Early detection, containment, and management of COVID-19 in dialysis facilities using multi-modal data sources

AbstractWith 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-sustainingtreatments and cannot shelter in place. There is a critical need to characterize COVID-19 transmission pathwaysin 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. Thesedata have not been fully utilized for COVID-19 prediction and control in dialysis clinics. In this proposal, we seekto leverage demographic, clinical, treatment, laboratory, socioeconomic, serological, metabolomic, wearable andmachine-integrated sensors, and COVID-19 surveillance data to develop mathematical and statistical modelsand implement them in a large number of dialysis clinics. The mathematical and statistical modeling usingmultiple data resources will help us understand how COVID-19 spread in dialysis facilities, identify potentialCOVID-19 patients before symptoms appear, and identify potential asymptomatic COVID-19 patients. We willdevelop novel mathematical and statistical models that fully utilize the high dimensional multimodal dataavailable to us and other dialysis providers. We capitalize on the intrinsic advantages of hemodialysis clinics toimplement and validate the proposed prediction models. We firmly believe that this cross-disciplinary effort willimprove patients' and staff's safety while delivering high-quality, individualized care to a high-risk population.