Particle modeling of the spreading of Coronavirus Disease (COVID-19)
By the end of June 2020, the COVID-19 pandemic had infected nearly ten million people and had spread to almost all countries worldwide. In response, many countries all over the world have used different methods to reduce the infection rate, such as including case isolation, the closure of schools and universities, banning public events, and mostly forcing social distancing, including local and national lockdowns. In our work, we use a Monte-Carlo (MC) based algorithm to predict the virus infection rate for different population densities using the most recent epidemic data. We test the spread of the Coronavirus using three different lockdown models, and eight various combinations of constraints, which allow us to examine the efficiency of each model and constraint. The main prediction of this model is that a cyclic schedule of one week without restrictions and two weeks of lockdown can help to control the virus infection. In particular, this model reduces the infection rate when accompanied by social distancing and complete isolation of symptomatic patients.
SpeakerECT*/TIFPAI am interested in a few- and many-body low-energy systems that can be studied using diffusion Monte Carlo (DMC). In particular focus on bosonic and fermionic systems that can be described using a pionless effective field potential.