Infection Simulator KYLA DE VILLA Monte Carlo Simulations

Infection Simulator KYLA DE VILLA

Monte Carlo Simulations • Used to obtain numerical results by randomly sampling from probability distributions for variables

My project: Monte Carlo Simulation of Infection Spread • Built a community of 300 people in a 50 x 50 room, making 1 random person infected • Each person is iteratively moved • Constant distance moved each time • New microstate suggested = random angle for direction of motion • Probability of acceptance is based on difference between current angle of motion and suggested angle. To more accurately model human movement, more similar angles are accepted with a higher probability

My project: Monte Carlo Simulation of Infection Spread • Each infected person will infect others who come within their “infection radius” • Each person stays infected for 10 iterations • During each iteration, the infected will have a chance of dying that is proportional to their age • Based loosely on real statistics (0. 0005 x age per iteration) • 25 years old = 0. 125% overall; 100 years old = 0. 5% overall • If the number of sick people exceeds 1/3 of the population, the probability of dying in any iteration is doubled (0. 0010 x age per iteration)

My project: Monte Carlo Simulation of Infection Spread • After 10 iterations, the infected will transition to “recovered, ” and will no longer be contagious or susceptible to infection. • The total number of healthy, sick, recovered, and dead individuals are tracked in each iteration.

References Cover Photo: https: //www. ceps. eu/ceps-publications/chronicle-of-a-pandemic-foretold/ Inspiration for this project: https: //www. washingtonpost. com/graphics/2020/world/coronasimulator/