COVID-19_simulation_Julia

COVID-19 spreading simulation, Julia translation of https://github.com/MuAuan/collective_particles with several changes and new functionalities.

Model description

num_particles particles are simulated over max_iteration temporal iteration.
Computational domain is [0, x_range] × [0, y_range].
Initial particle velocity (vel_x, vel_y) obeys Gaussian distribution, with average vel_mean and standard deviation vel_σ.
ratio_infection_init percentage of particles are initially infected by the virus.
Particles in the nearby (below relative distance radius_infection) of the infected particle can be infected by the chance rate infection_chance per time step.
Infected particles (deterministically) recover after recovery_time steps and never infected again.
During temporal development, particle velocity alters by up to vel_flc.

flag_multiple_infection allows recovered particles to re-infect the virus.
- In the case of multiple infection, infection_chance is modified by number of past infection.
flag_infected_isolation forces infected particles to stop their movement during infection.

The code is tested on Julia version 1.3.1.
./fig/ subdirectory is needed for gif video.
Module dependencies are ProgressMeter, Distributions, Printf and Plots with GR backend.