Digital Disease Detection — Harnessing the Web for Public Health Surveillance

By John S. Brownstein, Clark C. Freifeld, L. Madoff. New England Journal of Medicine, 2009

758 citations recorded by the source corpus.

John Brownstein, Clark Freifeld, and Lawrence Madoff write that a new generation of disease-surveillance “mashups” can mine, categorize, filter, and visualize online intelligence about epidemics in real time.

Subfields: Epidemiology, Health, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/563e52e04e1a258261f2c2eb47bcedbac4019b2a

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Epidemiology

In Health

In Sociology and Political Science

Citations in the corpus

Digital Epidemiology (2012, background): “The observation of the spatiotemporal movements of millions of people during disease outbreaks [4], the rapid detection of an unusual respiratory illness in a remote village anywhere on the globe [5], the near real-time estimation of influenza activity levels [6,7], and the assessment of vaccination”
Rapid implementation of mobile technology for real-time epidemiology of COVID-19 (2020, background): “Mobile phone applications or web-based tools facilitate self-guided collection of population-level data at scale (14) , the results of which can then be rapidly redeployed to inform participants of urgent health information (14, 15) .”
Risk of COVID-19 among front-line health-care workers and the general community: a prospective cohort study (2020, neutral): “36 By recruiting participants through existing cohort studies (https://www.monganinstitute.org/cope-consortium), these results also provide proof-of-concept of the feasibility of leveraging existing infrastructure and engaged participants to address a key knowledge gap.”
Health promotion in the digital era: a critical commentary. (2015, background): “The opportunity to collect large masses of digital data by accessing digital patient records and other health informatics and harvesting search engine queries and social media content is viewed as having great potential for producing new knowledges about illness and disease and contributing to preve”
The Use of Google Trends in Health Care Research: A Systematic Review (2014, background): “Internet search data may provide valuable insights into patterns of disease and population behavior.[1] In fact, the Institute of Medicine recognizes that the application of Internet data in health care research holds promise and may ‘‘complement and extend the data foundations that presently exist’”
Predicting the Present with Google Trends (2009, neutral): “So far as we know, the first published paper that suggested that web search data was useful in forecasting economic statistics was Ettredge et al. [2005], which examined the U.”
NCBI disease corpus: A resource for disease name recognition and concept normalization (2014, background): “of Health, and as a result, it is frequently searched for in literature [1] and on the Internet [2].”
Towards a genomics-informed, real-time, global pathogen surveillance system (2017, methodology): “This new approach to surveillance is known as digital epidemiology and is also referred to as digital disease detection 86 .”
National and Local Influenza Surveillance through Twitter: An Analysis of the 2012-2013 Influenza Epidemic (2013, background): “For example, news articles, search engine statistics and mobile phone data have been shown to be informative indicators of influenza activity, including the novel H7N9 outbreak [3,4].”
Google Trends in Infodemiology and Infoveillance: Methodology Framework (2019, methodology): “Digital disease detection [53] consists of detecting, analyzing, and predicting disease occurrence and spread, and several types of online sources are used, including mainly digital platforms [54,55].”
#Earthquake: Twitter as a Distributed Sensor System (2013, background): “Furthermore, recent work by Cheng et al. (2010) showed that even without any IP or geolocation information provided, users’ location could often be estimated at the city level based purely on the content of their messages.”
Researching Volunteered Geographic Information: Spatial Data, Geographic Research, and New Social Practice (2012, background): “Medical and health researchers have also used VGI to address many challenging issues in disease surveillance as well as health care accessibility (Boulos et al. 2008; Cheung et al. 2008; Brownstein, Freifeld, and Madoff 2009).”