COVID-19 modeling and non-pharmaceutical interventions in an outpatient dialysis unit

Author summary As we write this, the COVID-19 pandemic has essentially taken over the world, with more than 20 million cases spread over 216 countries. A big concern for policy makers all across the world has been the impact of COVID-19 on healthcare systems and whether these systems can cope with the enormous strain placed on them by COVID-19. In this paper, we consider the spread of COVID-19 in a specific healthcare setting: the outpatient dialysis unit. Hemodialysis patients are extremely vulnerable to infections in large part due to multiple immune-system deficiencies associated with renal failure and hemodialysis. Hemodialysis facilities also increase the risk of COVID-19 transmission because each patient is in frequent, close contact with other patients and healthcare personnel. Thus, a dialysis unit can be seen as a microcosm for the worst-case impacts of COVID-19 in a healthcare setting. In this manuscript, we show via high-fidelity modeling and simulations that under pessimistic modeling assumptions, there is a combination of relatively simple, inexpensive, and practical non-pharmaceutical interventions that can substantially lower the impact of COVID-19 in the dialysis unit. Our simulations are based on fine-grained healthcare personnel movement data that we make available for other modelers to use. This paper describes a data-driven simulation study that explores the relative impact of several low-cost and practical non-pharmaceutical interventions on the spread of COVID-19 in an outpatient hospital dialysis unit. The interventions considered include: (i) voluntary self-isolation of healthcare personnel (HCPs) with symptoms; (ii) a program of active syndromic surveillance and compulsory isolation of HCPs; (iii) the use of masks or respirators by patients and HCPs; (iv) improved social distancing among HCPs; (v) increased physical separation of dialysis stations; and (vi) patient isolation combined with preemptive isolation of exposed HCPs. Our simulations show that under conditions that existed prior to the COVID-19 outbreak, extremely high rates of COVID-19 infection can result in a dialysis unit. In simulations under worst-case modeling assumptions, a combination of relatively inexpensive interventions such as requiring surgical masks for everyone, encouraging social distancing between healthcare professionals (HCPs), slightly increasing the physical distance between dialysis stations, and-once the first symptomatic patient is detected-isolating that patient, replacing the HCP having had the most exposure to that patient, and relatively short-term use of N95 respirators by other HCPs can lead to a substantial reduction in both the attack rate and the likelihood of any spread beyond patient zero. For example, in a scenario with R-0 = 3.0, 60% presymptomatic viral shedding, and a dialysis patient being the infection source, the attack rate falls from 87.8% at baseline to 34.6% with this intervention bundle. Furthermore, the likelihood of having no additional infections increases from 6.2% at baseline to 32.4% with this intervention bundle.