Particle modeling of the spreading of Coronavirus Disease (COVID-19)

TL;DR

This paper uses a Monte-Carlo simulation to analyze COVID-19 spread under various lockdown and social distancing scenarios, finding cyclic lockdown schedules of at least ten days can effectively control infection rates.

Contribution

It introduces a Monte-Carlo based model to evaluate the effectiveness of different lockdown patterns and constraints on COVID-19 spread.

Findings

Cyclic lockdown schedules with at least ten days reduce infection rates.

Social distancing and isolation enhance lockdown effectiveness.

Model predicts optimal timing for lockdown cycles.

Abstract

By the end of July 2020, the COVID-19 pandemic had infected more than seventeen 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. We use a Monte-Carlo (MC) based algorithm to predict the virus infection rate for different population densities using the most recent epidemic data in our work. 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. In this paper, we have tested three different time-cyclic patterns of no-restrictions/lockdown patterns. This model's main prediction is that a cyclic schedule of no-restrictions/lockdown that contains at least ten days of lockdown for each time cycle can help control the virus infection. In particular, this model reduces the infection rate when accompanied by social distancing and complete isolation of symptomatic patients.