SARS-CoV-2 antigen lateral flow tests for detecting infectious people: linked data analysis

Area of Science:

• Infectious Diseases
• Public Health
• Diagnostic Accuracy

Background:

• Lateral flow tests (LFTs) are widely used for SARS-CoV-2 detection.
• Assessing the real-world performance of LFTs, especially in infectious individuals, is crucial for effective public health strategies.
• Mathematical models are frequently employed to predict disease spread and testing efficacy.

Purpose of the Study:

• To determine the proportion of infectious SARS-CoV-2 cases missed by LFTs.
• To evaluate the impact of disease stage and severity on LFT accuracy.
• To compare empirical LFT findings with predictions from mathematical models.

Main Methods:

• Linked data analysis combining Innova LFT accuracy with viral culture and transmission data.
• Utilized data from symptomatic individuals at NHS Test-and-Trace centres, asymptomatic individuals at municipal mass testing, and asymptomatic students.
• Compared empirical results with predictions from two influential mathematical models.

Main Results:

• Innova LFTs were predicted to miss 20-81% of infectious SARS-CoV-2 cases across different settings.
• Higher missed proportions were observed in asymptomatic individuals and for identifying sources of secondary cases.
• Mathematical models significantly underestimated the number of infectious individuals missed by LFTs.

Conclusions:

• A substantial proportion of infectious SARS-CoV-2 cases are missed by LFTs, with clinical significance.
• Missed case proportions vary by setting due to differing viral load distributions, likely highest in asymptomatic individuals.
• Empirical data indicate mathematical models often overestimate LFT sensitivity, highlighting a need for robust real-world studies to inform policy.