Pandemic lockdown, isolation, and exit policies based on machine learning predictions
Theodoros Evgeniou, Mathilde Fekom, Anton Ovchinnikov, Raphaël Porcher, Camille Pouchol, Nicolas Vayatis
| Year of publication: |
2023
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|---|---|
| Authors: | Evgeniou, Theodoros ; Fekom, Mathilde ; Ovchinnikov, Anton ; Porcher, Raphaël ; Pouchol, Camille ; Vayatis, Nicolas |
| Published in: |
Production and operations management : the flagship research journal of the Production and Operations Management Society. - London : Sage Publications, ISSN 1937-5956, ZDB-ID 2151364-8. - Vol. 32.2023, 5, p. 1307-1322
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| Subject: | COVID-19 | epidemic models | machine learning | personalized risk management | SIR | Künstliche Intelligenz | Artificial intelligence | Coronavirus | Epidemie | Epidemic | Risikomanagement | Risk management | Wirkungsanalyse | Impact assessment | Lockdown | Lock-down | Prognoseverfahren | Forecasting model |
| Type of publication: | Article |
|---|---|
| Type of publication (narrower categories): | Aufsatz in Zeitschrift ; Article in journal |
| Language: | English |
| Other identifiers: | 10.1111/poms.13726 [DOI] |
| Source: | ECONIS - Online Catalogue of the ZBW |
Persistent link: https://www.econbiz.de/10014266295