“Intelligent Use” of Electronic Data to Enhance Public Health Surveillance

Dr. Edward VelascoDepartment of Infectious Disease Epidemiology

Robert Koch Institute (Public Health Agency of Germany)

Berlin

2

~5 Days tEvent arises

Established surveillance systems: SurvNet, targeted surveillance systems (e.g. sentinels)

3 Days

Existing event-based services: EWRS, government websites,ProMed Mail, MedISys, news

What is important from our perspective?

2 Days

Web 2.0 and user generated sources ?

Time needed to spot an infectious disease health event

M-Eco (Medical Ecosystem)

EU 7th Framework project international project, 2010-2012

Goal generate, extract, organise and present viable information from Internet data for public health surveillance and early warning of infectious diseases

User specifications

4

1.Data is generated from the Web in a timely and specific way Websites: news, publications, Web 2.0 and user-generated sources (social media, Twitter, Facebook, blogs)

2. Data is extracted & organisedEpidemiological information is extracted and personalised based on signal needs: time frame, place, symptoms , etc.

3. Data is made easily available and user-friendlyVisualisation possibilities for time and geographic analysis

Data generation, extraction, organisation, presentation

How to Exploit Twitter for Public Health Monitoring? GMDS 2012 – Medical Informatics, Medicine and Neighboring Disciplines. K. Denecke (1, 2), M. Krieck (3), L. Otrusina (4), P. Smrz (4), P. Dolog (5), W. Nejdl (2), E. Velasco (6)

Innovation: Intelligent data extraction

• Media & text mining- Keyword identification, semantic trees

How to Exploit Twitter for Public Health Monitoring? GMDS 2012 – Medical Informatics, Medicine and Neighboring Disciplines. K. Denecke (1, 2), M. Krieck (3), L. Otrusina (4), P. Smrz (4), P. Dolog (5), W. Nejdl (2), E. Velasco (6)

Innovation: Algorithmic automation, interactive data presentation

How to Exploit Twitter for Public Health Monitoring? GMDS 2012 – Medical Informatics, Medicine and Neighboring Disciplines. K. Denecke (1, 2), M. Krieck (3), L. Otrusina (4), P. Smrz (4), P. Dolog (5), W. Nejdl (2), E. Velasco (6)

Tag cloud

Signal list

Interactive epicurve/timeline

Evaluation 1: How well does the system generate signals?

General simulation with Twitter 13 scientists created tweets for mock scenarios:

– A. Measles in a school– B. Salmonella at Eurocup– C. Hepatitis A: returning travellers

Tweets were fed into M-Eco, mixed with real-world tweets and analysed

Only 1/3 became part of signal (21%): this is low! 75-80% expected– Keywords not comprehensive enough: slang does not equal

medical terms in our keywords lists– Geolocation is difficult to aggregate, missing information– German language?

Evaluation 2: How does the system perform in real time?

Real signal production during mass gathering: Euro 2012,

men‘s football championship, Poland/Ukraine

Signals provided to „subscribers“ at RKI and NLGA– Daily monitoring alongside regular work

20 signals avg/day; 242 signals total– 13 total relevant over the event-time: this is low!

Again: Problems with keywords/terms: slang or off-use of terms „football fever“ „weakness“ of players“ or „headache“ from poor performance

Evaluation 3: Signal production over 3 weeks

Weekend!

Media coverage of flu-shot shortages!

Unresolved challenges

Information is not always moderated by professionals or interpreted for relevance before it is disseminated to epidemiologists, i.e. tweets, media reports

Automation: no standardized system for updates, often resulting in too much information

Algorithms and statistical baselines are not well developed New information about health events is not disseminated in

the most efficient way

12

Interdisciplinary challenges ahead

Social aspects– Privacy and data protection - legal/ethical concerns over

data access?– Artificial cognition systems

Bridging with traditional epidemiology– Evolving stakeholder roles– Data mining, automated systems replace people?– Comparison to traditional surveillance data unexplored– Big data: Unknown infrastructural investments for data

storage

13

Thank you

Some references for your review:

Social Media and Internet-Based Data in Global Systems for Public Health Surveillance: A Systematic Review. The Milbank Quarterly, Vol. 92, No. 1, 2014 (pp. 7-33) Velasco E, Agheneza A, Denecke K, Kirchner G, Eckmanns T.

How to Exploit Twitter for Public Health Monitoring? Methods of Information in Medicine, Vol. 52, No. 4, 2013 (pp. 326-339) Denecke K, Krieck M, Otrusina L, Smrz P, Dolog P, Nejdl W, Velasco E.

Website: www.meco-project.eu

Contact Edward Velasco, PhD, SM

Department for Infectious Disease Epidemiology

Robert Koch Institute, Berlin

Email: VelascoE@rki.de

14

M-Eco Project, Partners, Consortium and Advisory

FP 7th Framework EU Project, 2010-2012, website: www.meco-project.eu

Computer Science & Information Technology– L3S Research Centre, Leibnitz University Hannover, Germany (LUH) (Partner)– Aalborg University, Intelligent Web and Information Systems, Department of Computer Science,

Denmark (AAU) (Partner)– SAIL Labs, Austria (SAIL) (Partner)– Brno University of Technology, Faculty of Information Technology, Czech Republic (BUT) (Partner)– Joint Research Centre, European Commission, Italy (JRC) (Partner)

Epidemiology and Surveillance– State Public Health Agency of Lower Saxony, Germany (NLGA) (Partner)– Robert Koch Institute, Department for Infectious Disease Epidemiology, Surveillance Unit, Germany

(RKI) (Partner)– Health Protection Agency, UK, (HPA) (Advisory)– Institut de Veille Sanitaire, France, (INVS) (Advisory)– European Centre for Disease Prevention and Control, Stockholm, (ECDC) (Advisory)– Global Alert and Response, World Health Organization, Switzerland, (WHO) (Advisory)