This is a presentation about a "smart quarantine " solution that was developed by community #covid19cz with cooperation of Czech government, telco operators (O2, Vodafone, T-Mobile) and banks. It is fully GDPR compliant and scalable.

1. Overview
Application for Tier 2 call-center -
tracing suspected COVID cases based on location data

2. Purpose
The purpose was to build TRACING CALL CENTER
in order to
● decrease R0 of the epidemic using targeted, data-driven testing
● to be able to shift to “smart quarantine” from current countrywide lock-down
● dramatically decrease the negative economic impact of COVID19

3. Use Case I
Whitelist of numbers of infected people to be traced
sanitary station forwards the cell phone number of the infected person
through a secure channel
Step 1
Explicit Permission
Operator asks for explicit permission to use location data. This consent
is audited and stored, shared with mobile operators and banks and is a
prerequisite for the next step.
Step 2
Memory Map Tracing
A trained and dedicated call center operator (trained medic) calls the infected person to find
other suspected cases using the Memory Map. The resulting contact list is consulted with
hygiene and further processed according to its recommendations, for example, to call with other
information.
Steps 4-6
Data Generation
Operators and banks generate a set of location data on the movement and
payments of this one mobile number / person. Dotmaps and heatmaps
(Memory Map) is automatically created in CleverMaps
Steps 3

4. Use Case II
Whitelist of numbers of potentially infected people to be traced
system generates cell phone numbers of people who were in close
proximity to infected numbers during the watched period
Step 1
Explicit Permission
operator asks for explicit permission to use location data. This consent is
audited and stored, shared with mobile operators and banks and is a
prerequisite for the next step.
Step 2
Notifications and Follow-ups
All these numbers get push SMS notification to get to sanitary station asap as they could have
been in direct contact with infected person Verification calls - containing suspected cases to
check if they have been tested
Steps 4-6
Data Generation
system sends the number to telco operators and gets back a set of numbers which were identified as
“appeared in the same cell during the given period with the number of infected person”. This only applies to
numbers that are frequently used/called out of the infected number - i.e. covers mostly family, friends,
co-workers,)
Steps 3

5. Data sources used
● location data from mobile operators
● location data from bank cards and POS transactions
● location data downloaded from mobile apps in the phones of
contacted persons - special purpose built apps as well as
permissioned based phone location history (4weeks back)

6. Architecture
The whole system is secured,
GDPR compliant and audited
by PwC
Cloud based - scales
More architecture detail:
https://docs.google.com/document/d/1
WV71t8HthrtWdnw8h-6oieNxoAFo94o
5EIc6vXez3dA/edit#heading=h.c7113z
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7. BlueTooth Mobile App
App is anonymous
Monitors anonymous ids of others who you meet
and they have the app installed
If you agree, the data is then sent to local CDC to
be analyzed and and contacted for smart
quarantine purpose

8. Location data sample
dt,period,lat,lon,precision_code,intensity,id,msisdn
2020-03-09T00:00:00.000Z,80,50.1248,14.4548,1,1.0,2837e3044979c03ae39a343e514fc40d389a16b5,gAAAAABeb5O-A
NRydQy46Z1mMnOp0zRds9Qi4zne3xH4anOyXpLQ2PUrZex75PERaf4igsw-tb3utU6an2BFJuEm4Ww3P3Of3A==
2020-03-09T00:00:00.000Z,81,50.0473,14.4545,0,0.2,e9091daae681bf128a39df1759bd4004b5215885,gAAAAABeb5O-6
LFf79GcCWKUsqHj_n7jzqM-pb88EhVNmZi6WIKBdDnTIjsjGcYdpqS4u6u_emEOXYU5XVGpBB-Nf3GWrn4InQ==
MSISDN is a phone number and it's encrypted.
other columns
msisdn - phone number (e.g. 420123456789)
[dt - timestamp. 2020-03-09T00:00:00.000Z
[period - 6h time windows: 00:00-05:59, 06:00-11:59, 12:00-17:59, 18:00-23:59 represented by code 80, 81, 82, 83
[precision_code - Indicative information about location accuracy by 500 m steps, and represented by 1 - 10 where 10 means 5000
m or more
intensity - Spent time in 6-hour window expressed in intensity on a 0-1 scale linearly 0 = 10 minutes 1 = 6 hours

9. Example Screenshots - Memory Map

10. You can filter to see the detail

11. Day is March 9.
Time range 6am-12pm
Places where the phone has stayed for more than 30 min, but
less than 2 hrs
I can see place home, place of work and lunch place as places
with highest probability of infection spread
Results after filtering

12. Filtering with POIs
I can see place where the number stayed almost 4 hours in the evening time
In the close proximity are 2 bars - I should check if the person visited one of the bars.., with the
possibility to check phones of the owners and contact them immediately.

13. Advanced Functionality - Danger Heat Maps in time

14. Based on anonymized location data
system calculates infection “danger
zones” - in time:
- visualisation in map for
government purposes
- push to other apps via APIs
Includes heat maps - based on frequency/count

15. Heatmaps of local hotspots
as the infection was spreading
over in individual days back in
time until today
Aggregated results

16. Where does the data sit and how secure is the system?
Data sits within a secure data hub created by Keboola using Microsoft and AWS technologies. The
closed system has integration to operators and banks, dwh, synthetic data management, API based
communication and full governance. The system is SOCII type 2 audited, GDPR compliant and the
whole project is monitored by PwC . It has real-time safety proactive measures and can utilize AI to
predict “bad actor” behavior.
What call center is it compatible with?
The system for “smart quarantine” is independent of specific call center technology and can be adapted
within 24 hrs to work with major providers as well as data exchange with local CDCs.
FAQ