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Effective community screening for asymptomatic and symptomatic COVID-19 with a fast and extremely low cost COVID-Anosmia Checker tool (preprint)
medrxiv; 2020.
Preprint in English | medRxiv | ID: ppzbmed-10.1101.2020.10.28.20221200
ABSTRACT

Background:

Covid-19 curve can be flattened by adopting mass screening protocols with aggressive testing and isolating infected populations. The current approach largely depends on RT-PCR/rapid antigen tests that require expert personnel resulting in higher costs and reduced testing frequency. Loss of smell is reported as a major symptom of Covid-19, however, a precise olfactory testing tool to identify Covid-19 patient is still lacking.

Methods:

To quantitatively check for the loss of smell, we developed an odor strip, COVID-Anosmia checker, spotted with gradients of coffee and lemon grass oil. We validated its efficiency in healthy and COVID-19 positive subjects. A trial screening to identify SARS-CoV-2 infected persons was also carried out to check the sensitivity and specificity of our screening tool.

Results:

It was observed that COVID positive participants were hyposmic instead of being anosmic when they were subjected to smelling higher odor concentration. Our tool identified 97% of symptomatic and 94% of asymptomatic COVID-19 positive subjects after excluding most confounding factors like concurrent chronic sinusitis. Further, it was possible to reliably predict COVID-19 infection by calculating a loss of smell score with 100% specificity. We coupled this tool with a mobile application, which takes the input response from the user, and can readily categorize the user in the appropriate risk groups.

Conclusion:

Loss of smell can be used as a reliable marker for screening for Covid-19. Our tool can rapidly quantitate anosmia, hyposmia, parosmia, and can be used as a first-line screening tool to trace out Covid-19 infection effectively.

Full text: Available Collection: Preprints Database: medRxiv Language: English Year: 2020 Document Type: Preprint

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Full text: Available Collection: Preprints Database: medRxiv Language: English Year: 2020 Document Type: Preprint