Enhanced Sentinel Surveillance System for COVID-19 Outbreak Prediction in a Large European Dialysis Clinics Network.

Francesco Bellocchio,Luca Neri,Stefano Stuard,Paola Carioni,Caterina Lonati,Francisco Martínez-Martínez,Mario Garbelli

doi:10.3390/ijerph18189739

Abstract

Accurate predictions of COVID-19 epidemic dynamics may enable timely organizational interventions in high-risk regions. We exploited the interconnection of the Fresenius Medical Care (FMC) European dialysis clinic network to develop a sentinel surveillance system for outbreak prediction. We developed an artificial intelligence-based model considering the information related to all clinics belonging to the European Nephrocare Network. The prediction tool provides risk scores of the occurrence of a COVID-19 outbreak in each dialysis center within a 2-week forecasting horizon. The model input variables include information related to the epidemic status and trends in clinical practice patterns of the target clinic, regional epidemic metrics, and the distance-weighted risk estimates of adjacent dialysis units. On the validation dates, there were 30 (5.09%), 39 (6.52%), and 218 (36.03%) clinics with two or more patients with COVID-19 infection during the 2-week prediction window. The performance of the model was suitable in all testing windows: AUC = 0.77, 0.80, and 0.81, respectively. The occurrence of new cases in a clinic propagates distance-weighted risk estimates to proximal dialysis units. Our machine learning sentinel surveillance system may allow for a prompt risk assessment and timely response to COVID-19 surges throughout networked European clinics.

Highlights

Due to its unique characteristics, the Severe Acute Respiratory Syndrome Coronavirus2 (SARS-CoV-2) pandemic has posed unprecedented challenges to clinics providing lifesaving services to patients suffering from chronic illnesses, including chronic kidney disease (CKD)
Non-specific clinical manifestations of Coronavirus disease 2019 (COVID-19) [1] as well as the viral transmission from asymptomatic or pre-symptomatic individuals [2,3,4] make the early recognition of newly infected cases extremely difficult
The present study describes the development and validation of a novel sentinel surveillance system allowing for the prompt risk assessment of a COVID-19 outbreak in a large European network of dialysis clinics over a 2-week forecasting horizon

Summary

Introduction

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). Non-specific clinical manifestations of Coronavirus disease 2019. (COVID-19) [1] as well as the viral transmission from asymptomatic or pre-symptomatic individuals [2,3,4] make the early recognition of newly infected cases extremely difficult. 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]. Patients with end-stage kidney disease (ESKD) need to. ESKD individuals show a higher risk of complications following SARS-CoV-2 infection due to weakened immune response

Results

Discussion

Conclusion