ABSTRACT
Systematic SARS-CoV-2 testing is a valuable tool for infection control and surveillance. However, broad application of high sensitive RT-qPCR testing in children is often hampered due to unpleasant sample collection, limited RT-qPCR capacities and high costs. Here, we developed a high-throughput approach ('Lolli-Method') for SARS-CoV-2 detection in children, combining non-invasive sample collection with an RT-qPCR-pool testing strategy. SARS-CoV-2 infections were diagnosed with sensitivities of 100% and 93.9% when viral loads were >106 copies/ml and >103 copies/ml in corresponding Naso-/Oropharyngeal-swabs, respectively. For effective application of the Lolli-Method in schools and daycare facilities, SEIR-modeling indicated a preferred frequency of two tests per week. The developed test strategy was implemented in 3,700 schools and 698 daycare facilities in Germany, screening over 800,000 individuals twice per week. In a period of 3 months, 6,364 pool-RT-qPCRs tested positive (0.64%), ranging from 0.05% to 2.61% per week. Notably, infections correlated with local SARS-CoV-2 incidences and with a school social deprivation index. Moreover, in comparison with the alpha variant, statistical modeling revealed a 36.8% increase for multiple (≥2 children) infections per class following infections with the delta variant. We conclude that the Lolli-Method is a powerful tool for SARS-CoV-2 surveillance and can support infection control in schools and daycare facilities.
Subject(s)
COVID-19 , COVID-19/diagnosis , COVID-19/epidemiology , COVID-19 Testing , Child , Clinical Laboratory Techniques/methods , Humans , SARS-CoV-2/genetics , Sensitivity and SpecificityABSTRACT
BACKGROUND: Storing K(x)EDTA-conjugated blood samples at room temperature or under insufficient cooling conditions results in various morphological changes such as swelling of the blood cells. These changes are reproducible and have already been described well. However, they can lead to incorrect flagging when using automated hematology analyzers for complete blood counts and white blood cell differentials. The aim of this study was to determine if those changes can be detected automatically and used to prevent false positive flagging. METHODS: 150 blood samples were aged under controlled conditions and the impact on the "Aged sample" software was checked retrospectively. The results were verified in a second retrospective study including 6288 routine samples. RESULTS: When tested in a routine laboratory, the "Aged sample" software was able to reduce overall flagging by 23% without increasing false negative flagging. CONCLUSIONS: The "Aged sample" software of XN-Series analyzers does not only detect and flag samples that are aging or were stored under suboptimal conditions but also prevents false positive flagging.
