Date:
Wed, 06/01/202112:00-13:30
Lecturer: Hilla De Leon from TIFPA and ECT*.
Abstract:
In our work, we use a Monte Carlo based algorithm and the basic principles of statistical physics to predict the Covid-19 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 each model's efficiency.
Also, we refer to the developments of vaccines for COVID-19 for modelling the spread of the Coronavirus in the case of an effective vaccine and a possible re-infection.
We show that a relatively high vaccination rate is needed (accompanied by social distancing and mask-wearing) to keep the infection rate under control without strict restriction. In addition, we examine the extent of additional reliefs/restrictions that accompany the population's vaccination on the degree of infection.
Abstract:
In our work, we use a Monte Carlo based algorithm and the basic principles of statistical physics to predict the Covid-19 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 each model's efficiency.
Also, we refer to the developments of vaccines for COVID-19 for modelling the spread of the Coronavirus in the case of an effective vaccine and a possible re-infection.
We show that a relatively high vaccination rate is needed (accompanied by social distancing and mask-wearing) to keep the infection rate under control without strict restriction. In addition, we examine the extent of additional reliefs/restrictions that accompany the population's vaccination on the degree of infection.