COVID-19 transmission dynamics in close-contacts facilities: Optimizing control strategies

Abstract

Close-contact places such as long-term facilities have been found to be high-risk and high-morbidity places in the United States during COVID-19 outbreaks. This could be due to the presence of vulnerable resident population, frequent contacts of residents with visitors, staff working at multiple facilities, and potential contaminated surfaces at these facilities. Here, we model close-contacts places to evaluate the role of different transmission pathways of COVID-19 in the presence of adaptable interventions. The model captures a coupled dynamics between three subpopulations (staff, residents, and visitors) and incorporates infection from infectious individuals and through the environment. By using parameterization of the models via real data from facilities in the United States, we identify and quantify the impact of duration and choice of interventions in real time for subpopulations to mitigate disease burden. We study the trade-off between disease burden and prevention cost using cost-effectiveness analysis. It was found that the specific time interval in which an intervention is included has an important effect when maximizing the effectiveness while minimizing the costs. We find that the presence of super-spreader healthcare workers contribute to a significantly higher peak of the number of infected cases compared to similar situation in the presence of super-spreader residents. Considering two nonpharmaceutical interventions: the use of face masks and cleaning surfaces, we observe that the later is an optimum intervention at the beginning of the system’s evolution, while wearing masks becomes an optimum strategy once the infection is widely established and population behavior changes.

Keywords

• transmission modes
• masks
• cleaning surfaces
• healthcare facility
• mathematical models
• covid-19
• ordinary differential equations
• numerical analysis
• biology and other natural sciences
• 92d30