NSD Term Project (f-21)

A-simple-virus-spread-simulation

Author: Eric Zhong

Overview

A simple virus spread simulation, which can modify config.py to set the simulation configuration and run it.

This simulation use C++ computing power to speed up the calculation process in the simulation process and use python to visualize the simulation results.

This simulation combine C++ and python3 by pybind11.

In this simulator, increase problem size to 10000 population requires only 1.5 seconds of computational simulation time.

Problem to Solve

In recent years, COVID-19 has been raging around the world. It has a great impact on everyone's lives. According to this article, the spread of COVID-19 is increasing exponentially. In order to prevent the spread of COVID-19, each country/region has implemented many policies (e.g. quarantine, restriction of in-store dining, lockdown...). These policies have produced different results in controlling the spread of COVID-19.

The system can simulate the impact of different policies on the spread of pandemic viruses. User can adjust parameters(e.g. virus infection rate, virus mortality rate...) and policy to observe the final result. Finally, the syetem will output the animation to show the process of infection and saved the final simulation results. Therefore, we can observe the difference in the distribution of infection under different policies.

Prospective Users

Those who want to know if these policies help prevent the spread of COVID-19. Users can set different conditions, such as the total number of people, virus infection rate, death rate, and different policies. Then observe the distribution of the virus spread and the number of deaths.

System Architecture

Input

All parameters in config.py

Output

The graph or animation of simulation results.

Mathematical Model Description

This is a dynamicial system. A dynamic system is a fixed rule that describes how all points in a fixed space change over time. First of all, the system will give an activity area, and a dot represents a person. It will use input parameters to init everyone's state and build the mathematical model.

When running the Mathematical model, this model updates the state of everyone after each time step. The following is the calculation of the model. The components in RunStep():

1. CheckPolicy(): Check the policy and modify the population state according to the policy.
2. Move(): Everyone move one step.
3. UpdateDirection(): Check everyone's direction and update as needed.
4. SpreadVirus(): Check the distance between health person and infected person, if distance <= SPREAD_RANGE, the health person has the probability of INFECTED_RATE to be infected the virus.
5. RecoveredOrDead(): For those who are infected, update their status(maintain infection or recover or die).
6. ClassifyPeople(): Classify People and save their coordinates for draw the results.

Execute above calculations at each time step until there is no one be infected.

Program Workflow

1. Initialization

• Set up an activity area and randomly assign poeple to the area.
• Set up all input parameters.

2. Run simulator(mathematic model)

3. Visualize the results(in visualize.py)

System Components

1. Parser: Python class. Parse user input parameters for simulation.
2. Simulator: C++ class. Use input parameters to simulate the spread of the virus.
3. Printer: Python class. Print simulation results with animation.

System Workflow

API Description

Python API

1. set_simulation_state(config): Set the simulation state according to the config set by the user in config.py and return the init simulation state.

   Parameters: config: The Global parameters in config.py.

   Returns: simulation state.

2. build_figure(): Build the figure shape and return the figure.

   Returns: figure, fig1, fig2.

3. run(simulation_state, figure, fig1, fig2, on_each_iter_updated, save_result): Run the simulator.

   Parameters: simulation_state: The init simulation state. figure: init figure. fig1: init fig1. fig2: init fig2. on_each_iter_updated: draw the simulation results in each iteration. save_result: save the final simulation results.

   Returns: None.

Engineering Infrastructure

Build System

The system uses "make" to compile the c++ code into *.so file as a python model. Then use "Poetry" to manage the dependencies of the python package.

Testing Framework

Python: pytest According to this article, these policies should show the following distribution(under same situation):

1. Free: exponential curve (smallest variance)

2. Attempted quarantine: flatten curve than Free (the second smallest variance)

3. Moderate distancing: flatten curve than Attempted quarantine (the third smallest variance)

4. Extensive distancing: flatten curve than Moderate distancing (largest variance)

Version control

git

Build environment

Install the dependencies with Poetry.

poetry install --no-dev

Compile the *.so file in the poetry virtual environment.

poetry run make

Set config

Modify simulation config in config.py before run simulation.

vim config.py

Run

Run simulation using the Poetry environment.

poetry run python3 main.py

The visualization results of the simulation will saved in ./output_images/Simulation_policy{policy number}.png.

Simulation result

config

# all input parameter
# total number of people for simulation(optional, default = 1000(<= 10000))
TOTAL_POPULATION = 1000
# init number of infected people(percentage)(optional, default =
# 1 %)
INFECTED_PEOPLE = 0.01
# moving speed of people(optional, default = 3)
MOVE_SPEED = 19
# virus infection rate(optional, default = 0.7)
INFECTED_RATE = 0.7
# virus mortality rate(optional, default = 0.001)
MORTALITY_RATE = 0.001
# recovery rate(optional, default = 0.001)
RECOVERY_RATE = 0.001
# healthcare capacity(optional, default = 100, The mortality rate is
# halved.)
HEALTHCARE_CAPACITY = 100

MODE = 2
# Max simulation step, default = 1000
SIMULATION_STEP = 1000
# the distance of move one step(every iter move one step), default = 0.001
MOVE_STEP = 0.001
# Virus transmission range, default = 0.02
SPREAD_RANGE = 0.02

POLICY = 0 - 4

# If policy = 2, the probability of an infected person being quarantined.
ACCEPT_ISOLATION_RATE = 0.95

# Simulation boundary
LEFT_X = 0.0
RIGHT_X = 2.0
UP_Y = 2.0
DOWN_Y = 0.0

policy = 0 : free

Final result

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policy = 1 : free with healthcare (healthcare can reduce the mortality rate by half.)

Final result

---

policy = 2 : Attempted quarantine (infected people will go to quarantine area.)

Final result

---

policy = 3 : Moderate distancing (60% people can not move)

Final result

---

policy = 4 : Extensive distancing (90% people can not move)

Final result

---

References

1. Why outbreaks like coronavirus spread exponentially, and how to “flatten the curve”

2. python_corona_simulation

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proposal

https://github.com/Eric860730/nsdhw_21au/tree/Eric860730-proposal-submission/proposal/Eric860730

Next goal

Increase customized policy.

TODO

⬜ Increase customized policy.

for performance

⬜ Use QT QUICK to accelerate visualization.