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Talk by Carl koppeschaar on the 1st Symposium of Big Data and Public Health, 2013
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One Lunar Health
One Lunar Health
Carl KoppeschaarCarl KoppeschaarBDPH 56, Moon City, 25 October 2069BDPH 56, Moon City, 25 October 2069
Energy crisis
• Needed in 2080/90: 98 TW energy
• Available: 90 TW
• Possible end of industrial development
• Solution: extraterrestrial energy source
Energy from the Moon
• Solar energySolar energy
• Helium-3
Lunar Solar Power
1 tonne helium-3 =
130 milion barrels oil =
$ 300 billion
100 tonnes helium-3 =
$ 30 trillion
Costs of 5 space shuttle loads of 20 tons: $ 5 billion
Lunar Explorers Society
Road map to the lunar future
• 2002-2010: satellites and landers
• 2010-2015: robot missions
• 2015: manned landings
• 2020: permanently manned moonbase
• 2030: more moonbases and industry
• 2040: lunar villages
• 2060: lunar cities
• 2069: Republic of the Moon
2069 Lunar Olympic Games
Needed: Mass Gathering Medicine
Continuous surveillance for outbreaks of infectious diseases
One Martian Health
One Alien Health …
There might be zillions of viruses and other pathogens out there!
Carl Koppeschaar
Big Data and Public Health, Rio de Janeiro – October 25, 2013
Disease Radar: self-reported participatory surveillance for influenza and other diseases.
2003: The Great Influenza Survey
Project to raise the public awareness on flu
Interactive and participatory combination of science and communication informing the general audience on influenza
Inviting people to become ‘flu-reporters’, filling in their health status voluntarily every week in order to help researchers in finding more information on the spread of the influenza virus
Recruitment
Media attention Posters, flyersSchool materialFlu games
How to keep participants ?
Weekly newsletters with the latest ‘flu news’
Informative website: offering ‘flu news’, ‘flu games’, background information, expert interviews, free educational material at all levels for downloading, etc.
Focus on different target groups: laymen, press, school children and their teachers, families, and to a smaller extent, professional health care workers
Communicate results: participants help scientists
Reliable and easily accessible information: expert proven information, maps and graphs
Fast and simple survey
Postal code
Age
Smoker
Transportation
Vaccine
Allergy
…
…
Symptoms
Cough
Fever
Sneezing
Muscle pain
...
Start of symptoms
GP consultation
Single intake questionnaire:
Weekly newsletter + personal symptom’s questionnaire:
Community of ‘flu-reporters’
Blue = common cold
Red = ILI (influenza-like illness)
Participants in all age groups
Correction for age
Loyal participation
Early results
Bicycle
car
Public transport
With pet animals
Without pet animals
Families with children
Families without children
“Female flu”
Incidence in different age groups
Risk groups in smoking, chronic diseases, but
not in terms of transport means!
Significantly more ILI in:
• children: OR = 1.8 [1.7-2.0]
• parents: OR = 1.4 [1.4-1.5]
Regional transmission of the flu
Flu activity by region
Start of the seasonal epidemic
Baseline
Early signalOnset epidemic
> 350 (95%)
> 500 (99%)
Early media attention
Bias in GP’s reporting (1)
Visits many days after start of illness
Bias in GP’s reporting (2)
Seniors more often visit their GP
Bias in GP’s reporting (3)
Changes in visits to GP due to media reporting (2009 pandemic)
Faster than GP’s sentinel posts
A country like the US would need at least 400,000 participants to obtain similar results!
How many subgroups of the population do we need to obtain reliable results?
The Netherlands: on average more than 2 weeks
What can still be improved?1. Number of participants
2. Daily reporting
3. Children
Number of participants
Number of participants per country
1. Number of participants
2. Daily reporting
3. Children
Number of participants
Italy: Low reliability at 0.002% of the population
Number of participants
Netherlands and Belgium: e-mail reminders + news letters sent out through the week
Transmission on a European scale
From west to east and from south to northPaget WJ, Marquet R, Meijer A, Van der Velden J: Influenza activity in Europe during eight seasons (1999-2007): an evaluation of the indicators used to measure activity and an assessment to the timing, lenght and course of peak
activity (spread) across Europe. BMC Infectious Diseases, 2007; 7: 141.
What is the true role of transportation?
Van den Broeck, Gioannini, Gonçalves, Quaggiotto, Colizza, Vespignani: The GLEaMviz computational tool, a publicly available software to explore realistic epidemic spreading scenarios at the global scale. BMC Infect. Dis. 11:37. 2011.
Khan, Arino, Hu, Raposo, Sears, Calderon, et al.: Spread of a novel influenza A (H1N1) virus via global airline transportation. N. Engl. J. Med. 361(2): 212–4. 2009.
Sander van Noort, De Grote Griepmeting/Gripenet
1. Nursery school (crèche, Kindergarten)2. Brothers and sisters => primary schools3. Mothers (traditional role)4. Fathers (commuters)
How does seasonal flu spread?
Sander van Noort
Seasonal flu as a winter disease
Lipid ordering may contribute to viral stability at lower temperatures which is critical for airborne transmission
Flu viruses survive longer and are more easily transmitted when humidity levels are low
Sander van Noort
Hurricanes and monsoonsInfluenza activity appears to coincide with the rainy season in some tropical countries
Do Earth’s seasons cause a “flu conveyor belt”?
Rambaut, Pybus, Nelson, Viboud, Taubenberger, Holmes:The genomic and epidemiological dynamics of human influenza A virus. Nature 453 (7195): 615–9. 2008.
Bahl, Nelson, Chan, et al. Temporally structured metapopulation dynamics and persistence of influenza A H3N2 virus in humans. Proc Natl. Acad Sci. USA 108(48):19359–64. 2011.
• Influenza is quite likely to be under-reported in the tropics because there are so many other more serious diseases.
• Flu is often being mistaken for malaria in the tropics.
• Assumptions about the low impact of flu in the tropics may also be due to outbreaks which happen at unpredictable and irregular intervals.
• In most tropical countries collecting data is not easy.
Data on tropical influenza remain scarce!
Cécile Viboud, Wladimir J. Alonso, Lone Simonsen: Influenza in Tropical Regions. PLoS Medicine, March 7, 2006.
“Highways’ in a global circulation pattern
Portugal Belgium Netherlands
Lisbon, February 2008 (Epi-Forecast)
“A multidisciplinary research effort aimed at developing the appropriate framework of tools and knowledge needed for the design of epidemic forecast infrastructures to be used by epidemiologists and public health scientists.”
Influenzanet.eu
2012/13
2013
Denmark, 2013
2013
More than ILI alone
Individual symptoms
Vaccination uptake
Risc groups
Vaccine efficiency
Side effects of the flu jab
Where to focus next?
• Contact paternsMobile apps, Facebook, Twitter
• Swabs for virologySweden, Belgium 2012
• Survey: social and societal impacts of outbreaks of re-emerging infectious diseases (proposal phase)
• Cooperation with non-European countriesVS (Flu Near You), Australia (Flu Tracking)… Central America, Brasil, Asia, India, Africa
• One Health approachHuman (infectious) diseases, slow epidemics, zoonoses
Flu app
Flu mobile app
Flu app
Full medical apps Lab on a chip
Future technology
International conferencesDigital Disease Detection I, Harvard Medical School, Boston, USA
International Workshop on Participatory Surveillance I, San Francisco, USA
Prince Mahidol Award Conference 2013, Bangkok, Thailand
4th International Meeting on Emerging Diseases and Surveillance - IMED 2013, Vienna, Austria
International Workshop on Participatory Surveillance II, Amsterdam, the Netherlands
WWW 2013 - Participatory Health in the Digital Age, Rio de Janeiro, Brasil
International Workshop on Digital Epidemiology, Torino, Italy
EPIHACK, Phnom Penh, Cambodia
Digital Disease Detection II, San Francisco, USA
Big Data and Public Health, Rio de Janeiro, Brasil
International Workshop on Participatory Surveillance, July 2012
“I am thrilled! I’m witnessing the birth of a new science.
I foresee a whole new magazine, on self-reported participatory surveillance."
Larry Brilliant
AMSTERDAM, 15-17 APRIL 2013
2nd International Workshop on Participatory Surveillance
2nd International Workshop on Participatory Surveillance (IWOPS 2), Amsterdam, April 2013
Influenzanet (EU) – Flu Near You (USA) – Flutracking (Australia)
Platform for seasonal influenza
Checklist for early signals of outbreaks
AMSTERDAM, 15-17 APRIL 2013
2nd International Workshop on Participatory Surveillance
EPIHACK, Phnom Penh, August 2013
AMSTERDAM, 15-17 APRIL 2013
2nd International Workshop on Participatory Surveillance
Doctor Me (Thailand)
AMSTERDAM, 15-17 APRIL 2013
2nd International Workshop on Participatory Surveillance
Flu surveillance network organization
Great Pneumonia Survey (GLM)
GLM- Real Time Monitoring of Community Acquired Pneumonia
Week 1 2013
Week 2 2013
Week 3 2013
Week 4 2013
GLM : Goals
Scientific goals:
• Early detection of abnormal repiratory infectious “outbreaks”• Measuring the impact of CAP in the Dutch population• Exploring seasonal influences on infectious respiratory disease• Exploring effect of pneumococcal vaccination on disease impact
Public information goal:
• Informing patients and health care workers on infectious respiratory disease
GLM - Figures
• 24 Months online
• 1,724 unique participants
• 35 % female, 65% male• Mean age 66 yrs (SD 17)
• 13,000 measurements
GLM – Take home messages
• Real time monitoring system for Community Acquired Pneumonia
• Possible tool for early detection of legionella and Q-fever
• Scientific analyses in progress: Publication of 1st results Dec. 2013
More info (Dutch): www.degrotelongontstekingmeting.nl
GLM - Team
Carl KoppeschaarScience & content
Ronald SmallenburgFinance & organisation
Antwan WiersmaWebmaster & technical support
Dirk-Jan EnklaarAnalyses & reports
Advisory Board: Prof. Dr. Marc J.M. Bonten, Dr. Menno M. van der Eerden, Prof.dr. Jan C. Grutters, Dr. René E. Jonkers, Prof. Dr. Mattijs E. Numans, Prof. Dr. Jan M. Prins, Prof. Dr. Theo M.J. Verheij
“Disease radar”
(Infectious) diseases & behaviour
1. Self diagnosis
2. Surveillance of pertussis and mumps (waning immunities), Lyme, hay fever, norovirus, Q fever, etc.
3. Stress related to labor, slow epidemics (obesity)
4. Medication and side effects
Prediagnostic tool
(in close cooperation with the Dutch College of General Practitioners (NHG)
Lifestyle
Test yourself
Medical encyclopedia
Mobile app
Discussion forum
Top ten of health issues
Real time maps
Also includes zoonoses
Over 60% of human pathogens originate from animals: influenza virus H5N1, H3N7, anthrax, SARS, HIV, leptospirosis, rabies, Lyme, Nipah virus, dengue, malaria, hantavirus, MERS coronavirus, …
Lifestyle & Prevention
Online Health Community
Robust system• Participatory
• Real time
• Geographic information
• Integrated
• Threat verification
• Early signal detection
Integrated:
National institute for Public Health
Community Health Services
College of General Practitioners
Ministry of Health
ProMed, HealthMap, CORDS
CDC, ECDC, WHO, FAO
With our Disease Radar we want to build an
Threat verification (1)Measles in the Netherlands
Threat verification (2)Mumps amongst students in the Netherlands
Threat verification (3)Q fever in the Netherlands
> 4,000 sick
19 fatal
> 800 chronic
Retrospective analysis of hospital discharge data [van den Wijngaard et al. 2011 Epi. & Inf.] showed several plausible Q-fever clusters preceding the recognised beginning of the outbreak in 2007, 2006 and even in 2005, suggesting that had real-time syndromic surveillance been in place, the Q-fever clusters could have been detected up to two years earlier.
Early signal detectionInfluenzanet.eu
Analysis:
Sander van Noort
Compare with Google Flutrends
Sustainability
• Government
• Pharmaceutical companies
• Advertising
• Grants
• Health insurance companies
• Foundations
Millions of insured persons
Economic crisis
Less money available for PR
Small money
Zoosurv in the Netherlands?
These could help a lot
Disease Radar could have been in operation more than a year ago should we have had the proper funding!
References
R.L. Marquet, A.I.M. Bartelds, S.P. van Noort, C.E. Koppeschaar, J. Paget, F.G. Schellevis, J. van der Zee: Internet-based monitoring of influenza-like illness (ILI) in the general population of the Netherlands during influenza seasons 2003-2004, BMC Public Health 2006, 6:242.
S.P. van Noort, M. Muehlen, H. Rebelo de Andrade, C. Koppeschaar, J.M. Lima Lourenço, M.G.M. Gomes: Gripenet: an internet-based system to monitor influenza-like illness uniformly across Europe, Eurosurveillance, Volume 12, Issue 7-8, July/August, 2007.
IHM Friesema, CE Koppeschaar, GA Donker, F Dijkstra, SP van Noort, R Smallenburg, W van der Hoek, MAB van der Sande: Internet-based monitoring of influenza-like illness in the general population: experience of five influenza seasons in the Netherlands, Vaccine, Volume 27, Number 45, 23 October 2009, pp. 6353-6357. ISSN 0264-410X.
Sander P. van Noort, Ricardo Águas, Flávio Coelho, Cláudia Codeço, Daniela Paolotti, Carl E. Koppeschaar & M. Gabriela M. Gomes: Influenzanet: ILI trends, behaviour and risk factors in cohorts of internet volunteers, 2003 - 2013. In revision.
Marit M.A. de Lange, Adam Meijer, Ingrid H.M. Friesema, Gé A. Donker, Carl E. Koppeschaar, Wim van der Hoek: Comparison of five surveillance systems of influenza-like illness during the influenza A(H1N1)pdm09 virus pandemic and their link to media attention. BMC Public Health, 2013, 13:881 doi:10.1186/1471-2458-13-881.
Paolo Bajardi, Daniela Paolotti, Lorenzo Richiardi, Alessandro Vespignani, Sebastian Funk, Ken Eames, John Edmunds, Clement Turbelin, Marion Debin, Vittoria Colizza, Ronald Smallenburg, Carl Koppeschaar, Ana Franco, Vitor Faustino, Annasara Carnahan: Effect of recruitment methods on attrition in Internet-based studies. Submitted.
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