Human travel is the largest cause of epidemic spread, but there is little data available to understand and monitor those moves. Telecom data help solve this issue, as it is unique in terms of size, granularity and mobility insights. This presentation will show you how analytics tools based on telecom data enable aid workers to make smarter decisions, take prompter action and eventually save more lives.
Real Impact Analytics (RIA) has developed an important knowledge on societal issues, epidemic risk flows in particular. It has a proven track record in Zambia for dealing with Malaria and in Western Africa for Ebola, where it supported UNICEF’s action.
2. OVERVIEW
Data Analytics can predict the spread of epidemics
Why telecom data?
How do we predict this?
Example of malaria case
Conclusion
3. Epidemics and endemic diseases are some of
world’s most severe problems
ZIKA
Analytics can be used to predict the
spread of epidemics
4. Different types of data can be leveraged to
understand, measure and predict the
transmission of these diseases
Analytics can be used to predict the
spread of epidemics
Social Media Weather Transportation
5. Telecom data, thanks to its unique
features, is one of the most valuable
types of data for predictive analytics.
Analytics can be used to predict the
spread of epidemics
7. Telecom data is automatically
and systematically recorded in
the Call Detail Records (CDRs),
which means data collection
efforts and response bias can be
avoided.
Automatic data collection
8. It contains information on
connectivity between people,
which helps to understand
people’s social networks.
Social information
9. It contains information
about location changes
over time, which is great to
understand human mobility.
Mobility information
10. It contains information on phone
spending, which allows tracking
of socio-economic indicators
and focusing on different socio-
economic groups.
Spending patterns
15. Disease
Pattern
Malaria is
spread by
mosquitoes
at night
Incidence
Information
Information is
collected
from local
health care
agencies
Epidemiolo
gical Model
A model is
created
combining
mobility and
incident data
Map
Risk Flow
High risk
areas and
epidemic
trend are
predicted and
visualized
Action
Taken
Officials
direct
resources to
relevant
locations, or
limit mobility
The malaria case
16. Result: risk flows are mapped to identify
hotspots
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
17. 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
21. info@realimpactanalytics.com
@RIAnalytics
realimpactanalytics.com
@RealImpactAnalytics
Real Impact Analytics
Real Impact Analytics (RIA) taps into rich telecom
data to capture its value. The data is turned into
action with big data apps that ease our clients’
day-to-day work.
RIA provides guided and predictive analytics
through proprietary software. Five of the top ten
global telecom operators trust us to enhance
customer experience through Customer Value
Management, and optimize daily operations with
our Commercial Excellence apps.
To learn how Real Impact Analytics can create the
same value for you, visit realimpactanalytics.com.
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