An agent-based model to assess the impact of shared staff and occupancy rate on infectious disease burden in nursing homes

Infectious diseases can propagate between nursing homes through asymptomatic staff members who are employed at multiple facilities. However, the transmission dynamics of infections, both within individual nursing homes and across facilities, has been less investigated. To fill this gap, we developed an agent-based model of two nursing homes extendible to a network of n nursing homes connected with different percentages of shared staff. Focusing on the outbreaks of COVID-19 in U.S. nursing homes, we calibrated the model according to the COVID-19 prevalence data and estimated levels of shared staff for each State. The model simulations indicate that reducing the percentage of shared staff below 5% plays a significant role in controlling the spread of infection from one nursing home to another through personal protective equipment usage, rapid testing, and vaccination. As the percentage of shared staff increases to more than 30%, these measures become less effective, and the mean prevalence of infection reaches a steady state in both nursing homes. The hazard ratios for infection and mortality indicate that nursing homes with higher occupancy rates are more significantly affected by increased staff-sharing percentages. In conclusion, the burden of infection significantly increases with greater staff sharing between nursing homes, particularly in high-occupancy facilities, where transmission dynamics are amplified due to greater resident density and staff interactions.