Academic Journal
Enhancing pandemic surveillance and testing: a simulation modeling study utilizing german multicenter data with federated machine learning.
| Τίτλος: | Enhancing pandemic surveillance and testing: a simulation modeling study utilizing german multicenter data with federated machine learning. |
|---|---|
| Συγγραφείς: | Kempter S; Department of Technology, Management, and Economics, Technical University of Denmark, Akademivej 358, 2800, Kongens Lyngby, Denmark.; Center for Excellence in Healthcare Operations Planning, Next Generation Technology, Technical University of Denmark, Fælledvej 11, 4200, Slagelse, Denmark., Brunner JO; Department of Technology, Management, and Economics, Technical University of Denmark, Akademivej 358, 2800, Kongens Lyngby, Denmark. jotbr@dtu.dk.; Health Care Operations/Health Information Management, Faculty of Business and Economics, Faculty of Medicine, University of Augsburg, Universitätsstraße 16, 86159, Augsburg, Germany. jotbr@dtu.dk.; Center for Excellence in Healthcare Operations Planning, Next Generation Technology, Technical University of Denmark, Fælledvej 11, 4200, Slagelse, Denmark. jotbr@dtu.dk.; Faculty III - Economics, Business Informatics, Business Law, University of Siegen, Kohlbettstraße 15, 57072, Siegen, Germany. jotbr@dtu.dk., Hanses F; Department for Infection Control and Infectious Diseases, University Hospital Regensburg, Franz-Josef-Strauß-Allee 11, 93053, Regensburg, Germany., Spinner C; TUM School of Medicine and Health, Department of Clinical Medicine, Clinical Departments for Internal Medicine II, University Medical Center, Technical University of Munich, Ismaninger Str. 22, 93053, Munich, Germany., Zabel LT; Laboratory Medicine, Alb Fils Kliniken GmbH, Eichertstraße 3, 73035, Göppingen, Germany., Römmele C; Clinic for Internal Medicine III - Gastroenterology and Infectious Diseases, University Hospital Augsburg, Stenglinstraße 2, 86156, Augsburg, Germany., Borgmann S; Infectious Diseases and Infection Control, Ingolstadt Hospital, Krumenauerstraße 25, 85049, Ingolstadt, Germany., Vehreschild JJ; Department II of Internal Medicine, Hematology/Oncology, Goethe University, Theodor-Stern-Kai 7, 60590, Frankfurt, Germany., Bartenschlager CC; Anaesthesiology and Operative Intensive Care, University Hospital of Augsburg, Stenglinstraße 2, 86156, Augsburg, Germany.; Applied Data Science in Health Care, Ohm University of Applied Sciences Nürnberg, Wassertorstraße 10, 90489, Nürnberg, Germany. |
| Πηγή: | Health care management science [Health Care Manag Sci] 2026 Mar 14; Vol. 29 (1). Date of Electronic Publication: 2026 Mar 14. |
| Τύπος έκδοσης: | Journal Article |
| Γλώσσα: | English |
| Στοιχεία περιοδικού: | Publisher: Baltzer Science Publishers Country of Publication: Netherlands NLM ID: 9815649 Publication Model: Electronic Cited Medium: Internet ISSN: 1572-9389 (Electronic) Linking ISSN: 13869620 NLM ISO Abbreviation: Health Care Manag Sci Subsets: MEDLINE |
| Imprint Name(s): | Original Publication: Bussum, Netherlands : Baltzer Science Publishers, c1998- |
| Ιατρικοί όροι (MeSH): | COVID-19*/diagnosis , Pneumonia, Viral*/diagnosis , Pneumonia, Viral*/epidemiology , Federated Learning* , Pandemics* , Machine Learning*, Germany/epidemiology ; Humans ; SARS-CoV-2 ; Computer Simulation ; COVID-19 Testing |
| Περίληψη: | The COVID-19 pandemic has starkly exposed queryPlease check author names and affiliation if presented correctly.vulnerabilities in the management of surveillance and testing. Significant challenges associated with physical tests, i.e., PCR and antigen tests, include their high cost, resource-intensive nature, turnaround time, and sensitivity. Although the literature has underscored the potential of Machine Learning-based methods for the digital diagnosis of COVID-19, developing high-performing models crucially depends on extensive datasets exceeding the amount available in one healthcare institution. Federated Machine Learning offers a solution to that dilemma. The aim of this research is to evaluate the potential impact of Federated Learning-based digital COVID-19 diagnosis on the trajectory of a pandemic. Therefore, we design a multidimensional evaluation framework, consisting of a simulation study utilizing real-world lab parameters from multiple hospitals and a newly developed performance indicator, named Testing Evaluation for Pandemics. We find that Federated Learning can significantly support the decision-making process of diagnosing COVID-19 at the beginning of a pandemic while saving scarce resources. However, a warm-up phase is needed until constant performance similar to physical tests is reached. In addition, lab parameters have a high prediction power for the diagnosis and are well suited because of patient welfare reasons. |
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| Contributed Indexing: | Keywords: COVID-19; Federated machine learning; Multicentre data; Simulation; hospitals |
| Entry Date(s): | Date Created: 20260314 Date Completed: 20260627 Latest Revision: 20260627 |
| Update Code: | 20260628 |
| PubMed Central ID: | PMC12988995 |
| DOI: | 10.1007/s10729-025-09752-4 |
| PMID: | 41831104 |
| Βάση Δεδομένων: | MEDLINE |
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