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

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