**1. Introduction**

Due to its unique characteristics, the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) pandemic has posed unprecedented challenges to clinics providing lifesaving services to patients suffering from chronic illnesses, including chronic kidney disease (CKD). In fact, non-specific clinical manifestations of Coronavirus disease 2019 (COVID-19) [1] as well as the viral transmission from asymptomatic or pre-symptomatic individuals [2–4] make the early recognition of newly infected cases extremely difficult. Moreover, the occurrence of superspreading events (SSEV), during which few individuals are able to infect many people [5], hampers infection control measures [6,7].

Social distancing, preventive quarantine, and the isolation of infected subjects still represents the most effective means to reduce the risk of SARS-CoV-2 human-to-human transmission [8,9]. However, patients with end-stage kidney disease (ESKD) need to undergo in-center dialysis three times per week for 4 h per session, which makes physical distancing more difficult to achieve due to repeated, prolonged interactions with other patients and healthcare staff [10–13]. Unfortunately, ESKD individuals also show a higher risk of complications following SARS-CoV-2 infection due to weakened immune response [14–17] and to the occurrence of many of the risk factors commonly associated with development of severe COVID-19 [18,19], including older age and comorbidities [20,21]. Moreover, because of compromised host immunity, a vaccine may not exhibit the same efficacy on hemodialysis patients as it does in immunocompetent individuals [13].

Therefore, the reduction of the contagion risk within dialysis clinics while preserving clinical operations is a key challenge for healthcare systems during this pandemic. To help anticipate local epidemic dynamics and adjust non-pharmacological interventions to the changing background of infection risk, we sought to develop an advanced sentinel surveillance system supported by a machine learning (ML) prediction model, where the occurrence of COVID-19 cases in a clinic propagates distance-weighted risk estimates to adjacent dialysis units. The present study describes the derivation and validation of the prediction model, as well as the strategies adopted to monitor its performance throughout the pandemic period.
