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Model-based Evaluation of Continued COVID-19 Risk at Long Term Care Facilities
Preprint
in English
| medRxiv
| ID: ppmedrxiv-21259931
ABSTRACT
The COVID-19 pandemic severely impacted long-term care facilities resulting in the death of approximately 8% of residents nationwide. As COVID-19 case rates decline and state and county restrictions are lifted, facility managers, local and state health agencies are challenged with defining their own policies moving forward to appropriately mitigate disease transmission. The continued emergence of variants of concern has highlighted the need for a readily available tool that can be employed at the facility-level to determine best practices for mitigation and ensure resident and staff safety. To assist leadership in determining the impact of various infection surveillance and response strategies, we developed an agent-based model and an online dashboard interface that simulates COVID-19 infection within congregate care settings under various mitigation measures. In this paper, we demonstrate how this dashboard can be used to quantify the continued risk for COVID-19 infections within a facility given a designated testing schedule and vaccine requirements. Our results highlight the critical nature of testing cadence, test sensitivity and specificity, and the impact of removing asymptomatic infected individuals from the workplace. We also show that monthly surveillance testing at long-term care facilities is unlikely to successfully mitigate SARS-CoV-2 outbreaks in congregate care settings. DisclosuresThis work was supported by Colorado State Universitys Center for Healthy Aging, the Center for Vector-Bourne Infectious Disease, the Office of the Vice President for Research, the College of Health and Human Sciences, the Collage of Natural Sciences, the College of Veterinary Medicine and Biomedical Sciences, and the Walter Scott Jr College of Engineering.
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Full text:
Available
Collection:
Preprints
Database:
medRxiv
Type of study:
Diagnostic study
/
Experimental_studies
/
Prognostic study
Language:
English
Year:
2021
Document type:
Preprint