Abstract
With older age and multiple comorbidities, dialysis patients are at high risk for serious complications, even death,
from COVID-19. There is a large disproportionate representation of minorities, especially Blacks and Hispanics.
Over 85% of hemodialysis patients travel three times a week to dialysis facilities to receive life-sustaining
treatments and cannot shelter in place. There is a critical need to characterize COVID-19 transmission pathways
in dialysis patients and clinics, identify potential coronavirus carriers, and develop procedures to curb the spread.
With regular medical encounters, a large amount of data has been collected for each patient over time. These
data have not been fully utilized for COVID-19 prediction and control in dialysis clinics. In this proposal, we seek
to leverage demographic, clinical, treatment, laboratory, socioeconomic, serological, metabolomic, wearable and
machine-integrated sensors, and COVID-19 surveillance data to develop mathematical and statistical models
and implement them in a large number of dialysis clinics. The mathematical and statistical modeling using
multiple data resources will help us understand how COVID-19 spread in dialysis facilities, identify potential
COVID-19 patients before symptoms appear, and identify potential asymptomatic COVID-19 patients. We will
develop novel mathematical and statistical models that fully utilize the high dimensional multimodal data
available to us and other dialysis providers. We capitalize on the intrinsic advantages of hemodialysis clinics to
implement and validate the proposed prediction models. We firmly believe that this cross-disciplinary effort will
improve patients’ and staff’s safety while delivering high-quality, individualized care to a high-risk population.
