Monte Carlo Simulations of Infection Spread in Indoor Environment

TL;DR

This paper uses Monte Carlo simulations to model short-term infection spread in indoor environments, focusing on peer-to-peer transmission and the effects of population fluctuations to inform social distancing guidelines.

Contribution

It introduces a simple Monte Carlo model for indoor infection dynamics that accounts for population fluctuations and provides insights for social distancing strategies.

Findings

Large fluctuations in infection spread due to finite population size.

Effectiveness of social distancing guidelines in indoor settings.

Model applicability to peer-to-peer airborne transmission.

Abstract

The dynamics of infection spread in populations has received popular attention since the outbreak of Covid-19 and many statistical models have been developed. One of the interesting areas of research is short-time dynamics in confined, indoor environments. We have modeled this using a simple Monte Carlo scheme. Our model is generally applicable for the peer-to-peer transmission case, when the infection spread occurs only between an infected subject and a healthy subject with a certain probability, i.e., airborne and surface transmission is neglected. The probability of infection spread is incorporated using a simple exponential decay with distance between the subjects. Simulations are performed for the cases of (1) constant subject population and (2) variable subject population due to inflow/outflow. We specifically focus on the large fluctuations in the dynamics due to finite number of subjects. Results of our study may be useful to determine social-distancing guidelines in indoor contexts.