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ARTIFICIAL INTELLIGENCE IN MEDICAL EPIDEMIOLOGY
PREDICTING DISEASES. SAVING LIVES.
MALARIA
DENGUE
MEASLES
FLUHIV/AIDS
CHIKUNGUNYA
EBOLA
MERS
TB
GONORRHEA
CHOLERA
RABIES
POLIO
DENGUE2.5B at RISK
TB1.5 Million
Deaths
HIV/AIDS40M
Infected
FLU100
Million Deaths EBOLA
90% fatality
GONORRHEA
498M Cases
MALARIA
219 Million
CHOLERA4 Million Cases
400Mdenguecases
ANNUALLY
DENGUE WORLDWIDE
2.5 BILLIONAT RISK
BRAZIL $1.3 B
$440 M
ECONOMIC IMPACT
OUTBREAK MANAGEMENT
UNKNOWNOUTBREAK
AREAS
UNPLANNEDMANAGEMENT
HEALTHCAMPAIGNS
FOGGING & LARVICIDING
GENETICALLYMODIFIED
MOSQUITOES
CURRENT RESPONSEPASSIVE 76.90%
SENTINEL 19.20%
ACTIVE3.90%
“ The next outbreak? We're not ready yet.”
- Bill Gates
INTRODUCING
EPIDEMIOLOGICAL RESEARCH
Weather HotspotsDengue Construction
Bayesian Network
Bayes Model
Evidence Instantiation
GLMMapReduce
Regression Coeffs Table
GLMPredict
SingleTreeDrive
Decision TreeTable
SingleTree
Predict
Predicted Cases Outbreak Likelihood Hotspot Likelihood
Prediction
DISEASE ANALYTICS
93.0%Sensitivit
y
79.3%Specificit
y
VALIDATED
FIELDTESTED88.6%
Accuracy
84.11%5 days of Data AnalysisWith Limited Resources
Accuracy of
DISEASE ANALYTICS
GOVERNMENTS & MULTILATERAL ORGANIZATIONS
PUBLIC HEALTH OFFICERS (VIA ANALYTICS INTERFACE)
USERS OF THE COMMUNITY (VIA APP)
BACKED BYUSED BY
And possibly others likeUSAID, IDB
SUCCESSSTORIES
MARKET PENETRATION
MALARIA
DENGUE
MEASLES
FLUHIV/AIDS
CHIKUNGUNYA
EBOLA
MERS
TB
GONORRHEA
CHOLERA
RABIES
POLIO
@AIMEforLIFEFOLLOW US