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Network Based Approaches to
Studying Ebola and Emerging
Infectious Diseases in Real-time Using
Smartphones
Solomon Abiola, MS
CHET/DSAIL
Outline
Rationale and background
mHealth and the application of Node to Ebola
Network based approaches to combat disease
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Mobile health and big data among the current most
hyped technologies in 2014
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Source: Gartner
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The rapid growth of mobile technology allows it to
serve as the perfect health platform
Why mobile health?
There are over 334 million smartphones projected to be present in Africa in 2017
~ 30% of the continent’s population
How can we use mobile health?
Remote assessments, diagnostics, increase access to care, disease modeling,
etc.
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Source: TechCrunch
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Objective real time geospatial modelling via
smartphone sensors improves contact tracing
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The Princeton University pilot allowed us to
geospatially identify infectious disease sinks
Main Application Page
Source: Pilot Study
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Areas highly susceptible to interaction among students with the
application appear darker. (Aerial Map of Princeton University)
Node enables health workers and practioners
to see incidents in real time
Two user interactions sampled from March 17th, 2015
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The Node study is designed to recruit a small subpopulation in Lagos, Nigeria
Through our preparatory activities we’re expecting at least 100 participants (>40
University of Lagos + > 25 Nigerian Institute of Medical Research + >40 A/B testers)
Enroll up to 100 individuals within Lagos state (city population over 21 million)
Participants will be paid 50 USD equivalent in phone credits per month of
completion during the study
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Knowledge Discovery Process has lead to
interesting insights in mHealth for disease
mHealth App
Node
~70 users
Parse
(Facebook)
Google
CartoDB
PHASE 1
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MongoDB
~30GB
AWS
PHASE 2
PHASE 3
Analytics
~2mb
PHASE 4
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The Node View platform allows for real time
health action based on ubiquitous data
Locate
Identify
View
Enact
Lagos, Nigeria Beta Test
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Three distinct networks emerged over the
course of the study, correlated with study pop.
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Network evolved over three month time
period, with some nodes leaving and coming
AUG
SEP
OCT
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Worse time to contract diseases starts early in
the week – e.g. Today!
Calendar Heatmap of contact events for entire study duration.
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So if we perform a targeted removal of highly
infectious individuals what happens?
AUG
Graph Density (.127  .098)
Edges (198  130)
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SEP
OCT
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Public Health Official – “And so what?”
Actionable data. Finding patient ZERO.
When?
Calendar Heatmap of contact events for select user.
Who? Subject “_User$ri48ujTmJK”
How? Where?
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Where? Using geospatial information patient
ZERO can be located, and path protected.
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Where? Using geospatial information patient
ZERO can be located, and path protected.
_User$ri48ujTmJK
Activity
in_vehicle
on_bike
on_foot
still
tilting
64
unknown
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4
49
580
96
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Beyond Ebola, future emerging infectious
diseases
Continuous temperature tracking
Household influenza tracking
Disease forecasting
NSF #1516340
[1] Abiola, S. O. Node: A Real Time Smartphone Big Data Application for
Health Care Epidemiology. (2013). at
http://dataspace.princeton.edu/jspui/handle/88435/dsp01sx61dm401
[2] Abiola, S. O., Portman, Eric., Kautz, Henry., Dorsey, E.R., Node View: A
mHealth Real-time Infectious Disease Interface. Ubicomp/ISWC’15 Adjunct
Proceedings. (2015).
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Emerging infectious diseases platform –
example case of Zika
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Mobile Development and Analytics Courtesy of AbiTech, Inc. www.oneabitech.com
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