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John Brownstein, PhDAssociate Professor
Children’s Hospital Boston, Harvard Medical School@johnbrownstein
Collaborative Platforms for Disease Surveillance
2nd Annual ESRI Health GIS Conference
May 2003Arlington, VA
Mapping West Nile Virus risk: Evaluating surveillance and environmental Data
Emerging and re‐emerging infections, 1996‐2010
Venezuelan Equine EncephalitisDengue Haemorrhagic Fever
CryptosporidiosisHuman MonkeypoxE.Coli O157
nvCJD
Ebola Haemorrhagic FeverMarburg Haemorrhagic FeverRoss River VirusHendra VirusReston Virus
West Nile VirusLegionnaire’s DiseaseSevere Acute Respiratory
Syndrome (SARS)MalariaTyphoidCholeraBSELassa FeverYellow Fever
Lyme BorreliosisEchinococcosisDiphtheriaInfluenza A (H5N1)Nipah VirusRVF/VHFO’Nyong‐Nyong FeverBuruli UlcerMultidrug Resistant Salmonella
Outbreak Database (1996‐2009)
• Disease / Location• Date of onset of risk factor• Date of local mass gathering• Date of associated wildlife
outbreak• Date of exposure• Date of symptom onset• Date of outbreak start• Date of hospitalization or
medical visit• Date of outbreak detection• Date of death
• Date of laboratory confirmation
• Date of announcement by a local
• Date of any earlier mentioned report
• Date of ProMED, GPHIN, HealthMapreports
• Date of WHO notification
• Date of DON report (official)
• Date of mass immunization campaign
• Date of implementation of vector control)
• Date of declaration of an epidemic raised
• Date of declaration of end of epidemic
Δt2
Characterize global spatial‐temporal trends in the timeliness of outbreak detection and reporting
Outbreakstart
Δt1
Outbreakdiscovery
Publiccommunication
Global Surveillance Capacity Assessment
48 days (95% C.I. [40;56])
35 days (95% C.I. [32;47])
32 days (95% C.I. [28;38.5])
23 days (95% C.I. [18;30])
MEDIAN
Figure 2 . Boxplots of the median time (and inter‐quartile range) between outbreak start and various outbreak “milestones” for a set of WHO‐confirmed outbreaks during 1996‐2009.
Chan et al. 2010. Proceedings of the National Academy of Sciences.
World Bodies
(UN, WHO, FAO, OIE)
5
Ministry of
Health
4
Local Officials
Labs
3
Public health
practitioners
Healthcare workers,Clinicians
2
Public
1
Traditional Public Health Reporting
Public
1
Public health
practitioners
Healthcare workers,Clinicians
2
Local Officials
Labs
3
Ministry of
Health
4
World Bodies
(UN, WHO, FAO, OIE)
5
World Bodies
(UN, WHO, FAO, OIE)
Ministry of
Health
Local Officials
Labs
Public health
practitioners
Healthcare workers,Clinicians
Public
Traditional Public Health Reporting
54321
Digital Disease
DetectionWorld Bodies
(UN, WHO, FAO, OIE)
5
epidemic curve
Potential of Digital Disease Detection
SMS Messaging
Micro Blogging
Emailing
Internet Searching
Social Networking
Internet Chatting
Blogging
Online News Reporting
Video/Radio Reporting
Health Expert Reporting
0
50
100
150
200
1996 2000 2004 2008
Number of days from outbreak start to outbreak discovery
Year of Outbreak Start
Time in Day
s
20in 2010
days
167in 1996
days
Chan et al. 2010. Proceedings of the National Academy of Sciences.
The Rise of Digital Disease Detection
1st infectious disease social network
1st infectious disease web crawler
precisely placed in
locations
resulting in
alerts per day
the number of public and private sources we useto access more than 50,000 sites
in 10languagesevery hour
24/7
Typhoid cases in Mufulira have
reached 2, 227 with health authorities
calling for increased efforts to prevent
new infections in Mupambe Township.
Articles are scanned for key information usingnatural language processing
MufuliraTyphoid
MupambeTownship
2, 227
#
4800disease patterns
10,500locations
#Case and Death Counts
species220
Old News
Context
Not disease related
Breaking News
Warning
Articles are categorized using more than 19 million phrases
BayesianFiltering
91% accuracy
with
Breaking News
Warning
Text matching, similarity score, and rating value determine the significance of the alert
Old News
Context
Not disease related
Articles are categorized using more than 19 million phrases
BayesianFiltering
91% accuracy
with
Global spread of H1N1 with informal sources
Brownstein et al. 2010. New England Journal of Medicine.
Number of foodborne illnesses reported each year in the US
By the Centers for Disease Control and Prevention
By HealthMap
1000
10,000
Foodborne Outbreaks Reported to HealthMap in past year
Professional social networks to support disease reporting
Human networks has proven value
Professional networks play key roles in discovery and validation
2 Examples:ProMED ISIDGeoSentinel ISTM
Professional Networks and Epidemic Intelligence
Collaborations
Automated ExtractionAutomated Feed
Community Input
Added Value(1) Discovery of new content(2) Provides two‐way contextualization(3) Validation studies
Next Generation Public Health: Participatory Epidemiology
HealthMapOutbreaks Near Me
reporting
> 100k downloads
AndroidPotential for increased global coverage
HealthMap Hotline
919‐MAP‐1‐BUG (627‐1284)Leave a voicemail or Send SMS
Reporting through the website
worldwide user submitted reports
Chikungunya in India – July 22, 2011
• A London‐based doctor reported via HealthMap’s“Outbreaks Near Me” mobile application that his family in Jamshedpur, Jharkhand, India was ill with Chikungunya
• Report went on to say that local hospitals were full, and that the MOH was not reporting cases or providing information on prevention or treatment
• HM was able to enhance surveillance capabilities for this disease and location, and provide users with this information earlier than any other source
iPhone Submissions vs CDC sentinel surveillance
CDC Sentinel Physician Network (%ILI)Outbreaks Near Me iPhone app (%H1N1 submissions/Downloads)
R2=0.74
Future of Outbreaks Near Me Mobile
Engaging the users throughrecognition
Providing key public healthmessaging
Improved visualization
And building a disease detectivenetwork…
Undiagnosed Events in HealthMap
Validating Undiagnosed Events by Mobile Phone
Validating Undiagnosed Events by Mobile Phone
Geo‐localized push messages Validate event
New Applications of Digital Disease Detection
CDC Yellow Book: Dengue
2011 Yellow Book
2008 Yellow Book Risk Areas2011 Yellow Book Risk Areas
2011 Yellow Book
Model Performance
Cholera Surveillance in Haiti
52
• vcvb
HealthMap Pilot, Haiti
050
015
0025
0035
00
Date
Cum
ulat
ive
Rep
orte
d H
ospi
taliz
atio
ns (M
SP
P)
Oct-20 Nov-17 Dec-22 Jan-19 Feb-16
050
100
150
035
3070
6010
591
HM
Prim
ary
Artic
le V
olum
e
Twitt
er “c
hole
ra” V
olum
e
020
000
6000
010
0000
1400
00Date
Cum
ulat
ive
Rep
orte
d H
ospi
taliz
atio
ns (M
SP
P)
Oct-20 Nov-17 Dec-22 Jan-19 Feb-16
010
0020
0030
0040
0050
00
010
0020
0030
0040
0050
00
HM
Prim
ary
Arti
cle
Volu
me
Twitt
er “c
hole
ra” V
olum
e
Chunara et al. 2012. American Journal of TropicalMedicine and Hygiene.
Using Social Media to Build an Epidemic Curve
Estimate Reproductive Number (R0)
Phase 1: Informal sources 1.54‐6.89 compared to official sources 1.27‐3.72Phase 2: Informal sources 1.04‐1.51 compared to official sources 1.06‐1.73
Case counts Hospitalizations HealthMap Twitter
Chunara et al. 2012. American Journal of TropicalMedicine and Hygiene.
New Directions in Participatory Systems
India Malaria HIT
flunearyou.org
Registration
Facebook integration
Form after illness indication
IndividualTop Awards
GroupTop Awards
Awarded to top three individuals withhighest total number person‐forms
Minimum Threshold eligibility:10,000 person‐forms
(one form allowed per person per week)
Awarded to top three groups withhighest total number person‐forms
divided by number of persons in group
Minimum Threshold eligibility:100,000 person‐forms
(one form allowed per person per week)
APHA Challenge
Age Distributions
Users Household members
Linking Surveillance with Prevention:HealthMap Flu Vaccine Finder
HealthMap Flu Vaccine Finder
Collaboration of over 50,000 pharmacies
Global Official Surveillance Platforms
Ministry of Health Mapping
Automated mining/mapping of over 500 MOH data feeds
Conclusions
• Value in the fusion and visualization of distributed electronic resources (online epidemic intelligence, social networks, mobile technology)
• Novel Internet‐based collaborative systems can play an important complementary role in gathering information quickly and improving coverage and accessibility.
• These early efforts at tapping the power of digital tools demonstrate important steps in improving health systems as well as engaging the public as participants in the public health process.
HealthMap Team
• Clark Freifeld, MA• David Scales, MD PhD• Susan Aman• Leila Amerling, MBA• Emily Chan, MSc• Rumi Chunara, PhD• Annie Gatwood, PhD• Mikaela Keller, PhD• Sumiko Mekaru, DVM, MVM• Katelynn O'Brien• Amy Sonricker, MPH
77
Funding