Modelling the Impact of Robotics on Infectious Spread Among Healthcare Workers - Vicente, Raul; Mohamed, Youssef; Eguíluz, Victor M.; Zemmar, Emal; Bayer, Patrick; Neimat, Joseph S.; Hernesniemi, Juha; Nelson, Bradley J.; Zemmar, Ajmal (Frontiers in Robotics and AI, 2021)
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 of 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 R0, the largest effect is seen when R0 is slightly above its critical value. Our data suggests that robots represent an effective alternative to protect HCW and patients without disrupting healthcare services during a pandemic.