Talk by Carl koppeschaar on the 1st Symposium of Big Data and Public Health, 2013

1. One Lunar Health
One Lunar Health
Carl Koppeschaar
BDPH 56, Moon City, 25 October 2069

2. Energy crisis
•
•
•
•
Needed in 2080/90: 98 TW energy
Available: 90 TW
Possible end of industrial development
Solution: extraterrestrial energy source

3. Energy from the Moon
• Solar energy
• Helium-3

4. Lunar Solar Power

7. 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

10. Lunar Explorers Society

11. 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

17. 2069 Lunar Olympic Games
Needed: Mass Gathering Medicine

18. Continuous surveillance for
outbreaks of infectious diseases

19. One Martian
Health

22. One Alien Health …
There might be zillions of
viruses and other pathogens
out there!

23. Disease Radar: self-reported participatory surveillance for influenza
and other diseases.
Carl Koppeschaar
Big Data and Public Health, Rio de Janeiro – October 25, 2013

24. 2003: The Great Influenza Survey

25. 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

26. Recruitment
Media attention
Posters, flyers
School material
Flu games

27. 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

29. Fast and simple survey
Single intake
questionnaire:
Postal code
Age
Weekly newsletter
+ personal
symptom’s
questionnaire:
Symptoms
Smoker
−
Cough
Transportation
−
Fever
Vaccine
−
Sneezing
Allergy
−
Muscle pain
−
...
…
…
Start of symptoms
GP consultation

30. Community of ‘flu-reporters’
Blue =
common cold
Red = ILI
(influenza-like
illness)

31. Participants in all age groups

32. Correction for age

33. Loyal participation

34. Early results
With pet animals
Without pet animals
car
Bicycle
Public transport
Families with children
Families without children

35. “Female flu”

36. Incidence in different age groups

37. 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]

38. Regional transmission of the flu

39. Flu activity by region

40. Start of the seasonal epidemic
Early signal
> 350 (95%)
Baseline
Onset epidemic
> 500 (99%)

41. Early media attention

43. Bias in GP’s reporting (1)
Visits many days after start of illness

44. Bias in GP’s reporting (2)
Seniors more often visit their GP

45. Bias in GP’s reporting (3)
Changes in visits to GP due to media
reporting (2009 pandemic)

46. Faster than GP’s sentinel posts
The Netherlands: on average more than 2 weeks
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?

47. What can still be improved?
1. Number of participants
2. Daily reporting
3. Children
Number of
participants
per country
Number of participants

48. Italy: Low reliability at
0.002% of the population
Daily reporting
1. Number of participants
2.
3. Children
Number of participants

49. Netherlands and Belgium: e-mail reminders
+ news letters sent out through the week
Number of participants

50. Transmission on a European scale

51. From west to east and from south to north
Paget 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.

52. What is the true role of transportation?
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.
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.
Sander van Noort, De Grote Griepmeting/Gripenet

53. How does seasonal flu spread?
1.
2.
3.
4.
Nursery school (crèche, Kindergarten)
Brothers and sisters => primary schools
Mothers (traditional role)
Fathers (commuters)
Sander van Noort

54. Seasonal flu as a winter disease
Lipid ordering may contribute to viral stability at lower
temperatures which is critical for airborne transmission
Sander van Noort
Flu viruses survive longer and are more easily transmitted
when humidity levels are low

55. Hurricanes and monsoons
Influenza activity appears to coincide with the rainy season in
some tropical countries

56. 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.

57. Data on tropical influenza remain scarce!
• 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.
Cécile Viboud, Wladimir J. Alonso,
Lone Simonsen: Influenza in Tropical
Regions. PLoS Medicine, March 7,
2006.

58. “Highways’ in a global
circulation pattern

59. Portugal
Belgium
Netherlands

60. Lisbon, February 2008
(Epi-Forecast)

61. “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.”

62. Influenzanet.eu
Denmark,
2013
2013
2013
2012/13

63. More than ILI alone

65. Individual symptoms

66. Vaccination uptake

67. Risc groups

68. Vaccine efficiency

69. Side effects of the flu jab

70. Where to focus next?
• Contact paterns
Mobile apps, Facebook, Twitter
• Swabs for virology
Sweden, Belgium 2012
• Survey: social and societal impacts of outbreaks of re-emerging
infectious diseases (proposal phase)
• Cooperation with non-European countries
VS (Flu Near You), Australia (Flu Tracking)… Central America, Brasil,
Asia, India, Africa
• One Health approach
Human (infectious) diseases, slow epidemics, zoonoses

71. Flu mobile app
Flu
app

72. Future technology
Full medical apps
Lab on a chip
Flu
app

73. International conferences
Digital 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

74. International Workshop on
Participatory Surveillance, July 2012
Larry
Brilliant
“I am thrilled! I’m
witnessing the
birth of a new
science.
I foresee a whole
new magazine,
on self-reported
participatory
surveillance."

75. 2nd International Participatory
2nd International Workshop onWorkshop
Surveillance (IWOPS 2), Amsterdam, April 2013
on Participatory Surveillance
Influenzanet (EU) – Flu AMSTERDAM, 15-17 APRIL 2013
Near You (USA) – Flutracking (Australia)

76. Platform for seasonal influenza

77. Checklist for early signals of outbreaks

78. 2nd International Workshop
EPIHACK, Phnom Penh, August 2013
on Participatory Surveillance
AMSTERDAM, 15-17 APRIL 2013

79. 2nd International Workshop
on Participatory Surveillance
Doctor Me
(Thailand)
AMSTERDAM, 15-17 APRIL 2013

80. 2nd International organization
Flu surveillance network Workshop
on Participatory Surveillance
AMSTERDAM, 15-17 APRIL 2013

81. Great Pneumonia Survey (GLM)

82. GLM- Real Time Monitoring of
Community Acquired Pneumonia
Week 1 2013
Week 2 2013
Week 3 2013
Week 4 2013

83. 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

84. GLM - Figures
•
24 Months online
• 1,724 unique participants
• 35 % female, 65% male
• Mean age 66 yrs (SD 17)
• 13,000 measurements

85. 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

86. GLM - Team
Carl Koppeschaar
Science & content
Antwan Wiersma
Webmaster &
technical support
Ronald
Smallenburg
Finance &
organisation
Dirk-Jan Enklaar
Analyses & 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

88. “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

89. Real time
maps
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

90. 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, …

91. Lifestyle & Prevention

92. With our Disease Radar we want to build an
Online Health Community
Robust system
Integrated:
• Participatory
National institute for Public Health
• Real time
Community Health Services
• Geographic information
College of General Practitioners
• Integrated
Ministry of Health
• Threat verification
ProMed, HealthMap, CORDS
• Early signal detection
CDC, ECDC, WHO, FAO

93. Threat verification (1)
Measles in the Netherlands

94. Threat verification (2)
Mumps amongst students in the Netherlands

95. Threat verification (3)
Q fever in the Netherlands
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 Qfever clusters could have been detected up to two years earlier.
> 4,000 sick
19 fatal
> 800 chronic

96. Early signal detection
Influenzanet.eu
Analysis:
Sander van Noort

97. Compare with Google Flutrends

98. Sustainability
Disease Radar could have been in operation more than a
year ago should we have had the proper funding!
• Government
Economic crisis
• Pharmaceutical companies
Less money available for PR
• Advertising
Small money
• Grants
Zoosurv in the Netherlands?
• Health insurance companies
Millions of insured persons
• Foundations
These could help a lot

99. 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.

100. Contact:
carl@science-in-action.nl