Covid-19 in hospitals: Studying influencing factors through agent-based modelling - Philippos Michaelides; Ştefan Sarkadi (PLoS ONE, 2025)
Hospitals are highly dynamic environments where Covid-19 is highly transmissible if effective measures are not taken. General preventive policies are not necessarily effective; however, agent-based modelling can offer a way to tailor policies in such specific settings. In this paper, we develop an agent-based model to simulate a hospital as a multi-agent system and examine the influence of well-established Covid-19 factors on transmission, based on the Susceptible-Exposed-Infected-Recovered epidemic model. These factors include the mask efficacy, the mask-wearing percentage, the vaccination percentage, the room ventilation, the distancing rule, and the screening policy. This study, conducted in 2023, simulates agent interactions in a hospital setting using Python. We apply different parametrisations and employ a local sensitivity analysis to isolate the effect of each factor on transmission. We perform statistical analysis using independent two-sample t-tests, with a significance threshold of p<0.05. When worn by the entire population, N95 masks effectively mitigate transmission. They are more effective than surgical masks and substantially more effective than not wearing masks. Surgical masks, especially when worn by all individuals, also have a significant impact. Vaccination is highly effective, particularly when the entire population is vaccinated, and becomes critical when mask protection is weaker. Ventilation and distancing show minimal impact when N95 masks are used entirely within the hospital; alternatively, their effect is noteworthy but not highly decisive. Finally, the screening policy substantially affects transmission when a high percentage of entrants are tested, especially when surgical masks or no masks are worn. The findings highlight the need for prioritising mask-wearing and vaccination compliance, combined with a comprehensive screening policy. The model is highly parameterisable and can be adjusted to simulate other hospital settings or other infectious diseases, serving as a decision-making tool. Future studies could explore the combined effects of multiple interventions and validate the model with empirical data.