Optimizing COVID-19 surveillance in long-term care facilities: a modelling study.

Area of Science:

• Epidemiology
• Infectious Disease Modeling
• Public Health Surveillance

Background:

• Long-term care facilities (LTCFs) are highly susceptible to outbreaks of coronavirus disease 2019 (COVID-19).
• Effective outbreak response in LTCFs is hindered by asymptomatic infections and limited testing capacity.
• Timely epidemiological surveillance is crucial for managing COVID-19 transmission in these settings.

Purpose of the Study:

• To evaluate the efficacy and resource-efficiency of various COVID-19 surveillance strategies in LTCFs.
• To compare group testing and testing cascades against standard surveillance methods.
• To identify optimal strategies for early outbreak detection in LTCFs.

Main Methods:

• A stochastic, individual-based model simulating SARS-CoV-2 transmission in a real LTCF.
• Modeling of nasopharyngeal swab and RT-PCR test distribution based on clinical and demographic factors.
• Evaluation of surveillance strategies including group testing (sample pooling) and testing cascades.

Main Results:

• Symptom-based screening alone is insufficient due to silent and pre-symptomatic transmission.
• Testing upon admission identified only 34-66% of infected patients and missed staff introductions.
• At low testing capacity, group testing detected outbreaks earliest; at high capacity, cascades were most effective.
• Pooling symptomatic individuals was the most efficient strategy under resource limitations.

Conclusions:

• Group testing is the most effective and efficient COVID-19 surveillance strategy for resource-limited LTCFs.
• Testing cascades offer superior effectiveness when testing resources are abundant.
• Enhanced testing capacity and updated surveillance protocols are essential for early outbreak detection and intervention in LTCFs.