CoDiSS

Contagious Disease Spread Simulator

This Python module provides a tool to simulate the spread of confection disease in buildings using an agent-based simulation approach. The simulation considers the emission of virus quanta by infected agents and calculates the quanta inhaled by healthy agents. Based on stochastic calculations, the simulation predicts probable new infections, makes agents infected by chance and continues the simulation based on the agents' new status.

The CoDiss module consists of two main classes: Model and Agent. Both classes inherit from the general purpose ABS.py (Agent-Based Simulation) transferred from AgentBuilt project: https://github.com/Project-AgentBuilt/AgentBuilt

1. The Model class is responsible for setting up the simulation environment, including defining the parameters and initial conditions of the simulation.

2. The Agent class represents individual agents in the simulation, each with its properties and behaviors.

The simulation can be run for a desired period, which is specified using the datetime Python module. During this time, the spread of COVID-19 is tracked and visualized using the ABS module. The time step for the simulation is adjustable, allowing for a balance between the speed and accuracy of the model. For COVID-19 spread, a time-step equal to 1 minute (60 seconds) is recommended to capture the spread dynamics accurately. This module provides a powerful tool for understanding the dynamics of COVID-19 spread and designing effective intervention strategies to control the spread of contagious diseases in buildings.

This repository includes the following python modules:

• CoDiSS.py: Main file for simulating the disease spread
• ABS.py: transferred rom https://github.com/Project-AgentBuilt/AgentBuilt
• layout.py: This file creates the office layout for the case study. It helps visualize and plan different scenarios for testing or analysis.
• create_senarios.py: This Python module provides a flexible way to define and test various interventions in an office layout. The module defines different scenarios, such as adding decompression areas, reducing agent cluster sizes in working areas, and shifting agent schedules.
• case_study_MCS.py: This Python file utilizes the CoDiSS.py and Create_Scenarios.py modules to simulate and test the effectiveness of different interventions in controlling the spread of infectious diseases.
• animation.py: This Python file provides an animation of a case study layout, depicting a short periord in the life of the building to showcase how the agents arrive at and leave the building, allowing the user to visualize the movement patterns of the agents throughout the simulation.