Modelling the Impact of Robotics on Infectious Spread Among Healthcare Workers

Abstract

The Coronavirus disease 2019 (Covid-19) pandemic has brought the world to a standstill. Healthcare systems are critical to maintain during pandemics, however, providing service to sick patients has posed a hazard to frontline healthcare workers (HCW) and particularly those caring for elderly patients. Various approaches are investigated to improve safety for HCW and patients. One promising avenue is the use of robots. Here, we model infectious spread based on real spatio-temporal precise personal interactions from a geriatric unit and test different scenarios of robotic integration. We find a significant mitigation of contamination rates when robots specifically replace a moderate fraction of high-risk healthcare workers, who have a high number of contacts with patients and other HCW. While the impact of robotic integration is significant across a range of reproductive number R 0 , the largest effect is seen when R 0 is slightly above its critical value. Our analysis suggests that a moderate-sized robotic integration can represent an effective measure to significantly reduce the spread of pathogens with Covid-19 transmission characteristics in a small hospital unit.