Skip to main content

Detecting Covid-19 infections from Omicron using lateral flow devices

Several patients have asked me if  lateral flow devices (LFDs) will detect Omicron infections. These are the rapid tests that people can use to check for Covid-19 infection while they are asymptotic. NHS staff are required to use these tests regularly if they are in patient-facing roles.

Short Answer: Yes. The UK HSA has confirmed this in an initial laboratory evaluation of the LFDs currently used in the UK. The data from the initial samples in the HSA study show a similar sensitivity for the detection of Covid-19 from Omicron to that seen for previous strains of SARS-CoV-2 including Delta, which has been the predominant strain in the UK from May to December 2021. 

All LFDs approved for use within the UK specifically detect the nucleocapsid protein of SARS-CoV-2 using a combination of 2 or more different antibodies, each targeting a distinct epitope. Full details of the study are available on pages 14-16 of the HAS study. Finally, remember that LFDs are not 100% sensitive and won’t detect some infections. Full details of the study are available on pages 14-16 of the HSA report

If you have symptoms of a possible Covid-19 infection, get a PCR test. Irrespective of your test result, continue to practise good infection control measures such as wearing a face mask and avoiding higher risk venues such s very crowded indoor spaces with poor ventilation.


Comments

Popular posts from this blog

What is the difference between primordial prevention and primary prevention?

Primordial prevention and primary prevention are both crucial strategies for promoting health, but they operate at different levels. Primordial prevention aims to address the root causes of health problems and improve the wider determinants of health. It focuses on preventing the emergence of risk factors in the first place by tackling the underlying social, economic, and environmental determinants of health. This involves broad, population-wide interventions such as: Policies that promote healthy food choices: Think about initiatives like taxing sugary drinks to discourage unhealthy consumption, or providing subsidies for fruits and vegetables to make them more accessible. Urban planning that prioritises well-being: This could include creating walkable neighborhoods with safe cycling routes, ensuring access to green spaces for recreation and relaxation, and designing communities that foster social connections. Social programs that address inequality: Initiatives aimed at reducing pov...

What makes a good doctor – and who gets to decide?

What Makes a Good Doctor? This is the question that Waseem Jerjes and I explore in the Journal of the Royal Society of Medicine . It is a key question that underpins the architecture of medical education, clinical practice, regulation, and professional identity. It cannot be answered by regulators, educators, or employers in isolation. It must be answered together – by doctors and patients – revisited throughout a career, and adapted as society and the profession change. Without that shared reflection, the danger is not simply disillusionment, but the erosion of the moral foundations of clinical work. As we enter an era when diagnosis will increasingly involve artificial intelligence and when performance metrics reward volume over value, reclaiming this question as a professional one is imperative. The integrity of our institutions – and of the practitioners within them – depends on reimagining excellence in inclusive, relational terms. A good doctor is not a flawless technician or a f...

Relevance Over Recall: Rethinking How AI Uses Clinical Data

Our article in the Journal of the Royal Society of Medicine argues that safe and effective AI in healthcare must incorporate mechanisms that emulate human judgement - down-weighting old, inaccurate or superseded information and prioritising what is recent, clinically relevant and reaffirmed - so that AI supports, rather than disrupts, high-quality patient care.  Clinicians constantly revise, reinterpret and filter past information so that only what is relevant, accurate and timely shapes present-day management decisions; medical records function as dynamic “working tools” rather than fixed archives. By contrast, many AI systems lack this capacity for selective forgetting and often treat all historical data as equally meaningful.  This can lead to outdated or low-confidence diagnoses being repeatedly resurfaced, persistent labels influencing clinical expectations, and irrelevant, long-resolved events cluttering summaries and decision-support outputs. Such indiscriminate recall...