PANDEXIT is a tool for helping decision makers through the simulation of alternatives scenarios with a realistic agent-based model. We are using very high performance algorithms.

1. Pandexit
A tool for helping decision makers through
the simulation of alternatives scenarios
with a realistic agent-based model.

2. 1.
What is PandExit?
A simulation tool with a realistic agent-based model
2

3. What is PandExit?
1. PandExit is
a. An agent based simulation and forecasting tool,
including people/places/transports in the model.
b. Capable of evaluating the outcomes of
alternative scenarios depending on external
circumstances or internal policies of a country.
c. An assistant for informed decision making.
3

4. What PandExit is not?
1. PandExit is NOT
a. A real time tracking system.
b. Another statistical model.
c. A tool to predict the exact number of cases or
deaths in a certain point in time.
4

5. “An agent based model is a class of computing models
which simulate actions and interactions of a set of
autonomous agents, with the objective of understanding
and predicting the behavior of a system as a whole.

6. 6

7. Places
Which entities are there is the simulation?
Transports People
7

8. People
PandExit simulates every person you enter into the system.
Each one is an agent with its specific behavior...
8

9. Places
… following its daily
routine in a virtual world,
commuting between his
home and his job, or the
s c h o o l , o r t h e
supermarket...
9

10. Transports
… using his car, or a taxi,
or a combination of metro
and bus, or walking ...
10

11. Other activities
… and performing other
activities that correspond
to his demographic or age
profile.
11

12. Geography and geometry
1. Geography is not geometry
a. Geography is territorially large
i. World
ii. Country
iii. Province
iv. Region.
b. Geometry is smaller but more detailed
i. Plant, stadium, hospital, casino, airport
ii. Building
iii. Floor
12

13. Examples of simulations with geometry
1. Pandexit supports both geography and geometry
a. Geography
i. We will see examples in section 5.
b. Geometry
i. Evacuation of spectators in stadiums...
ii. … or other types of building.
iii. Movement of personnel on a plant floor.
iv. Queueing times in complex scenarios
13

14. Example in a Casino
14
Zones more likely to
become infection areas for
customers in a Casino.
Pandexit can use security
cameras to allow real time
visualization of risky
customer behavior.

15. 2.
How does it relate to the pandemic?
The model is specialized for infections
15

16. How does it relate to the pandemic?
PandExit allows to estimate the
evolution of the COVID-19 pandemic
using the precise demographic,
territorial, and economic characteristics,
and evaluating the spread according to
different policies like closing sets of
businesses or restricting internal travel.
16

17. Individual tracking
17

18. Group tracking
18

19. Zone tracking
19

20. National scope - Argentina
20

21. National scope - Qatar
21

22. We can model any country if we have the data22

23. 3.
How is it used?
23

24. How do you get results?
Load data
24
Configuration Simulation

25. 25
Load data
▸ People
▹ Home
▹ Age
▹ Workplace
▸ Places
▹ Companies
▹ Schools
▹ Hospitals
▸ Transports
▹ Types
▹ Frequencies
▹ Destinations

26. 26
Configuration
▸ Parameters
▹ Speed
▹ Reinfections
▹ Age ramp
▹ Death rate
▸ Policies
▹ Open/close
▹ Schools
▹ Non-essential
▹ Restrict
▹ Internal travel

27. 27
Simulation
The simulation advances
based on the loaded
data, and the policies
can be modified during
its run.

28. More is better
With more data, and more precise data, the quality of the
simulation improves and so do the results.
28

29. 4.
Small-world network effects
Small variations can generate big differences in outcome
29

30. “A small-world network is a network, whether social, of
information, abstract, or any kind, in which most nodes
are not neighbours of one another, but the neighbours of
any given node are likely to be neighbours of each other
and most nodes can be reached from every other node by
a small number of hops or steps.

31. 31
Source: Generalization of the small-world effect on a model approaching the Erdős–Rényi random graph, Benjamin F. Mayer, Scientific Reports, 9

32. Normal scenario - Social hubs and centrality
32
● Schools
● Restaurants/bars/cafes
● Religious places
● Public transport
● Supermarkets

33. The risk of small-world network effects
33
Which industries or
activities can be
allowed without
risking the
formation of a
small network?

34. Qatar Stage 1 - What if only jobs are open?
34

35. Qatar Stage 1 - What if only jobs are open?
35

36. Creating synthetic social networks
36
Source: Modeling Social Networks in Geographic Space, Approach and Empirical Application. Theo Arentze, Pauline van
den Berg, Harry Timmermans. Environment and Planning, 2012, vol. 44, pag. 1101-1120.

37. Friendship formation model
37
Source: Modeling Social Networks in Geographic Space, Approach and Empirical Application. Theo Arentze, Pauline van
den Berg, Harry Timmermans. Environment and Planning, 2012, vol. 44, pag. 1101-1120.
Probability of friendship depends on
● Homophily
○ Age
○ Nationality
○ Socioeconomic profile
● Geographic distance
● Transitivity or existence of common friends

38. Real world social networks
38
Source: Modeling Social Networks in Geographic Space, Approach and Empirical Application. Theo Arentze, Pauline van
den Berg, Harry Timmermans. Environment and Planning, 2012, vol. 44, pag. 1101-1120.

39. 5.
Qatar and MINDEF prototypes
A prototype is worth one thousand meetings
39

40. MINDEF Prototype
40

41. MINDEF Prototype
1. Approximately 50.000 agents.
a. Students, teachers, and personnel of military
schools following information provided by
MINDEF (Ministry of Defense) of Argentina.
b. Additional family members added to complete
household data.
2. Nine military schools included.
3. Transports generated through statistical means.
41

42. Limitation of the MINDEF prototype
1. Infections coming from outside the considered group
are not modeled, only internal spread.
a. The rest of the country data is needed.
2. Home coordinates are assigned randomly inside the city
or district of residence.
a. Precise home data will improve the simulation.
3. Family and household structure was created through
statistical means and not with real data
a. Person by person demographic data will improve the simulation.
4. The transport network has been simplified.
42

43. QATAR Prototype
43

44. QATAR prototype
1. One agent per person, 2.78 million agents.
a. Adults and children with age and nationality
following following public census information.
b. Household conformation follows statistical
patterns of public census information.
2. Home coordinates are assigned inside zones using
density data extracted through computer vision.
3. Transports generated through statistical means.
44

45. Extraction of density data using computer vision
45

46. Limitation of the QATAR prototype
1. Infections coming from outside the country are not
modeled, only internal spread.
a. Travelling data is needed, as well as data from other countries.
2. Home coordinates are assigned inside zones using
density data extracted through computer vision.
a. Precise home/workplace data will improve the simulation.
3. Family and household structure was created through
statistical means and not with real data.
a. Person by person demographic data will improve the simulation.
4. The transport network has been simplified.
46

47. Workbench and individual tracking
47

48. How to proceed?
▸ Integrate reasonable scenarios
▹ Internal frontiers
▹ Specific policies
▹ Essential jobs
▹ Non-essential jobs
▸ Load detailed data from the country
▹ People
▹ Homes/Workplaces
▹ Transports
▹ School, hospitals, etc.
48

49. 49
Thanks!
Any doubt?
Send an email to nahuel.gonzalez@adgs.com