Evidence map›Paper›PMID 37988136›Full record

ArticleJMIR formative research2023

A Web-Based COVID-19 Tool for Testing Residents in Retirement Homes: Development Study.

Mansoor Davoodi, Ana Batista, Adam Mertel, Abhishek Senapati, Wildan Abdussalam, Jiri Vyskocil, Giuseppe Barbieri, Kai Fan, Weronika Schlechte-Welnicz, Justin M Calabrese

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Article in JMIR formative research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Mansoor Davoodi *Center for Advanced Systems Understanding, Görlitz, Germany.ORCID https://orcid.org/0000-0003-1010-4121
Ana BatistaCenter for Advanced Systems Understanding, Görlitz, Germany.ORCID https://orcid.org/0000-0002-3643-5802
Adam MertelCenter for Advanced Systems Understanding, Görlitz, Germany.ORCID https://orcid.org/0000-0002-3634-1857
Abhishek SenapatiCenter for Advanced Systems Understanding, Görlitz, Germany.ORCID https://orcid.org/0000-0003-1572-2658
Wildan AbdussalamCenter for Advanced Systems Understanding, Görlitz, Germany.ORCID https://orcid.org/0000-0002-4948-3934
Jiri VyskocilCenter for Advanced Systems Understanding, Görlitz, Germany.ORCID https://orcid.org/0000-0001-8822-0929
Giuseppe BarbieriCenter for Advanced Systems Understanding, Görlitz, Germany.ORCID https://orcid.org/0009-0006-5094-4896
Kai FanCenter for Advanced Systems Understanding, Görlitz, Germany.ORCID https://orcid.org/0000-0003-2570-0372
Weronika Schlechte-WelniczCenter for Advanced Systems Understanding, Görlitz, Germany.ORCID https://orcid.org/0000-0002-2835-3946
Justin M CalabreseCenter for Advanced Systems Understanding, Görlitz, Germany.ORCID https://orcid.org/0000-0003-0575-6408

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundLong-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.

objectiveWe 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.

methodsWe 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.

resultsConsidering 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.

conclusionsRecommended 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.

Indexed as

applicationCOVID-19optimized testingpandemicretirement homeweb application

Identifiers

PMID37988136
PMCPMC10664773

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.