Koudou

Modular small community simulator

This repository contains the simulator used for the paper: Shiyu Jiang, Hee Joong Kim, Fabio Tanaka, Claus Aranha, Anna Bogdanova, Kimia Ghobadi and Anton Dahbura. 2023. Simulating Disease Spread During Disaster Scenarios In Proceedings of the International Conference on Artificial Life (ALife 2023).

How to reproduce the results:

Set up environment

git clone -b ALIFE_2023 https://github.com/caranha/Koudou.git
cd Koudou
pip install -r requirements.txt

Deploy Map .osm file

Download the .osm file for the tsukuba area and place it on: osm_files/Tx-To-TU.osm

• To download the file go to: https://www.openstreetmap.org/export and set the following coordinates:
  • minlat="36.0777000"
  • minlon="140.0921000"
  • maxlat="36.1217000"
  • maxlon="140.1201000"
• Or download using the API req: https://www.openstreetmap.org/api/0.6/map?bbox=140.0921%2C36.0777%2C140.1201%2C36.1217

Modify the config files

Referring to the directory of config and parameters, modify the files to the desired configuration as introduced in the paper. Here is some default parameters as tables for reference:

Reduced infection rate for mask config/infection/covid.json --> "precautionary_measures"

The infection will multiply by the reduced infection rate based on agent's mask condition.

Mask	Reduced Infection Rate
No Mask	1.0
Surgical Mask	0.34
N95 Mask	0.17

Chances of changing mask config/infection/covid.json --> "precautionary_measures"

When self-infected, the chance of changing mask to a better one.

Condition	Current	Next	Chance
Self infection masked	Surgical mask	N95	0
Self infection masked	No change	No change	1
Self infection unmasked	No mask	Surgical mask	0
Self infection unmasked	No mask	N95	0
Self infection unmasked	No change	No change	1

Agent profession with mask /config/behavioral/profession.csv

Proportion of agents with mask for each profession at initialization. The sum of the proportion of each profession should be 1.

Profession	No mask	Surgical mask	N95 mask
student	0.75	0.125	0.125
university student	0.75	0.125	0.125
teacher	0.75	0.125	0.125
medical doctor	0	0	1
teacher	0.75	0.125	0.125
university professor	0.75	0.125	0.125
salaryman outside city	0.75	0.125	0.125
retailer	0.75	0.125	0.125
salaryman	0.75	0.125	0.125
barber	0.75	0.125	0.125
restuarant worker	0.75	0.125	0.125
scientist	0.75	0.125	0.125

Run the simulation

-s: seed for random number generator. It can help with the reproducibility of the results. If not specified, the default seed is 1111.

python main.py -p parameters/default.py -s 1111

Analyze the results

Use dashboard to analyze the results with visualization and statistics. It locates at /src/dashapp/App.py Please refer to the README.md in the dashapp folder for more details.