SHOULD FEARDATA ANALYTICS

Presented by Real Impact Analytics

WHY EBOLA & ZIKAMALARIA,

OVERVIEW

Data Analytics can predict the spread of epidemicsWhy telecom data?How do we predict this?Example of malaria caseConclusion

Epidemics and endemic diseases are some of world’s most severe problems

ZIKA

Analytics can be used to predict thespread of epidemics

Different types of data can be leveraged to understand, measure and predict the transmission of these diseases

Analytics can be used to predict thespread of epidemics

Social Media Weather Transportation

Telecom data, thanks to its uniquefeatures, is one of the most valuabletypes of data for predictive analytics.

Analytics can be used to predict thespread of epidemics

WHYTELECOM DATA

Telecom data is automaticallyand systematically recorded inthe Call Detail Records (CDRs),which means data collectionefforts and response bias can beavoided.

Automatic data collection

It contains information onconnectivity between people,which helps to understandpeople’s social networks.

Social information

It contains informationabout location changesover time, which is great tounderstand human mobility.

Mobility information

It contains information on phonespending, which allows trackingof socio-economic indicatorsand focusing on different socio-economic groups.

Spending patterns

Anonymized and aggregated mobile telecom data gives mobility insights while protecting privacy.

Confidentiality

HOWDO WE PREDICT?

Identifymobilitypatterns

during theepidemic

Getincidence

data

Set upepidemiologicalmodel based on

telecom data

Map epidemicrisk flows

Prioritize andmonitor public

healthmeasures

How does telecom data help us understand the spread of disease

EXAMPLEMALARIA CASE

DiseasePatternMalaria isspread by

mosquitoesat night

IncidenceInformationInformation is

collectedfrom local

health careagencies

Epidemiological Model

A model is created

combiningmobility andincident data

MapRisk Flow

High riskareas andepidemictrend are

predicted andvisualized

ActionTakenOfficials

direct resources to

relevant locations, or limit mobility

The malaria case

Result: risk flows are mapped to identifyhotspots

Map displays malaria incidence (as shades) and people

movement for short-term trips (as lines), extracted from

CDRs The export and import scores are calculated with an epidemiological model taking into account mobility

between two places and malaria rates at the orgin and destination

Result: efforts can be focused on higher risk regions

The shades correspond to the impact that malaria elimination in an area would have on drop in malaria imports to the rest

of the country

CONCLUSION

CONCLUSION

With anonymized telecom dataand proper data analyticsmethods, epidemic diseaseprediction can be more efficientand accurate.

QUESTIONS?#EPIDEMICRIA

info@realimpactanalytics.com

@RIAnalytics

realimpactanalytics.com

@RealImpactAnalytics

Real Impact Analytics

Real Impact Analytics (RIA) taps into rich telecomdata to capture its value. The data is turned intoaction with big data apps that ease our clients’day-to-day work.

RIA provides guided and predictive analyticsthrough proprietary software. Five of the top tenglobal telecom operators trust us to enhancecustomer experience through Customer ValueManagement, and optimize daily operations withour Commercial Excellence apps.

To learn how Real Impact Analytics can create thesame value for you, visit realimpactanalytics.com.

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