Additional file 2: S1. of Assessing Ebola-related web search behaviour: insights and implications from an analytical study of Google Trends-based query volumes

Dataset of raw weekly GT data and weekly new cases of Ebola as according to the WHO patient database and situation report at the global level (Sheet 1); in the three main affected West African Ebola countries (Guinea – Sheet 2, Sierra Leone – Sheet 3 and Liberia – Sheet 4); raw weekly GT data in all countries where primary cases of Ebola were registered and Pearson’s correlations (Sheet 5); total GT RSV and the total number of Ebola cases per country (Sheet 6). (XLSX 44 kb

Zika and Zika virus related Google Trends search queries at global level, November 2015 –October 2016.

<p>Zika and Zika virus related Google Trends search queries at global level, November 2015 –October 2016.</p

Global reaction to the recent outbreaks of Zika virus: Insights from a Big Data analysis - Fig 1

<p><b>Monthly normalized data of web-searches and social network interactions related to Zika retrieved from Google Trends, Google News, Wikitrends, YouTube and Twitter from January 2004 to June 2016 (1a) and from November 2015 to October 2016 (1b)</b>.</p

Global reaction to the recent outbreaks of Zika virus: Insights from a Big Data analysis - Fig 3

<p><b>Global interest for Zika (3a) and Zika virus-related search terms (3b and 3c): internet activities from November 2015 to October 2016 according to country</b> (the map was freely accessible and modifiable at the link <a href="https://commons.wikimedia.org/wiki/File:Carte_du_monde_vierge_(Allemagnes_séparées).svg" target="_blank">https://commons.wikimedia.org/wiki/File:Carte_du_monde_vierge_(Allemagnes_séparées).svg</a>).</p

Novel data streams used for capturing public reaction to Zika epidemic outbreak.

<p>Novel data streams used for capturing public reaction to Zika epidemic outbreak.</p

Spearman’s correlation between all novel data streams.

<p>Spearman’s correlation between all novel data streams.</p

External validation of unsupervised COVID-19 clinical phenotypes and their prognostic impact

Hospitalized patients with coronavirus disease 2019 (COVID-19) can be classified into different clinical phenotypes based on their demographic, clinical, radiology, and laboratory features. We aimed to validate in an external cohort of hospitalized COVID-19 patients the prognostic value of a previously described phenotyping system (FEN-COVID-19) and to assess the reproducibility of phenotypes development as a secondary analysis. Patients were classified in phenotypes A, B or C according to the severity of oxygenation impairment, inflammatory response, hemodynamic and laboratory tests according to the FEN-COVID-19 method. Overall, 992 patients were included in the study, and 181 (18%), 757 (76%) and 54 (6%) of them were assigned to the FEN-COVID-19 phenotypes A, B, and C, respectively. An association with mortality was observed for phenotype C vs. A (hazard ratio [HR] 3.10, 95% confidence interval [CI] 1.81–5.30, p p p = 0.115). By means of cluster analysis, three different phenotypes were also identified in our cohort, with an overall similar gradient in terms of prognostic impact to that observed when patients were assigned to FEN-COVID-19 phenotypes. The prognostic impact of FEN-COVID-19 phenotypes was confirmed in our external cohort, although with less difference in mortality between phenotypes A and B than in the original study. Hospitalized patients with COVID-19 can be classified into different clinical phenotypes based on their demographic, clinical, radiology, and laboratory featuresIn this study, we externally confirmed the prognostic impact of clinical phenotypes previously identified by Gutierrez-Gutierrez and colleagues in a Spanish cohort of hospitalized patients with COVID-19, and the usefulness of their simplified probabilistic model for phenotypes assignmentThis could indirectly support the validity of both phenotype’s development and their extrapolation to other hospitals and countries for management decisions during other possible future viral pandemics Hospitalized patients with COVID-19 can be classified into different clinical phenotypes based on their demographic, clinical, radiology, and laboratory features In this study, we externally confirmed the prognostic impact of clinical phenotypes previously identified by Gutierrez-Gutierrez and colleagues in a Spanish cohort of hospitalized patients with COVID-19, and the usefulness of their simplified probabilistic model for phenotypes assignment This could indirectly support the validity of both phenotype’s development and their extrapolation to other hospitals and countries for management decisions during other possible future viral pandemics</p