Near Real Time Public Health Protection
with DIZIE and BioCaster
Nigel COLLIER, Son DOAN, Bao-Khanh Ho VO

http://born.nii.ac.jp
What kind ofresearch?

どんな研究？

Early detection and tracking of a possible disease outbreak such as SARS
or Avian influenza is a responsibility for governments who are faced with
monitoring massive quantities of local news on the WWW in several
languages. In BioCaster we are developing a web-portal using the latest
text mining technology that can filter news reports in various regional
languages and present a summarized translation in the local language.
Research is focusing on creating: (1) a multi-lingual knowledge resource(ontology), (2) a high-performance text mining system, (3) an intelligent
linkage system for navigating between news about diseases and thelatest research findings in the literature and genetics databases.

SARSやトリインフルエンザのような感染症の発生を早期に発見し、監視・追跡するには、様々な言語で書かれたWeb上のローカルニュースを、各国の政府が責任を持ってモニターする必要がある。BioCasterプロジェクトでは、最新のテキストマイニング技術を応用して多言語のニュース記事をフィルタリングし、構造化された形式で現地語に翻訳するWebポータルを開発する。特に、(1) 多言語知識リソース（オントロジー）、(2) 高性能クラスタコンピュータおよびストレージシステム、(3) 感染症に関するニュース記事と、研究文献や遺伝子データベースにある最新の研究成果をナビゲートする、
知的なリンケージシステム等の構築に焦点を当てる。

What is global media monitoring?

Global media monitoring aims to rapidly detect publichealth hazards such as disease outbreaks andchemical spillages from open Web based sources
such as news and social media sites like Twitter.
BioCaster is active in the detection of health threats
to humans, animals and plants.

What are the challenges?
Understanding the Web’s limitations…
News sources vary in trustworthiness and different
regions of the world have differing reporting patterns.
We are now beginning to understand the Web’slimitations in terms of its coverage and timeliness ofevent reporting.

Detecting the unusual…
Ebola in Uganda? Salmonellosis inDublin? How does a computer know
when an event is unusual? Our researchanalyses and evaluates a range of timeseries analysis algorithms for statistical
alerting of event counts.

CDC data showing epidemic
alerting thresholds for influenza

Understanding ambiguity…
Writers have many different ways of reporting
the same health condition such as influenza,flu, H5N1, bird flu etc. Reports in multiple
languages represents an opportunity but also
increase the challenge. A key research result
has been the production of a sophisticatedontology for unifying different ways ofreporting health conditions.

Recent Press Reports about BioCaster

Combing information sources…

News reports, blogs, search queries … How do wecombine signals across media that may differ intemporal and spatial granularity as well as reporting
rates and population characteristics? How do wevalidate them against a gold standard? We have justbegun to explore this question in our work within theGrand Challenge funded project DIZIE.

Key Reference
1. Collier, N. et al. (2008) “BioCaster: detecting
public health rumors with a Web-based text
mining system”, BioInformatics, 24(24): 29402941, Oxford University Press.
連絡先： Nigel COLLIER ／ 国立情報学研究所 Principles of Informatics, Associate Professor
TEL : 03-4212-2536

FAX : 03-3556-1916

Email : collier@nii.ac.jp

Near Real Time Public Health Protection
with DIZIE and BioCaster
Nigel COLLIER, Son DOAN, Bao-Khanh Ho VO

http://born.nii.ac.jp
BioCaster’s Semantic analysis pipeline

What core technologies do you use?
In BioCaster we are exploring a range advanced
algorithms for intelligent text processing over very large
data sets using optimized feature selection. Key tasks
include text classification, terminology recognition, event
extraction, event alerting and visualization. Underlying
the whole system is a multi-lingual ontology – theBioCaster Ontology or BCO. The BCO is freely available
to download and contains a wealth of structured
terminology in many languages related to infectious
diseases.
How are you using social media?

How can I use BioCaster data in my own research?
This year for the first time we launched a new databaseof public health events called GENI-DB. Here you canfind downloadable data for over 176 infectious diseases
and chemicals affecting human and animal health. The
data is an aggregated summary of reports in the world’s
news media in 10 languages.

Recent studies by ourselves and others have shown astrong correlation between social networking messages
and national influenza rates. In the DIZIE project wehave expanded on this to develop an automated text
mining system that classifies and aggregates Twitter
messages in real time. Messages are classified
according to six types of diseases: respiratory,gastrointestinal, neurological, rash, constitutional andhemorrhagic. Results are shown on a novel radial
interface for 40 major world cities.

Key Reference
2. Collier, N. and Doan, S. (2012)“GENI-DB: a database of global
events for epidemic intelligence”,
BioInformatics, 28(8): 1186-8,
Oxford University Press.

Who are you working with?

Partnership is central to our goal in improving health andsafety and making sure that our results are accurate anduseful. We are working with a number of international
public health organizations including: the World HealthOrganisation, the European Centres for Disease Control,
the US Centers for Disease Control, the Ministry ofHealth in Japan, the Health Protection Agency in the UK,
the European Commission’s DG SANCO and Public
Health Canada. Technology partners include: Kasetsart
Key Reference
University (Thailand), Viet Nam National University and3. Collier, N. and Doan, S. (2012)“Syndromic classification of Twitter Okayama University.
messages”, Lecture Notes of theFrom 2009 to 2012 BioCaster was supported by grantInstitute for Computer Sciences,
Social Informatics andin-aid from the JST Sakigake fund. DIZIE is supported
Telecommunications Engineering, by grant-in-aid from NII’s Grand Challenge Project fund.
vol. 91. pp. 186-195. Springer.

連絡先： Nigel COLLIER ／ 国立情報学研究所 Principles of Informatics, Associate Professor
TEL : 03-4212-2536

FAX : 03-3556-1916

Email : collier@nii.ac.jp