A group of experts, from the George Mason University College of Health and Human Services, have created an algorithm to identify which symptoms are more likely to indicate COVID during flu season. They have published their results in a study, which is the first of its kind to take seasonality into account. In it, the researchers predict the probability that a patient has COVID-19, flu, or another respiratory illness prior to testing, depending on the season.
They hope it will help clinicians triage patients who are most suspected of having COVID-19.
Dr Farrokh Alemi, principal investigator and professor of Health Administration and Policy, said: “When access to reliable COVID testing is limited or test results are delayed, clinicians, especially those who are community-based, are more likely to rely on signs and symptoms than on laboratory findings to diagnose COVID-19.
“Our algorithm can help health care providers triage patient care while they are waiting on lab testing or help prioritise testing if there are testing shortages.”
It comes after the Government ended free testing for the public on April 1 as part of the Living with COVID plan.
Many people have been left confused as to whether they have flu or Covid – and therefore unsure where they stand morally on going about their usual business.
While you’re no longer legally required to self-isolate if you have COVID-19, the NHS says you should try to stay at home and away from others to avoid passing on the virus.
The NHS Confederation this week accused ministers of “abandoning any interest in Covid whatsoever” and called for “mitigating actions” to tackle record infection rates.
These measures included not meeting people indoors and wearing masks in crowded spaces.
The new study suggests that community-based health care providers should follow different signs and symptoms for diagnosing COVID depending on the time of year.
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It also included 2,885 influenza and 884 influenza-like illnesses in US patients.
Now, experts want to take it to the next level.
Dr Alemi added: “Though helpful, the algorithms are too complex to expect clinicians to perform these calculations while providing care.
“The next step is to create an AI, web-based calculator that can be used in the field.
“This would allow clinicians to arrive at a presumed diagnosis prior to the visit.”