Evaluating efficacy of indoor non-pharmaceutical interventions against COVID-19 outbreaks with a coupled spatial-SIR agent-based simulation framework

Abstract

Contagious respiratory diseases, such as COVID-19, depend on sufficiently prolonged exposures for the successful transmission of the underlying pathogen. It is important that organizations evaluate the efficacy of non-pharmaceutical interventions aimed 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 an agent-based SIR model to assess the relative risks of different intervention strategies. By applying our model on MIT’s Stata center, 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 reducing the frequency at which individuals leave their workstations combined with lowering the number of individuals admitted below the current recommendations 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 agent-based SIR model, we compare relative infection risk of four respiratory illnesses, MERS, SARS, COVID-19, and Measles, within the simulated area, and recommend appropriate intervention guidelines.

COVID-19 Pandemic Risk Assessment: Systematic Review

“…Evaluation of the effectiveness of intervention strategies (eg, one-way movement versus unrestricted movement, frequency of leaving designated work locations for breaks, distance learning in primary and secondary schools, and so on) 2 [50,51] Evaluation of the control and prevention policies by government/policymakers 8 [18,24,29,37,[52][53][54][55] (Continued) The study locations examined by these 62 included articles comprised worldwide coverage. Four class types, based on the size of the geographic areas studied, were identified: Local areas (32.3% of articles), Global coverage (32.3% of articles), Specific areas (27.4% of articles), and Regional areas (6.5% of articles).…”

“…A contact tracing/network method with a SIR (Susceptible, Infected, and Recovered) model was incorporated to develop an enhanced spatio-SIR model/spatial agent-based SIR model 2 [36,50] A Susceptible, Exposed, Infectious, Recovered (SEIR) model was constructed and differential equations were used to obtain the relationships of model parameters 2 [32,54] The basic SIR model (Susceptible, Infectious, Recovered) was modified by considering more parameters, such as having been vaccinated, and whether a recovered person was reinfected, and using additional techniques such as differential equations and the Kendall ranking method 3 [16,20,65] Total 7…”

Stress Propagation in Human-Robot Teams Based on Computational Logic Model

“…These requirements describe the composite models, such as agent-based models in combination with a specific mechanism for stress propagation (such as SIS) [14], [19].…”

“…Work [35] extends the traditional SIR model by introducing exposed groups, asymptomatic and symptomatic infected people, and dead group, including spatio-temporal interactions regarding different buildings, social contexts, and countermeasures (isolation, distancing, mask wearing, vaccination). Spatial agentbased SIR models to evaluate the relative risk reduction of intervention strategies was introduced in [19]. Intervention strategies were decomposed into three dimensions, namely movement restriction, population capacity, hourly break probability.…”

An LBS and agent-based simulator for Covid-19 research

“…Many population travel studies are trying to estimate or extract population origin-destination matrices 14 or daily activity chains [15][16][17] , which are relatively high level results. In some studies, daily activity patterns for agents were assigned according to survey samples or empirical data 18,19 , therefore with limited diversity among agents. Moreover, many of the agents in the simulations for Covid-19 do not move according to the real road network.…”