An informal talk given at the ALife for Society internal webinar on December 14, 2020

1. What an ALifer
Has Been Doing
About COVID-19
HIROKI SAYAMA
SAYAMA@BINGHAMTON.EDU

2. University
Closed in
March
2

3. Apex Modeling in Early April
(in Response to County’s Request)
3

4. 4

5. 5
(University President’s statement,
April 20, 2020)

6. 6

7. Projects
1. Campus traffic modeling
2. Campus epidemic modeling
3. New York State county-level visualization
4. Nationwide, worldwide visualization
5. SUNY COVID seed funding
6. Collaboration with local healthcare provider
7

8. 1. Campus Traffic Modeling
▪ Agent-based model on multilayer
transportation networks
▪ Python + NetworkX + Jupyter
▪ Car road layer + pedestrian path layer
▪ Designated locations (dorms, classrooms, food
places, parking lots, bus stops, campus
entrances)
▪ 17k pedestrians + 10k vehicles, each with unique
behavior reconstructed from data
▪ Objective: Visualize on-campus traffic and
measure frequencies and locations of close
contacts on a typical Tuesday in Fall 2019
8

9. Real Data Incorporated
9
Course schedules
and locations in Fall
2019 (from Michelle
Ponczek)
Individual students’
residence hall and class
registration for Fall 2019
(from Michelle Ponczek)
Individual
employees’ office
building and FTE
(from Michelle
Ponczek)
Campus road/path
networks (extracted
from Google Earth)
Square-foot areas of
classrooms (from
Michelle Ponczek) Parking lot
capacities (from
Brian Rose)
Bus arrival/
departure
frequencies (from
Brian Rose)
Used to develop
individual agents’
detailed behaviors
Used to count close contacts

10. “A Day at Binghamton”
10

11. Hypothetical
Agent
Behaviors
11
Has an actual schedule of Tuesday classes
On-campus residents: all transportation on foot (congestion
can slow down walking speed down to 50%)
Off-campus residents: Commuting by car (need parking) or
by bus; after getting off car/bus, all transportation on foot
Moves from class to class according to schedule; may go
back to dorm if there is enough time
Tries a quick trip for food at an appropriate time (if possible)
Goes back home when everything is done

12. Counting Indoor Contacts
N: Number of people in space
A: Area of space
a: Area of a 3-foot-radius disc
Number of close contacts in space
= Number of neighbors each person has
x number of people / 2
nCC = (N / (A/a) – 1) N / 2
12
(if this is greater than 1)

13. Cumulative
Contact Density
Map
13

14. 50% Density
14

15. 25% Density
15

16. Effects of
Lowering
Campus
Population
Density
16
R0 = d n pi
n = 5 makes R0 = 1
(with d = 4, pi = 0.05)

17. 17
2. Campus Epidemic Modeling

18. 3. NYS County-Level Visualization
18

19. 4. Nationwide, Worldwide Visualization
19
https://p.dw.com/p/3jH59
https://bit.ly/3kwfglo

20. 20
5. SUNY COVID Seed Funding Project

21. 6. Collaboration with Local
Healthcare Provider (in Planning Stage)
United Health Services (UHS)
Behavioral/epidemiological hybrid
modeling of senior living facilities
21

22. Lessons Learned
• Not what ALife can do, but what we can do (we do have skills!)
• Science and theories are there, but real situations are here
• We (scientists) know nothing; we must listen, learn, collaborate
• Particularly listen to and learn from professionals in the frontline;
they are more expert than “experts”
• Constraints, constraints, constraints
• Things keep changing in a matter of days
• People need to make decisions, no matter what
22

23. Advantages of Being ALifers
• We have technical skills of computation, simulation, visualization
• We study complex interactions among heterogeneous agents
• We go across multiple scales (micro -- meso -- macro)
• We emphasize the importance of space and time
• We are exposed to a wide variety of topics, capable of learning more
• We know struggle and value of interdisciplinary efforts
• We care details and specifics, not just generality or universality
• No point in getting in fancy journals; let’s save people’s lives instead
23

24. A Moment of Pride
I haven’t published a single paper (or posted a single
preprint) about COVID-19 this year
◦ Everything was done genuinely to help people
◦ Not to produce universal knowledge, but to derive solutions to
very specific local problems for my own communities
24

25. Thank
You
25
@hirokisayama