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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 .

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Global risk informatics microsoft gates foundation

Global Risk InformaticsMicrosoft / 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?



The information gap is real… validate!

?

First data becomes available



But what if
But what if? validate!

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
Microsoft – Gates Foundation validate!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
The Bill and Melinda Gates Foundation validate!

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”



Malaria today
Malaria today validate!

Malaria Burden -2008

•863 000 deaths

•243 million cases

•Half of the world's population is at risk of malaria


Current solution
Current solution validate!

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
What motivates the GF? validate!

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 w hy technical and high performance computing
Malaria modeling: validate!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
Malaria Models validate!

  • 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 Malaria Models validate!

  • 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

    Analytical

    tools

    Assembly of regional vector ecology profiles

    Entomology

    Local environments

    Epidemiology

    1

    2

    3

    Identification of critical data gaps

    Location-specific stratifications and data

    Vector species ecology profiles and ranges

    Malaria parasite locations, rates

    Assessment of utility of potential VC interventions

    Second-wave input

    Policies and regulations

    Interventions

    4

    Identification of gaps in current intervention set as informant of TPPs

    Second-wave output

    Supply, demand and financing assessment

    Regulations, policies, financing

    Intervention profiles, incl. efficacy and resistance


    Analytical framework will capture four key types of data
    Analytical framework will capture four key types of data information sets, enables assessment of vector control interventions

    1

    2

    3

    4

    Entomology

    Local Environments

    Epidemiology

    Interventions

    Aggregate vector species information

    Consolidate multiple location-based variables

    Map against malaria outbreak data (location, rate)

    Overlay intervention profiles, including efficacy info.

    Primary data components

    • Parasite rates and coordinates

    • Expert-derived epidemiological ranges

    • Classified list of interventions1

    • Efficacy and effectiveness

    • List of reproductively isolated vector groups

    • Vector ecology profiles (biting, resting, breeding sites, sugar meal source)

    • Vector presence coordinates

    • Expert-derived vector ranges

    • Political map

    • Precipitation

    • Human density estimates

    • Climate

    • Topography

    • Local resistance to active ingredients

    • Availability of alternative interventions (e.g., drugs, vaccines)

    Secondary components (used to expand and/or refine framework)

    • Emergence of new species

    • Mating and swarm behavior

    • Species genomic data

    • 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

    Key sources for data

    • Malaria Atlas Project (MAP)

    • Disease Vector Database

    • Swiss Tropical Institute / MARA

    • Walter Reed Biosystematics Unit

    • VectorBase / Anobase

    • WHO

    • MAP

    • CIA Factbook

    • Koppen-Geiger Climate Classification

    • SEDAC (GRUMP)

    • Malaria Atlas Project (MAP)

    • WHO

    • Swiss Tropical Institute

    • CDC

    • WHO

    • Croplife

    • IVM evidence committee

    • STI

    • Vestergaard-Frandsen

    • Academic literature

    • Expert input

    • WHO

    • AFPMB

    • ANVR

    1. Interventions to be classified by control paradigm, target vector age, active ingredient(s), number of active ingredients, safety, development status and robustness against pyrethroid-resistant vectors


    Multiple data sets to be combined and integrated
    Multiple data sets to be combined and integrated information sets, enables assessment of vector control interventions

    MAP

    Local resistance to AIs

    Academic lit., Vestergaard-Frandsen,

    Comprehensive vector ecologies

    Vector ecology

    profiles

    Regional Vector

    Ecology Profiles

    MAP, WRBU, STI

    Integratedepidemiological

    & vector speciesdatasets / maps

    Vector species

    datasets / maps

    Integratedepidemiological

    &

    entomological

    datasets / maps

    MAP, WRBU, DVD, STI

    List of reproduct. isolated groups

    DVD, MAP., STI

    Entomology

    Vector locations

    Vector presence coordinates

    DVD, MAP , Academic lit.

    MAP, DVD , Academic lit., Expert ranges

    Expert-derived vector ranges

    MAP, Expert input

    Political map

    Data gaps

    MAP

    Location-specificboundaries & data

    Precipitation

    Stratification

    map

    NASA; MAP

    Climate

    MAP

    MAP

    NASA; MAP

    Local Environments

    • Searchable database and vector or location-specific datasets

    • Visual maps

    • Searchable database and vector or location-specific datasets

    • Visual maps

    • Searchable database and vector or location-specific datasets

    • Visual maps

    Topography

    MAP

    Hum. population

    GRUMP

    MAP

    MAP, GRUMP WHO, Academic lit., Vestergaard-Frandsen

    Intervention utility map

    Altern. interven.

    WHO, Academic lit.

    Parasiteepidemiology

    Epidemiological map

    Parasite rates and coordinates

    MAP, Academic lit.

    Epidemiology

    Expert-derived epidem. ranges

    MAP, Expert input

    MAP, Academic lit., Expert input

    MAP, WHO, STI

    Profiles of currentinterventions

    Interventioneffectiveness

    Intervention gap assessment

    WHO, Academic lit., STI, Expert input

    List of interventions

    Expert input

    Interventions

    WHO, STI, Expert input, academic literature

    Intervention efficacy

    WHO, STI, academic literature


    What are we doing
    What are we doing? information sets, enables assessment of vector control interventions

    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, GIS-mapping, search, simulation and modeling


    Challenges
    Challenges information sets, enables assessment of vector control interventions

    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
    Where do we go from here? information sets, enables assessment of vector control interventions


    Thank you! information sets, enables assessment of vector control interventions


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