Research Collection
Presentation
Modelling COVID19 using an agent-based-model of travel
Author(s):
Axhausen, Kay W.
Publication Date:
2021-04
Permanent Link:
https://doi.org/10.3929/ethz-b-000479837
Rights / License:
In Copyright - Non-Commercial Use Permitted
This page was generated automatically upon download from the ETH Zurich Research Collection. For more information please consult the Terms of use.
Preferred citation style
Axhausen, K.W. (2021) Modelling COVID19 using an agent-based-model of
travel, presentation at the Chinese University Hong Kong, online, April
2021.
Modelling COVID19 using an agent-based-model of travel
KW Axhausen IVT
ETH Zürich
April 2021
Collaborators and partners NFP78
ETH Zürich
• M Balac
• G Kagho
• S Pennazi
• A Sallard
ETH Zürich (Thomas van Boeckel) FHNW (Erath Alexander)
University Basel (Melissa Penny) University Geneve (Olivia Keiser) Senezon (Michael Balmer)
Kai Nagel (TU Berlin), Zbigniew Smoreda (ORANGE), Sebastian
Bonhoeffer (ETH Zürich)
Acknowledgements
Sebastian Müller, TU Berlin
Stefano Penazzi, ETH Zürich
Joseph Molloy, ETH Zürich
COVID19 in Switzerland – Some numbers
MOBIS/COVID19 sample since March 2020
Share of mobile persons by day since September 2019
0 100 200 300 400
0%
25%
50%
75%
100%
0 28 56 84 112 140 168 196 224 252 280 308 336 364
N ew c o n fi rm ed h o sp ti al iz ed c as es
M o b ile p e rs o n s [% ]
Day of year
MOBIS control group COVID19 2020
COVID19 2021
Hospital cases 2021
Hospital cases 2020
Average number of trips and size of activity space by week
0.0 1.0 2.0 3.0 4.0 5.0 6.0
0.0 100.0 200.0 300.0 400.0 500.0
02.03 02.05 02.07 02.09 02.11 02.01 02.03
N u m b er o f tr ip s/ d ay
A ct iv it y sp ac e [k m 2 ]
Week starting with Monday
Weekday - Activity space
Weekend&holidays
Weekday - Trips
Weekend&holidays
Δ% change in PKm by mode: All by week
-100.0%
-50.0%
0.0%
50.0%
100.0%
150.0%
02 .0 9 .1 9 02 .1 0 .1 9 02 .1 1 .1 9 02 .1 2 .1 9 02 .0 1 .2 0 02 .0 2 .2 0 02 .0 3 .2 0 02 .0 4 .2 0 02 .0 5 .2 0 02 .0 6 .2 0 02 .0 7 .2 0 02 .0 8 .2 0 02 .0 9 .2 0 02 .1 0 .2 0 02 .1 1 .2 0 02 .1 2 .2 0 02 .0 1 .2 1 02 .0 2 .2 1 02 .0 3 .2 1
% c h an ge
Week starting
Bike
Walking
Bus
Tram
Train
Car
All
Δ% change in PKm by mode: Cycling and train by period
-100%
-50%
0%
50%
100%
150%
1 5 9 13 17 21 25 29 33 37 41 45 49 53
C h an ge
Caledar week
Bicylce before B2020
B2021 Train before T2020
T2021
How to assess the measures against a pandemic ?
How to assess ..? Model types
• Aggregate SEIR (Susceptible-Exposed-Infectious-Removed)
• Agent-based with contact networks SEIR
• Aggregate networks of co-presence in vehicles
• Repeated networks of co-presence based on agent-based transport
models
How to assess ..? Model types
• Repeated networks of co-presence based on agent-based transport models
Smieszek, T., M. Balmer, J. Hattendorf, K.W. Axhausen, J. Zinsstag and R.W. Scholz (2011) Reconstructing the 2003/2004 H3N2 influenza
epidemic in Switzerland with a spatially explicit, individual-based
model, BMC infectious diseases, 11 (1) 1-18.
How to assess ..? Networks of co-presence
Co-presence is defined by
• A space
• A starting time
• A joint duration
• Other persons present for the joint duration
How to assess ..? Networks of co-presence
Time
How to assess ..? MATSim
MATSim delivers
• An agent-population
• The network of co-presence for one day (at activities and “in vehicle”)
• The computing infrastructure to replan the days
How to does MATSim generate the days?
MATSim calculates the equilibrium pipelines for scenario generation for reproducibility
• OSM reader
• GTFS + transit network mapping
• Population synthesis (incl variant for GSM data use)
Tools inside the open-source framework MATSim
Pipelines for scenario generation for reproducibility
• OSM reader
• GTFS + transit network mapping
• Population synthesis (inc. variant for GSM data use)
• Sallard’s traffic signal imputation Faster implementations
• Hörl/Balac’s eqasim (RUM mode choice)
• HERMES for MobSim
• Penazzi’s DEDALO to form super-networks
• Flötteröd’s OPTYTS for parameter calibration External commercial products
• VIA for result analysis
• TRAMOLA for scenario editing and run management
How to assess ..? Simple measures
Prohibit or limit co-presence by
• Quarantine
• Home-office; home-schooling
• Limits on group size
• Range restrictions
How to assess ..? Episim’s infection model
episim delivers
• Repeated instances of the same day
• Tracing of the person and its health status over the days
• Possible infections during joint activities
• Simple measures by removing the affected agents from the
activities
How to assess ..? Episim’s infection model
episim assumes a random process among N randomly chosen agents:
𝑃
𝑖𝑛𝑓𝑒𝑐𝑡𝑖𝑜𝑛= 1 − 𝑒
− Θ・ContactIntensity ・Shedding・Intake・Duration・Susceptibility・ Infectivity・OutdoorFactor• ContactIntensity ∼ Flow per agents
• Shedding, Intake = 1, unless mask wearing
• Duration = Activity time from MATSim
• Susceptibility, Infectivity = 1, but reduced for children
• OutdoorFactor = 1, unless outdoor activity, e.g. dining
How to assess ..? Progression process after infection
Exposed Infectious
Symptomatic
Seriously sick Critical
Seriously sick
Recovered
Recovered
Recovered
Recovered
[3.5 (3.5)]
[2.0 (2.0)]
[4.0 (4.0)]
[1.0 (1.0)]
[21.0 (21.0)]
[4.0 (4.0)]
[8.0 (8.0)]
[14.0 (14.0)]
[3.5 (3.5)]
[7.0 (7.0)]
[Median (st. dev.)]
Switzerland implementation
Calibrated model, well used in previous projects
• OSM network
• Public transport timetables (SBB and all regional providers)
• STATPOP population (registry data)
• Travel diaries (Microzensus 2015)
• Validated against counts, modes*distance
Switzerland: Mask adoption factor
Switzerland: Imported cases (calibrated)
Infectiousness
• SARS_COV_2 = 1.0
• B.1.1.7 = 1.5
• B.1.351 = 1.5
• P.1 = 1.5
Switzerland: Calibrated model results
Switzerland: Seriously sick by example region (canton)
Switzerland: Average ICU cases by hospital
Switzerland: Vaccination strategy – ICU patients
3000 6000 9000 12000
Persons/day
Next steps: Hypertension + diabetes
Age
