Carl koppeschaar: Disease Radar: Measuring and Forecasting the Spread of Infectious Diseases and Zoonoses


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

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Carl koppeschaar: Disease Radar: Measuring and Forecasting the Spread of Infectious Diseases and Zoonoses

  1. 1. One Lunar Health One Lunar Health Carl Koppeschaar BDPH 56, Moon City, 25 October 2069
  2. 2. Energy crisis • • • • Needed in 2080/90: 98 TW energy Available: 90 TW Possible end of industrial development Solution: extraterrestrial energy source
  3. 3. Energy from the Moon • Solar energy • Helium-3
  4. 4. Lunar Solar Power
  5. 5. 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
  6. 6. Lunar Explorers Society
  7. 7. 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
  8. 8. 2069 Lunar Olympic Games Needed: Mass Gathering Medicine
  9. 9. Continuous surveillance for outbreaks of infectious diseases
  10. 10. One Martian Health
  11. 11. One Alien Health … There might be zillions of viruses and other pathogens out there!
  12. 12. Disease Radar: self-reported participatory surveillance for influenza and other diseases. Carl Koppeschaar Big Data and Public Health, Rio de Janeiro – October 25, 2013
  13. 13. 2003: The Great Influenza Survey
  14. 14. 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
  15. 15. Recruitment Media attention Posters, flyers School material Flu games
  16. 16. 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
  17. 17. 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
  18. 18. Community of ‘flu-reporters’ Blue = common cold Red = ILI (influenza-like illness)
  19. 19. Participants in all age groups
  20. 20. Correction for age
  21. 21. Loyal participation
  22. 22. Early results With pet animals Without pet animals car Bicycle Public transport Families with children Families without children
  23. 23. “Female flu”
  24. 24. Incidence in different age groups
  25. 25. 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]
  26. 26. Regional transmission of the flu
  27. 27. Flu activity by region
  28. 28. Start of the seasonal epidemic Early signal > 350 (95%) Baseline Onset epidemic > 500 (99%)
  29. 29. Early media attention
  30. 30. Bias in GP’s reporting (1) Visits many days after start of illness
  31. 31. Bias in GP’s reporting (2) Seniors more often visit their GP
  32. 32. Bias in GP’s reporting (3) Changes in visits to GP due to media reporting (2009 pandemic)
  33. 33. 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?
  34. 34. What can still be improved? 1. Number of participants 2. Daily reporting 3. Children Number of participants per country Number of participants
  35. 35. Italy: Low reliability at 0.002% of the population Daily reporting 1. Number of participants 2. 3. Children Number of participants
  36. 36. Netherlands and Belgium: e-mail reminders + news letters sent out through the week Number of participants
  37. 37. Transmission on a European scale
  38. 38. 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.
  39. 39. 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
  40. 40. 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
  41. 41. 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
  42. 42. Hurricanes and monsoons Influenza activity appears to coincide with the rainy season in some tropical countries
  43. 43. 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.
  44. 44. 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.
  45. 45. “Highways’ in a global circulation pattern
  46. 46. Portugal Belgium Netherlands
  47. 47. Lisbon, February 2008 (Epi-Forecast)
  48. 48. “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.”
  49. 49. Denmark, 2013 2013 2013 2012/13
  50. 50. More than ILI alone
  51. 51. Individual symptoms
  52. 52. Vaccination uptake
  53. 53. Risc groups
  54. 54. Vaccine efficiency
  55. 55. Side effects of the flu jab
  56. 56. 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
  57. 57. Flu mobile app Flu app
  58. 58. Future technology Full medical apps Lab on a chip Flu app
  59. 59. 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
  60. 60. 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."
  61. 61. 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)
  62. 62. Platform for seasonal influenza
  63. 63. Checklist for early signals of outbreaks
  64. 64. 2nd International Workshop EPIHACK, Phnom Penh, August 2013 on Participatory Surveillance AMSTERDAM, 15-17 APRIL 2013
  65. 65. 2nd International Workshop on Participatory Surveillance Doctor Me (Thailand) AMSTERDAM, 15-17 APRIL 2013
  66. 66. 2nd International organization Flu surveillance network Workshop on Participatory Surveillance AMSTERDAM, 15-17 APRIL 2013
  67. 67. Great Pneumonia Survey (GLM)
  68. 68. GLM- Real Time Monitoring of Community Acquired Pneumonia Week 1 2013 Week 2 2013 Week 3 2013 Week 4 2013
  69. 69. 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
  70. 70. GLM - Figures • 24 Months online • 1,724 unique participants • 35 % female, 65% male • Mean age 66 yrs (SD 17) • 13,000 measurements
  71. 71. 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):
  72. 72. 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
  73. 73. “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
  74. 74. 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
  75. 75. 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, …
  76. 76. Lifestyle & Prevention
  77. 77. 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
  78. 78. Threat verification (1) Measles in the Netherlands
  79. 79. Threat verification (2) Mumps amongst students in the Netherlands
  80. 80. 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
  81. 81. Early signal detection Analysis: Sander van Noort
  82. 82. Compare with Google Flutrends
  83. 83. 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
  84. 84. 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.
  85. 85. Contact: