Davoodi Mansoor, Batista Ana, Mertel Adam, Senapati Abhishek, Abdussalam Wildan, Vyskocil Jiri, Barbieri Giuseppe, Fan Kai, Schlechte-Welnicz Weronika, M Calabrese Justin
Center for Advanced Systems Understanding, Görlitz, Germany.
Helmholtz-Zentrum Dresden-Rossendorf, Dresden, Germany.
JMIR Form Res. 2023 Nov 21;7:e45875. doi: 10.2196/45875.
Long-term care facilities have been widely affected by the COVID-19 pandemic. Empirical evidence demonstrated that older people are the most impacted and are at higher risk of mortality after being infected. Regularly testing care facility residents is a practical approach to detecting infections proactively. In many cases, the care staff must perform the tests on the residents while also providing essential care, which in turn causes imbalances in their working time. Once an outbreak occurs, suppressing the spread of the virus in retirement homes (RHs) is challenging because the residents are in contact with each other, and isolation measures cannot be widely enforced. Regular testing strategies, on the other hand, have been shown to effectively prevent outbreaks in RHs. However, high-frequency testing may consume substantial staff working time, which results in a trade-off between the time invested in testing and the time spent providing essential care to residents.
We developed a web application (Retirement Home Testing Optimizer) to assist RH managers in identifying effective testing schedules for residents. The outcome of the app, called the "testing strategy," is based on dividing facility residents into groups and then testing no more than 1 group per day.
We created the web application by incorporating influential factors such as the number of residents and staff, the average rate of contacts, the amount of time spent to test, and constraints on the test interval and size of groups. We developed mixed integer nonlinear programming models for balancing staff workload in long-term care facilities while minimizing the expected detection time of a probable infection inside the facility. Additionally, by leveraging symmetries in the problem, we proposed a fast and efficient local search method to find the optimal solution.
Considering the number of residents and staff and other practical constraints of the facilities, the proposed application computes the optimal trade-off testing strategy and suggests the corresponding grouping and testing schedule for residents. The current version of the application is deployed on the server of the Where2Test project and is accessible on their website. The application is open source, and all contents are offered in English and German. We provide comprehensive instructions and guidelines for easy use and understanding of the application's functionalities. The application was launched in July 2022, and it is currently being tested in RHs in Saxony, Germany.
Recommended testing strategies by our application are tailored to each RH and the goals set by the managers. We advise the users of the application that the proposed model and approach focus on the expected scenarios, that is, the expected risk of infection, and they do not guarantee the avoidance of worst-case scenarios.
长期护理机构受到了新冠疫情的广泛影响。实证证据表明，老年人受影响最大，感染后死亡风险更高。定期对护理机构居民进行检测是主动发现感染的一种切实可行的方法。在许多情况下，护理人员必须在为居民提供基本护理的同时对其进行检测，这反过来又导致了他们工作时间的失衡。一旦发生疫情，在养老院抑制病毒传播具有挑战性，因为居民之间相互接触，隔离措施无法广泛实施。另一方面，定期检测策略已被证明能有效预防养老院的疫情爆发。然而，高频检测可能会消耗大量工作人员的工作时间，这就导致了在检测投入时间和为居民提供基本护理所花费时间之间的权衡。
我们开发了一个网络应用程序（养老院检测优化器），以协助养老院管理人员确定针对居民的有效检测时间表。该应用程序的结果，即“检测策略”，是基于将机构居民分成若干组，然后每天检测不超过一组。
我们通过纳入居民和工作人员数量、平均接触率、检测所花费的时间以及检测间隔和组规模的限制等影响因素来创建这个网络应用程序。我们开发了混合整数非线性规划模型，以平衡长期护理机构工作人员的工作量，同时将机构内可能感染的预期检测时间降至最低。此外，通过利用问题中的对称性，我们提出了一种快速有效的局部搜索方法来找到最优解。
考虑到居民和工作人员数量以及机构的其他实际限制，所提出的应用程序计算出最优的权衡检测策略，并为居民建议相应的分组和检测时间表。该应用程序的当前版本部署在Where2Test项目的服务器上，可在其网站上访问。该应用程序是开源的，所有内容均提供英文和德文版本。我们提供了全面的说明和指南，以便于使用和理解该应用程序的功能。该应用程序于2022年7月推出，目前正在德国萨克森州的养老院进行测试。
我们应用程序推荐的检测策略是根据每个养老院及其管理人员设定的目标量身定制的。我们建议该应用程序的用户，所提出的模型和方法侧重于预期情况，即预期感染风险，它们并不能保证避免最坏情况。
