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

Francesco Bellocchio, Paola Carioni, Caterina Lonati, Mario Garbelli, Francisco Martínez-Martínez, Stefano Stuard and Luca Neri
Additional contact information
Francesco Bellocchio: Fresenius Medical Care Italia SpA, Palazzo Pignano, 26020 Lombardia, Italy
Paola Carioni: Fresenius Medical Care Italia SpA, Palazzo Pignano, 26020 Lombardia, Italy
Caterina Lonati: Center for Preclinical Research, Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico, 20122 Milan, Italy
Mario Garbelli: Fresenius Medical Care Italia SpA, Palazzo Pignano, 26020 Lombardia, Italy
Francisco Martínez-Martínez: Santa Barbara Smart Health S. L., Parc Cientific Universitat id Valencia, Carrer del Catedràtic Agustín Escardino Benlloch, 9, 46980 Paterna, Spain
Stefano Stuard: Fresenius Medical Care Deutschland GmbH, 61352 Bad Homburg, Germany
Luca Neri: Fresenius Medical Care Italia SpA, Palazzo Pignano, 26020 Lombardia, Italy

IJERPH, 2021, vol. 18, issue 18, 1-18

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.

Keywords: SARS-CoV-2; COVID-19; sentinel surveillance system; outbreak prediction; machine learning; artificial intelligence (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/1660-4601/18/18/9739/pdf (application/pdf)
https://www.mdpi.com/1660-4601/18/18/9739/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jijerp:v:18:y:2021:i:18:p:9739-:d:636443

Access Statistics for this article

IJERPH is currently edited by Ms. Jenna Liu

More articles in IJERPH from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().