Evaluating Efficacy of Indoor Non-Pharmaceutical Interventions against COVID-19 Outbreaks with a Coupled Spatial-SIR Agent-Based Simulation Framework

TL;DR

This paper introduces a coupled spatial-SIR agent-based simulation framework to evaluate indoor COVID-19 intervention strategies, revealing that reducing population size and break frequency effectively lowers infection risk.

Contribution

The study develops a novel simulation framework combining spatial movement and epidemiological models to assess intervention efficacy in indoor settings.

Findings

Lowering population size reduces infection risk.

One-way movement restrictions have minimal impact.

Reducing break frequency decreases contact network connectivity.

Abstract

Contagious respiratory diseases, such as COVID-19, depend on sufficiently prolonged exposures for the successful transmission of the underlying pathogen. It is important for organizations to evaluate the efficacy of interventions aiming at mitigating viral transmission among their personnel. We have developed a operational risk assessment simulation framework that couples a spatial agent-based model of movement with a SIR epidemiological model to assess the relative risks of different intervention strategies. By applying our model on MIT's STATA building, we assess the impacts of three possible dimensions of intervention: one-way vs unrestricted movement, population size allowed onsite, and frequency of leaving designated work location for breaks. We find that there is no significant impact made by one-way movement restrictions over unrestricted movement. Instead, we find that a combination of lowering the number of individuals admitted below the current recommendations and advising individuals to reduce the frequency at which they leave their workstations lowers the likelihood of highly connected individuals within the contact networks that emerge, which in turn lowers the overall risk of infection. We discover three classes of possible interventions based on their epidemiological effects. By assuming a direct relationship between data on secondary attack rates and transmissibility in the SIR model, we compare relative infection risk of four respiratory diseases, MERS, SARS, COVID-19, and Measles, within the simulated area, and recommend appropriate intervention guidelines.