JMIR Publications

ABSTRACT

Background:

Long-term care facilities have been widely affected by the COVID-19 pandemic. Empirical evidence from the first wave of the COVID-19 pandemic demonstrated that elderly people are the most impacted and are at higher risk of mortality after being infected with SARS-CoV-2. Regularly testing care facility residents is a practical approach to detecting infections proactively and avoiding propagation. Thus, the care staff must perform the test on the residents while providing essential care, which in turn causes imbalances in their working time.

Objective:

Once an outbreak occurs, suppressing the spread of the virus in retirement homes 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 retirement homes. 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 develop a web application to assist retirement home managers in identifying effective testing schedules for residents that detect outbreaks as quickly as possible, while not overburdening already busy care staff with testing duties. The outcome of the application, called testing strategy, is based on dividing facility residents into groups and then testing no more than one group per day. It shows the optimal partitioning of the residents into some groups and specifies particular days on which they should be tested.

Methods:

We created the web application (Retirement Home Testing Optimizer) in collaboration with retirement homes and long-term care facilities (LCF) operated by the Diakonie Löbau-Zittau in Saxony (Germany), which provided data and insights important for various phases of the application development. By incorporating the influential factors such as the number of residents and staff, the average rate of contacts among them, the amount of time spent to test each resident and the amount of time on preparation and cleanup activities, and constraints on the test interval and size of groups, we develop Mixed Integer Nonlinear Programming (MINLP) models for balancing staff workload in LCFs while minimizing the expected detection time of a probable infection inside the facility. Additionally, by leveraging symmetries in the problem, we propose a fast and efficient local search method to find the optimal solution. Finally, application prototypes were discussed with potential users to ensure both plausibility of optimal testing strategies and maximal efficiency of the user interface design.

Results:

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 of residents. The current version of the application is deployed on the server of the Where2test project and is accessible on [1]. The application is open source, and all contents are offered in English and German languages. We provide comprehensive instructions and guidelines for easy use and understanding of the application functionalities. The application was launched in July 2022, and it is currently being tested in retirement homes in Saxony.

Conclusions:

Our web application was developed to empower LCF managers to introduce efficient testing strategies that minimize outbreak detection while balancing the tradeoff between time invested in testing and time spent providing essential care to residents. Recommended testing strategies are tailored to each retirement home 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, i.e., the expected risk of infection, and they do not guarantee the worst case scenarios.