Surveillance Sans Frontières: Internet-Based Emerging Infectious Disease Intelligence and the HealthMap Project

Harvard University, Cambridge, Massachusetts, United States
PLoS Medicine (Impact Factor: 14.43). 08/2008; 5(7):e151. DOI: 10.1371/journal.pmed.0050151
Source: PubMed


John Brownstein and colleagues discuss HealthMap, an automated real-time system that monitors and disseminates online information about emerging infectious diseases.

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Available from: Clark Freifeld, Apr 22, 2014
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    • "In human medicine, text mining has been successfully applied to clinical records in many public health surveillance systems (Botsis et al., 2011; Steinberger et al., 2008; Brownstein et al., 2008; Wagner et al., 2004). The approaches range from hand-written rule-based systems to fully automated methods using machine learning. "
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    ABSTRACT: Public health surveillance systems rely on the automated monitoring of large amounts of text. While building a text mining system for veterinary syndromic surveillance, we exploit automatic and semi-automatic methods for terminology construction at different stages. Our approaches include term extraction from free-text, grouping of term variants based on string similarity, and linking to an existing medical ontology.
    Full-text · Conference Paper · Jan 2015
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    • "The advantage with respect to other surveillance schemes (e.g. GPs or other digital approaches of unsupervised nature, such as web search records [7, 8], online news [9, 10], or tweets [11]) is the ability to ask users about themselves– including geographic, demographic, mobility, socio-economic and health indicator questions; this information can be compared with national statistics. The aim is to identify possible biases to be taken into account for epidemiological analyses. "
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    ABSTRACT: Background: The Internet is becoming more commonly used as a tool for disease surveillance. Similarly to other surveillance systems and to studies using online data collection, Internet-based surveillance will have biases in participation, affecting the generalizability of the results. Here we quantify the participation biases of Influenzanet, an ongoing European-wide network of Internet-based participatory surveillance systems for influenza-like-illness. Methods: In 2011/2012 Influenzanet launched a standardized common framework for data collection applied to seven European countries. Influenzanet participants were compared to the general population of the participating countries to assess the representativeness of the sample in terms of a set of demographic, geographic, socio-economic and health indicators. Results: More than 30,000 European residents registered to the system in the 2011/2012 season, and a subset of 25,481 participants were selected for this study. All age classes (10 years brackets) were represented in the cohort, including under 10 and over 70 years old. The Influenzanet population was not representative of the general population in terms of age distribution, underrepresenting the youngest and oldest age classes. The gender imbalance differed between countries. A counterbalance between gender-specific information-seeking behavior (more prominent in women) and Internet usage (with higher rates in male populations) may be at the origin of this difference. Once adjusted by demographic indicators, a similar propensity to commute was observed for each country, and the same top three transportation modes were used for six countries out of seven. Smokers were underrepresented in the majority of countries, as were individuals with diabetes; the representativeness of asthma prevalence and vaccination coverage for 65+ individuals in two successive seasons (2010/2011 and 2011/2012) varied between countries. Conclusions: Existing demographic and national datasets allowed the quantification of the participation biases of a large cohort for influenza-like-illness surveillance in the general population. Significant differences were found between Influenzanet participants and the general population. The quantified biases need to be taken into account in the analysis of Influenzanet epidemiological studies and provide indications on populations groups that should be targeted in recruitment efforts.
    Full-text · Article · Sep 2014 · BMC Public Health
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    • "[4] HealthMap is another major system that facilitates the monitoring of global infectious diseases. As described by Freifeld et al. [5], this system uses a wide variety of online formal and informal information sources and channels such as Google News, ProMED, GeoSentinel etc. to collect and aggregate content in several languages which is then classified by infectious disease agents, geography and time. The system is based on open-source products, both for its development (Linux, Apache) and its continued use (Google Maps, Google Translate API, etc). "

    Full-text · Article · Aug 2014
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