AI can assist establish which signs usually tend to point out COVID throughout flu season

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Testing shortages, lengthy waits for outcomes, and an over-taxed well being care system have made headlines all through the COVID-19 pandemic. These points could be additional exacerbated in small or rural communities within the US and globally. Moreover, respiratory signs of COVID-19 reminiscent of fever and cough are additionally related to the flu, which complicates non-lab diagnoses throughout sure seasons. A brand new examine by Faculty of Well being and Human Companies researchers is designed to assist establish which signs usually tend to point out COVID throughout flu season. That is the primary examine to take seasonality into consideration.

Farrokh Alemi, principal investigator and professor of Well being Administration and Coverage, and different Mason researchers predict the chance {that a} affected person has COVID-19, flu, or one other respiratory sickness previous to testing, relying on the season. This can assist clinicians triage sufferers who’re most suspected of getting COVID-19.

“When entry to dependable COVID testing is proscribed or take a look at outcomes are delayed, clinicians, particularly those that are community-based, usually tend to depend on indicators and signs than on laboratory findings to diagnose COVID-19,” stated Alemi, who noticed these challenges at factors all through the pandemic. “Our algorithm can assist well being care suppliers triage affected person care whereas they’re ready on lab testing or assist prioritize testing if there are testing shortages.”

The findings recommend that community-based well being care suppliers ought to observe completely different indicators and signs for diagnosing COVID relying on the time of 12 months. Exterior of flu season, fever is a fair stronger predictor of COVID than throughout flu season. Throughout flu season, an individual with a cough is extra more likely to have the flu than COVID. The examine confirmed that assuming anybody with a fever throughout flu season has COVID can be incorrect. The algorithm relied on completely different signs for sufferers in numerous age and gender. The examine additionally confirmed that symptom clusters are extra essential in analysis of COVID-19 than signs alone.

The algorithms had been created by analyzing the signs reported by 774 COVID sufferers in China and 273 COVID sufferers in the US. The evaluation additionally included 2,885 influenza and 884 influenza-like sicknesses in US sufferers. “Modeling the Chance of COVID-19 Primarily based on Symptom Screening and Prevalence of Influenza and Influenza-Like Sicknesses” was printed in High quality Administration in Well being Care’s April/June 2022 situation. The remainder of the analysis workforce can also be from Mason: Professor of World Well being and Epidemiology Well being Amira Roess, Affiliate School Jee Vang, and doctoral candidate Elina Guralnik.

Although useful, the algorithms are too complicated to anticipate clinicians to carry out these calculations whereas offering care. The subsequent step is to create an AI, web-based, calculator that can be utilized within the subject. This could permit clinicians to reach at a presumed analysis previous to the go to.”


Farrokh Alemi, Principal Investigator and Professor of Well being Administration and Coverage

From there, clinicians could make triage choices on tips on how to look after the affected person whereas ready for official lab outcomes.

The examine doesn’t embody any COVID-19 sufferers with out respiratory signs, which incorporates asymptomatic individuals. Moreover, the examine didn’t differentiate between the primary and second week of onset of signs, which might range.

This analysis was a prototype of how current knowledge can be utilized to search out signature signs of a brand new illness. The methodology could have relevance past this pandemic.

“When there’s a new outbreak, amassing knowledge is time consuming. Speedy evaluation of current knowledge can scale back the time to distinguish presentation of latest ailments from sicknesses with overlapping signs. The strategy on this paper is helpful for speedy response to the subsequent pandemic,” stated Alemi.

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Alemi, F., et al. (2022) Modeling the Chance of COVID-19 Primarily based on Symptom Screening and Prevalence of Influenza and Influenza-Like Sicknesses. High quality Administration in Well being Care. doi.org/10.1097/QMH.0000000000000339.

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