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Article

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

1
Fresenius Medical Care Italia SpA, Palazzo Pignano, 26020 Lombardia, Italy
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Center for Preclinical Research, Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico, 20122 Milan, Italy
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Santa Barbara Smart Health S. L., Parc Cientific Universitat id Valencia, Carrer del Catedràtic Agustín Escardino Benlloch, 9, 46980 Paterna, Spain
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Fresenius Medical Care Deutschland GmbH, 61352 Bad Homburg, Germany
*
Author to whom correspondence should be addressed.
Current address: Clinical & Data Intelligence Systems—Advanced Analytics, Fresenius Medical Care Deutschland GmbH, Via Papa Giovanni Paolo II, 41, 26020 Vaiano Cremasco, Italy.
Academic Editors: Shang-Ming Zhou and Paul B. Tchounwou
Int. J. Environ. Res. Public Health 2021, 18(18), 9739; https://doi.org/10.3390/ijerph18189739
Received: 13 July 2021 / Revised: 9 September 2021 / Accepted: 11 September 2021 / Published: 16 September 2021
(This article belongs to the Special Issue Data Science in Healthcare)
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. View Full-Text
Keywords: SARS-CoV-2; COVID-19; sentinel surveillance system; outbreak prediction; machine learning; artificial intelligence SARS-CoV-2; COVID-19; sentinel surveillance system; outbreak prediction; machine learning; artificial intelligence
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MDPI and ACS Style

Bellocchio, F.; Carioni, P.; Lonati, C.; Garbelli, M.; Martínez-Martínez, F.; Stuard, S.; Neri, L. Enhanced Sentinel Surveillance System for COVID-19 Outbreak Prediction in a Large European Dialysis Clinics Network. Int. J. Environ. Res. Public Health 2021, 18, 9739. https://doi.org/10.3390/ijerph18189739

AMA Style

Bellocchio F, Carioni P, Lonati C, Garbelli M, Martínez-Martínez F, Stuard S, Neri L. Enhanced Sentinel Surveillance System for COVID-19 Outbreak Prediction in a Large European Dialysis Clinics Network. International Journal of Environmental Research and Public Health. 2021; 18(18):9739. https://doi.org/10.3390/ijerph18189739

Chicago/Turabian Style

Bellocchio, Francesco, Paola Carioni, Caterina Lonati, Mario Garbelli, Francisco Martínez-Martínez, Stefano Stuard, and Luca Neri. 2021. "Enhanced Sentinel Surveillance System for COVID-19 Outbreak Prediction in a Large European Dialysis Clinics Network" International Journal of Environmental Research and Public Health 18, no. 18: 9739. https://doi.org/10.3390/ijerph18189739

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