Global Risk Informatics
Microsoft / Gates Foundation
Debra Goldfarb
Sr. Director, Technical Computing Industry Strategy
The crisis information gap
When the global economic crisis hit in 2008, world
leaders knew they needed to act quickly.
They knew that they needed to take immediate
policy actions to protect communities from
downstream impacts on health, nutrition,
education, jobs, and the environment.
Agile, targeted responses required up-to-date
evidence of how families were coping with shocks.
Sounds pretty straightforward, no?
Household-level stats take months
to collect, and years to validate!
The information gap is real…
?
First data becomes
available
…as are its consequences.
But what if?
Decision makers had access to real-time data and the
tools to detect the early signals ?
Policy-makers and field workers had models to help
uncover the complexities of disease, economic crises,
poverty, civil unrest?
We could tailor interventions based on real data and
analysis?
We could broadly apply simulation and modelling to
global risk to dramatically change outcomes?
Microsoft – Gates Foundation
Collaboration
What are we doing?
Why we care?
What will we learn?
What are the impacts?
How does it fit?
The Bill and Melinda Gates Foundation
Guided by the belief that every life has equal value,
the Bill & Melinda Gates Foundation works to help all
people lead healthy, productive lives. In developing
countries, it focuses on improving people’s health
and giving them the chance to lift themselves out of
hunger and extreme poverty. In the United States, it
seeks to ensure that all people—especially those with
the fewest resources—have access to the
opportunities they need to succeed in school and life.
The Foundation focuses primarily on the “bottom 20”
The Bill and Melinda Gates Foundation
Malaria today
Malaria Burden -2008
•863 000 deaths
•243 million cases
•Half of the world's population is at risk of malaria
Current solution
Tools
Current: LLINs, IRS, ACTs, accurate diagnostics
Future: vaccine, vector compromise, surveillance tools
Strategies for human behavior change
Improve the health systems infrastructure
Economic development
Understand climate change impacts
What motivates the GF?
The Goal: Eradication
Removal/depletion of the last malaria parasite on the
earth
It’s been done before:
•
Smallpox, Rinderpest
•
Guinea Worm, Polio, Measles
Ambiguities/challenges
•
Syndrome vs single disease
•
Animal reservoirs?
•
Latent infections
Malaria modeling: why technical and
high performance computing?
To predict the impact of a particular intervention
To explore the modes of action of specific tools
To evaluate transmission patterns and efforts to
reduce them
To explore economic and public health arguments
for particular eradication strategies
To simulate approaches to eradication and explore
options for achieving it
Malaria Models
Transmission models
Ross McDonald (transmission)
R0: The number of new infections that arise from a single one
Within-host models
Immunity: partial protection in adult humans who survive infancy
Population models
Parasite drug resistance or insecticide resistance in mosquitoes
…and then you add in all the parameters and sub
models: biology, climate, human population models,
environmental, technology, complex relationships, food,
etc.
Modern Malaria Models
Modern range
Simple “ODE” models
Multiparametric MCMC Simulations
Novel modeling approaches
Nested hierarchical models
Computational/statistical innovations
“Network” models of human movement
Different assumptions about underlying biology
Proposed analytical framework incorporates multiple
information sets, enables assessment of vector control
interventions
Integration of community inputs into unified framework
1
Entomology
2
3
Local
environments
Epidemiology
Biting
VS
Indoors Outdoors Dawn Night Dusk
aconitus
1
1
tbd
tbd
tbd
annularis
0
1
0
0
1
campestris
1
1
tbd
tbd
tbd
dirus
1
0
0
1
0
fluviatilis
tbd
tbd
0
0
1
funestus
1
0
1
1
0
gabaldoni
tdb
tdb
tdb
tdb
tdb
jeyporiensis
1
0
0
1
0
lesteri
1
0
tbd
tbd
tbd
maculatus
0
1
0
0
1
Vector species ecology
profiles and ranges
Assembly of regional vector
ecology profiles
Identification of critical data
gaps
Location-specific
stratifications and data
Malaria parasite locations,
rates
Second-wave input
4
Analytical
tools
Interventions
Policies and
regulations
Pat. of use # AIs Resist. Target
IRS
4
1
Adult
Nets (LLIN/ITNs)
0
1
Adult
Space spraying/fogging
2
0
All
Topical Repellants
2
0
Adult
Emanators/coils
0
1
Adult
Larviciding
0
1
Larva
Durable wall lining
1
Adult
2
Topical Repellants
3
tbd
Adult
Intervention profiles, incl.
efficacy and resistance
Assessment of utility
of potential VC interventions
Identification of gaps in current
intervention set
as informant of TPPs
Second-wave output
Regulations,
policies, financing
Supply, demand and financing
assessment
Analytical framework will capture four key types of data
1
Entomology
2
Aggregate vector species
information
Species
Larval Habitats
Rice fields, stream
pools, shaded pools with
grasses.
Rice fields, permanent
water with emergent
vegetation.
An. annularis
Usually deep, brackish
water, ditches, wells with
some vegetation and
An. campestris shade.
Anopheles
aconitus
An. dirus
Isolated stream pools,
undisturbed ground
pools, cisterns.
Local Environments
3
Consolidate multiple locationbased variables
Epidemiology
4
Map against malaria
outbreak data (location, rate)
Interventions
Overlay intervention profiles,
including efficacy info.
Feeding Behavior
Feeds on man and
animals, indoors and
outdoors.
Generally zoophilic,
feeding outdoors before
midnight.
Often anthropophilic,
feeds indoors or
outdoors, bites in
shaded areas.
Highly anthropophilic,
feeds primarily between
2200-0400 hrs indoors
and outdoors.
Target
Paradigm # of AIs Vector age
IRS
4
Adult
Nets
0
Adult
Space spray
2
All
Topical
2
Adult
Coils
0
Adult
Larviciding
4
Larva
Preventive
efficacy
30-75%
40-64%
tbd
tbd
tbd
tbd
Developme
nt status
Current tool
Current tool
Current tool
Current tool
Current tool
Current tool
Primary data components
• List of reproductively isolated
vector groups
• Vector ecology profiles
(biting, resting, breeding sites,
sugar meal source)
• Vector presence coordinates
• Expert-derived vector ranges
•
•
•
•
•
•
•
• Parasite rates and coordinates
Political map
• Expert-derived epidemiological
Precipitation
ranges
Human density estimates
Climate
Topography
Local resistance to active ingredients
Availability of alternative interventions
(e.g., drugs, vaccines)
• Classified list of interventions1
• Efficacy and effectiveness
Secondary components (used to expand and/or refine framework)
• Emergence of new species
• Mating and swarm behavior
• Species genomic data
Key sources for data
•
•
•
•
•
•
Malaria Atlas Project (MAP)
Disease Vector Database
Swiss Tropical Institute / MARA
Walter Reed Biosystematics Unit
VectorBase / Anobase
WHO • AFPMB • ANVR
• Climate change impact
• Human development impact
• Urban, rural, agriculture
stratifications
• Cost constraints
• Infrastructure/accessibility
• Socio-political obstructions
• Relevant cultural mores
• Use patterns for alt. interventions
• Impact of human migration
patterns
• Actual disease burden
• Human and vector host
resistance
•
•
•
•
Compliance
Cost
Impact of educational efforts
Ecological influences on
intervention efficacy
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
WHO
Croplife
IVM evidence committee
STI
Vestergaard-Frandsen
Academic literature
Expert input
WHO
MAP
CIA Factbook
Koppen-Geiger Climate
Classification
• SEDAC (GRUMP)
Malaria Atlas Project (MAP)
WHO
Swiss Tropical Institute
CDC
1. Interventions to be classified by control paradigm, target vector age, active ingredient(s), number of active ingredients, safety, development status and robustness against pyrethroidresistant vectors
Multiple data sets to be combined and integrated
Framework inputs
List of reproduct.
isolated groups
DVD, MAP., STI
Vector presence
coordinates
DVD, MAP ,
Academic lit.
Expert-derived
vector ranges
MAP, Expert input
Political map
Local Environments
MAP
Precipitation
NASA; MAP
Climate
NASA; MAP
Topography
MAP
Hum. population
GRUMP
Local resistance to
AIs
Academic lit.,
Vestergaard-Frandsen,
Altern. interven.
Interventions
Epidemiology
WHO, Academic lit.
Parasite rates and
coordinates
MAP, Academic lit.
Expert-derived
epidem. ranges
MAP, Expert input
Comprehensive
vector ecologies
Regional Vector
Ecology Profiles
Biting
VS
Indoors Outdoors Dawn Night Dusk
aconitus
1
1
tbd
tbd
tbd
annularis
0
1
0
0
1
campestris
1
1
tbd
tbd
tbd
dirus
1
0
0
1
0
fluviatilis
tbd
tbd
0
0
1
funestus
1
0
1
1
0
gabaldoni
tdb
tdb
tdb
tdb
tdb
jeyporiensis
1
0
0
1
0
lesteri
1
0
tbd
tbd
tbd
maculatus
0
1
0
0
1
Vector locations
Country Long Lat
Indonesia
Greece
Saudi
Arabia
China
Brazil
Ethiopia
Ethiopia
Ethiopia
Brazil
Brazil
97.2
26
50.2
109
-62.8
37.5
37.6
37.6
-62.8
-61.9
Species
1.38
40.9
26.3
19.3
-8.7
5.88
6.03
6.03
-9.68
-10.7
sundaicus
superpictus
superpictus
aconitus
albitarsis
arabiensis
arabiensis
arabiensis
albitarsis
albitarsis
MAP, DVD ,
Academic lit.,
Expert ranges
Location-specific
boundaries & data
Country
All Asia
All Asia
All Asia
All Asia
All Asia
All Asia
All Asia
All Asia
Bangladesh
Bangladesh
Bangladesh
Bangladesh
Bangladesh
Bangladesh
Bangladesh
Bangladesh
Bhutan
Ecological stratifications
All ecological stratifications
Plains and valleys
Forest and forest fringes
Highland and desert fringes
Wetland and coastal areas
Urban and peri-urban areas
Agricultural development
Socio-political disturbances
All ecological stratifications
Plains and valleys
Forest and forest fringes
Highland and desert fringes
Wetland and coastal areas
Urban and peri-urban areas
Agricultural development
Socio-political disturbances
All ecological stratifications
MAP, GRUMP WHO,
Academic lit.,
VestergaardFrandsen
Parasite
epidemiology
WHO, STI, Expert
input, academic
literature
Intervention
efficacy
WHO, STI, academic
literature
Vector species
datasets / maps
Integrated
epidemiological
& vector species
datasets / maps
aconitus
crascens
dirus
minimus A
minimus C
scanloni
Integrated
epidemiological
&
entomological
datasets / maps
Thailand
# Vectors
Indoors
Outdoors
Dawn
Night
Dusk
Human
Animal
Sugar meals
No Sugar meals
Resting
Indoors
Outdoors
4
4
0
5
3
6
3
0
0
3
6
Data gaps
Stratification
map
Data type
Vector
bionomics
MAP
MAP
 Searchable
database
and vector
or locationspecific
datasets
 Visual maps
MAP
 Searchable
database
and vector
or locationspecific
datasets
 Visual maps
Data gap
Sugar feeding None
Vector
bionomics
Western
Pacific region
Larvicide
Interventions effectiveness
Space
spraying
Interventions effectiveness
p. ovale
Epidemiology prevalence
MAP
 Searchable
database
and vector
or locationspecific
datasets
 Visual maps
Current efforts to
fill gap?
Malaria Atlas
Project- in progress
Some local
experiments
None
None
Intervention
utility map
Epidemiological map
b. Reported malaria
deaths (annual) -> 2003
Cambodia
Democratic
Republic of
the Congo
Dominican
Republic
Ecuador
Egypt
El Salvador
Eritrea
Ethiopia
492
16,498
16
0
MAP, Academic
lit.,
0
n/a input
Expert
List of
interventions
Vector ecology profile for:
Vector
Ecology features
Species
MAP, WRBU, DVD,
STI
Biting
Entomology
MAP, WRBU, STI
End-user tools
Feeding
Vector ecology
profiles
Intermediate outputs
MAP, WHO, STI
78
n/a
Profiles of current
interventions
Target
Paradigm
# of AIs Vector age
IRS
4
Adult
Nets
0
Adult
Space spray
2
All
Topical
2
Adult
Coils
0
Adult
Larviciding
4
Larva
WHO, Academic lit.,
STI, Expert input
Expert input
Intervention
effectiveness
Paradigm
IRS
Nets
Space spray
Topical
Coils
Larviciding
Biting
indoors
Yes
Yes
Yes
Yes
Yes
Yes
Biting
outdoors
No
No
Yes
Yes
No
Yes
Intervention gap
assessment
Vector ecology or
land ecology
feature
Outdoor biting
Outdoor biting
Outdoor biting
Outdoor biting
Forest environment
Forest environment
Forest environment
Current
intervention
option, if
applicable
Space spraying
Space spraying
Space spraying
Space spraying
None
None
None
Region
affected
Ethiopia
Thailand
India
Brazil
Thailand
India
Brazil
What are we doing?
VCDN consortia member
Develop the “cyber infrastructure,” applications
and tools to enable broad-based sharing of Malaria
data and models; simulation and analysis to drive
positive and predictive outcomes
Components: cloud-based large scale data
integration, collaborative tools, extraction/
modeling/analytic tools, visualization, GISmapping, search, simulation and modeling
Challenges
Data: integrity, formats, ontologies, currency and
curation, security….not to mention the “politics” of
data
Collaboration: data owners don’t always play nice
Technology + policy = impacts
We are in unchartered territory…….
Where do we go from here?
• GF at scale
• WHO
• UN/Global
Pulse
Public
Health
Extreme
Scale
“Informatio
n Exhaust”
Global
view for
Health
Data
• UNSD
• NGO/IGO
Thank you!