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Global Risk Informatics Microsoft / Gates Foundation Debra Goldfarb Sr. Director, Technical Computing Industry Strategy

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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?

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Household-level stats take months to collect, and years to validate!

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The information gap is real ? First data becomes available

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as are its consequences.

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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? But what if?

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Microsoft Gates Foundation Collaboration What are we doing? Why we care? What will we learn? What are the impacts? How does it fit?

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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 peoples 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 peopleespecially those with the fewest resourceshave 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

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Malaria today Malaria Burden -2008 863 000 deaths 243 million cases Half of the world's population is at risk of malaria

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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

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What motivates the GF? The Goal: Eradication Removal/depletion of the last malaria parasite on the earth Its been done before: Smallpox, Rinderpest Guinea Worm, Polio, Measles Ambiguities/challenges Syndrome vs single disease Animal reservoirs? Latent infections

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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

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Malaria Models Transmission models Ross McDonald (transmission) R 0: 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.

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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

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Proposed analytical framework incorporates multiple information sets, enables assessment of vector control interventions Integration of community inputs into unified framework Identification of gaps in current intervention set as informant of TPPs Analytical tools Identification of critical data gaps Assessment of utility of potential VC interventions Assembly of regional vector ecology profiles Local environments Location-specific stratifications and data Intervention profiles, incl. efficacy and resistance Interventions Malaria parasite locations, rates Epidemiology Vector species ecology profiles and ranges Entomology 1 Regulations, policies, financing Policies and regulations 23 4 Second-wave input Supply, demand and financing assessment Second-wave output

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Analytical framework will capture four key types of data Primary data components Secondary components (used to expand and/or refine framework) Key sources for data Aggregate vector species information Entomology 1 List of reproductively isolated vector groups Vector ecology profiles (biting, resting, breeding sites, sugar meal source) Vector presence coordinates Expert-derived vector ranges Emergence of new species Mating and swarm behavior Species genomic data Malaria Atlas Project (MAP) Disease Vector Database Swiss Tropical Institute / MARA Walter Reed Biosystematics Unit VectorBase / Anobase Consolidate multiple location- based variables Local Environments 2 Political map Precipitation Human density estimates Climate Topography Local resistance to active ingredients Availability of alternative interventions (e.g., drugs, vaccines) 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 WHO MAP CIA Factbook Koppen-Geiger Climate Classification SEDAC (GRUMP) Map against malaria outbreak data (location, rate) Epidemiology Overlay intervention profiles, including efficacy info. Interventions 34 Parasite rates and coordinates Expert-derived epidemiological ranges Impact of human migration patterns Actual disease burden Human and vector host resistance Malaria Atlas Project (MAP) WHO Swiss Tropical Institute CDC Classified list of interventions 1 Efficacy and effectiveness Compliance Cost Impact of educational efforts Ecological influences on intervention efficacy WHO Croplife IVM evidence committee STI Vestergaard-Frandsen Academic literature Expert input 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 WHOAFPMBANVR

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Framework inputsIntermediate outputsEnd-user tools Interventions Epidemiology Local Environments Integrated epidemiological & vector species datasets / maps Vector species datasets / maps Vector locations Location-specific boundaries & data Stratification map Epidemiological map Intervention effectiveness Integrated epidemiological & entomological datasets / maps Profiles of current interventions Comprehensive vector ecologies WHO, Academic lit., STI, Expert input MAP, GRUMP WHO, Academic lit., Vestergaard- Frandsen MAP, DVD, Academic lit., Expert ranges MAP, WRBU, DVD, STI MAP, WHO, STI MAP Parasite epidemiology MAP, Academic lit., Expert input Multiple data sets to be combined and integrated Intervention utility map Data gaps Intervention gap assessment Regional Vector Ecology Profiles MAP Parasite rates and coordinates Expert-derived epidem. ranges Vector ecology profiles MAP, WRBU, STI List of reproduct. isolated groups DVD, MAP., STI Vector presence coordinates DVD, MAP, Academic lit. Expert-derived vector ranges MAP, Expert input MAP, Academic lit. MAP, Expert input List of interventions WHO, STI, Expert input, academic literature Precipitation Political map Hum. population NASA; MAP GRUMP MAP Local resistance to AIs Academic lit., Vestergaard-Frandsen, Altern. interven. WHO, Academic lit. Climate NASA; MAP Topography MAP Intervention efficacy WHO, STI, academic literature Expert input Entomology 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 MAP

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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, GIS- mapping, search, simulation and modeling

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Challenges Data: integrity, formats, ontologies, currency and curation, security.not to mention the politics of data Collaboration: data owners dont always play nice Technology + policy = impacts We are in unchartered territory.

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Where do we go from here? UNSD NGO/IGO WHO UN/Glob al Pulse GF at scale Public Health Extreme Scale Informati on Exhaust Data Global view for Health

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Thank you!