BioCaster: detecting public health rumors with a Web-based text mining system

Collier, Nigel; Doan, Son; Kawazoe, Ai; Goodwin, Reiko Matsuda; Conway, Mike; Tateno, Yoshio; Ngo, Quoc Hung; Đinh, Điền; Kawtrakul, Asanee; Takeuchi, Koichi; Shigematsu, Mika; Taniguchi, Kiyosu

doi:10.1093/bioinformatics/btn534

Cited by 203 publications

(147 citation statements)

References 3 publications

(3 reference statements)

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“…For example, we exclude from detailed analysis work that provides only alerts [39,40], measures public perception of a disease [41], includes disease dynamics in its model [42], evaluates a third-party method [43], uses non-single-source data feeds [39,44], or crowd-sources health-related data (participatory disease surveillance) [45,46]. We also focus on work that estimates biologically-rooted metrics.…”

Section: Author Summarymentioning

confidence: 99%

Global Disease Monitoring and Forecasting with Wikipedia

Generous

Fairchild²,

Deshpande³

et al. 2016

OJPHI

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Infectious disease is a leading threat to public health, economic stability, and other key social structures. Efforts to mitigate these impacts depend on accurate and timely monitoring to measure the risk and progress of disease. Traditional, biologically-focused monitoring techniques are accurate but costly and slow; in response, new techniques based on social internet data, such as social media and search queries, are emerging. These efforts are promising, but important challenges in the areas of scientific peer review, breadth of diseases and countries, and forecasting hamper their operational usefulness. We examine a freely available, open data source for this use: access logs from the online encyclopedia Wikipedia. Using linear models, language as a proxy for location, and a systematic yet simple article selection procedure, we tested 14 location-disease combinations and demonstrate that these data feasibly support an approach that overcomes these challenges. Specifically, our proof-of-concept yields models with r 2 up to 0.92, forecasting value up to the 28 days tested, and several pairs of models similar enough to suggest that transferring models from one location to another without retraining is feasible. Based on these preliminary results, we close with a research agenda designed to overcome these challenges and produce a disease monitoring and forecasting system that is significantly more effective, robust, and globally comprehensive than the current state of the art.

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Section: Author Summarymentioning

confidence: 99%

Global Disease Monitoring and Forecasting with Wikipedia

Generous

Fairchild²,

Deshpande³

et al. 2016

OJPHI

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“…There are encouraging signs, however, that informal data can be used reliably, as preliminary experiments from DIZIE's development team have verified that respiratory data in the U.S. from Twitter correlates well with data from the CDC [70]. Second, challenges arise in extracting and integrating information from data in -different file formats, schemas, naming systems‖ [71] and languages, and are subjects being discussed in several studies [72,73]. Third, existing ethical issues are rarely explored in passive data collection (e.g., from Twitter).…”

Section: Health Data Collectionmentioning

confidence: 99%

Web GIS-Based Public Health Surveillance Systems: A Systematic Review

Luan

Law

2014

IJGI

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Web Geographic Information System (Web GIS) has been extensively and successfully exploited in various arenas. However, to date, the application of this technology in public health surveillance has yet to be systematically explored in the Web 2.0 era. We reviewed existing Web GIS-based Public Health Surveillance Systems (WGPHSSs) and assessed them based on 20 indicators adapted from previous studies. The indicators comprehensively cover various aspects of WGPHSS development, including metadata, data, cartography, data analysis, and technical aspects. Our literature search identified 58 relevant journal articles and 27 eligible WGPHSSs. Analyses of results revealed that WGPHSSs were frequently used for infectious-disease surveillance, and that geographical and performance inequalities existed in their development. The latest Web and Web GIS technologies have been used in developing WGPHSSs; however, significant deficiencies in data analysis, system compatibility, maintenance, and accessibility exist. A balance between public health surveillance and privacy concerns has yet to be struck. Use of news and social media as well as Web-user searching records as data sources, participatory public health surveillance, collaborations among health sectors at different spatial levels and among various disciplines, adaption or reuse of existing WGPHSSs, and adoption of geomashup and open-source development models were identified as the directions for advancing WGPHSSs.

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“…Different approaches are based on the extraction of information available in Web documents (news, reports, and so forth) in order to predict knowledge [4][5][6].…”

Section: State-of-the-artmentioning

confidence: 99%

Mining Web Data for Epidemiological Surveillance

Breton¹,

Bringay

Marques³

et al. 2013

Lecture Notes in Computer Science

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Abstract. Epidemiological surveillance is an important issue of public health policy. In this paper, we describe a method based on knowledge extraction from news and news classification to understand the epidemic evolution. Descriptive studies are useful for gathering information on the incidence and characteristics of an epidemic. New approaches, based on new modes of mass publication through the web, are developed: based on the analysis of user queries or on the echo that an epidemic may have in the media. In this study, we focus on a particular media: web news. We propose the Epimining approach, which allows the extraction of information from web news (based on pattern research) and a fine classification of these news into various classes (new cases, deaths...). The experiments conducted on a real corpora (AFP news) showed a precision greater than 94% and an F-measure above 85%. We also investigate the interest of tacking into account the data collected through social networks such as Twitter to trigger alarms.

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BioCaster: detecting public health rumors with a Web-based text mining system

Cited by 203 publications

References 3 publications

Global Disease Monitoring and Forecasting with Wikipedia

Global Disease Monitoring and Forecasting with Wikipedia

Web GIS-Based Public Health Surveillance Systems: A Systematic Review

Mining Web Data for Epidemiological Surveillance

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