The blue line represents in all graphs the z-scores of CDC’s reported ILI for the 14-week period starting in week 5 (January 2013) through week 18 (May 2013). The red line represents the z-scores of tweets satisfying the ECDC ILI query, selected with a different geolocalization strategy in each of the four graphs: a) all tweets (independently from geolocalization); b) US GEO(GPS localized tweets); c) Extended wide localization pattern; d) Extended narrow localization pattern.</p

Additional file 1: of Investigating paternal preconception risk factors for adverse pregnancy outcomes in a population of internet users

Survey items on paternal preconception risk factor for APOs. (DOCX 16 kb

Additional file 1: of What drives attitude towards telemedicine among families of pediatric patients? A survey

Questionnaire on the attitude towards telemedicine among families of pediatric patients. (DOCX 25 kb

Web-Based Surveillance of Public Information Needs for Informing Preconception Interventions

<div><p>Background</p><p>The risk of adverse pregnancy outcomes can be minimized through the adoption of healthy lifestyles before pregnancy by women of childbearing age. Initiatives for promotion of preconception health may be difficult to implement. Internet can be used to build tailored health interventions through identification of the public's information needs. To this aim, we developed a semi-automatic web-based system for monitoring Google searches, web pages and activity on social networks, regarding preconception health.</p><p>Methods</p><p>Based on the American College of Obstetricians and Gynecologists guidelines and on the actual search behaviors of Italian Internet users, we defined a set of keywords targeting preconception care topics. Using these keywords, we analyzed the usage of Google search engine and identified web pages containing preconception care recommendations. We also monitored how the selected web pages were shared on social networks. We analyzed discrepancies between searched and published information and the sharing pattern of the topics.</p><p>Results</p><p>We identified 1,807 Google search queries which generated a total of 1,995,030 searches during the study period. Less than 10% of the reviewed pages contained preconception care information and in 42.8% information was consistent with ACOG guidelines. Facebook was the most used social network for sharing. Nutrition, Chronic Diseases and Infectious Diseases were the most published and searched topics. Regarding Genetic Risk and Folic Acid, a high search volume was not associated to a high web page production, while Medication pages were more frequently published than searched. Vaccinations elicited high sharing although web page production was low; this effect was quite variable in time.</p><p>Conclusion</p><p>Our study represent a resource to prioritize communication on specific topics on the web, to address misconceptions, and to tailor interventions to specific populations.</p></div

Sharing volumes and the sharing per page (s/p) ratio for each topic.

<p>^a percentage on total sharing events</p><p>Sharing volumes and the sharing per page (s/p) ratio for each topic.</p

Web pages publication.

<p>^a Percentage on total number of pages.</p><p>^b Percentage on topic pages.</p><p>^c Percentage on topic pages.</p><p>^d Percentage on topic information pages.</p><p>Web pages publication.</p

Correlation between the volumes of information pages with searches (A), and with the sharing/page ratio by topic (B).

<p>Correlation between the volumes of information pages with searches (A), and with the sharing/page ratio by topic (B).</p

Google search data by topic and their corresponding volumes.

<p>^a Percentage on total number of queries.</p><p>^b Percentage on total search volume.</p><p>^c Percentage on topic volume</p><p>Google search data by topic and their corresponding volumes.</p