Details zur Publikation |
| Kategorie | Textpublikation |
| Referenztyp | Zeitschriften |
| DOI | 10.1007/s10957-025-02843-w |
Lizenz  |
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| Titel (primär) | Optimal testing strategies in long-term care facilities during pandemics |
| Autor | Davoodi, M.; Batista, A.; Senapati, A.; Schlechte-Welnicz, W.; Wagner, B.; Calabrese, J.M. |
| Quelle | Journal of Optimization Theory and Applications |
| Erscheinungsjahr | 2025 |
| Department | OESA |
| Band/Volume | 208 |
| Seite von | art. 38 |
| Sprache | englisch |
| Topic | T5 Future Landscapes |
| Keywords | Testing strategy; Scheduling; Long-term care; Retirement home; Symmetry property; COVID-19 pandemic |
| Abstract | The COVID-19 pandemic has significantly impacted long-term care facilities, with retirement homes being particularly vulnerable due to the high mortality risk among infected elderly residents. Once an outbreak occurs, containing the virus is challenging due to frequent resident interactions and limited isolation measures. While regular testing has proven effective in preventing outbreaks, high-frequency testing can strain staff resources, creating a trade-off between testing efforts and essential care provision. This paper addresses this challenge by proposing two novel optimization models for testing schedules that minimize infection risk while balancing staff workload. Using a probabilistic approach, the models incorporate factors such as contact rates, incidence status, and infection probabilities among residents. To solve these models, we introduce an enhanced local search algorithm that leverages the symmetry property of optimal solutions. Experimental results demonstrate the effectiveness of the proposed approach, outperforming a genetic algorithm in deriving optimal testing strategies. |
| dauerhafte UFZ-Verlinkung | https://www.ufz.de/index.php?en=20939&ufzPublicationIdentifier=31633 |
| Davoodi, M., Batista, A., Senapati, A., Schlechte-Welnicz, W., Wagner, B., Calabrese, J.M. (2025): Optimal testing strategies in long-term care facilities during pandemics J. Optim. Theory Appl. 208 , art. 38 10.1007/s10957-025-02843-w |
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