AI can predict the likelihood of COVID-19 versus the flu based on symptoms

Testing shortages, long waits for results and an overburdened healthcare system have dominated the headlines throughout the COVID-19 pandemic. These problems can be further exacerbated in small communities or rural communities in the United States and around the world. Additionally, respiratory symptoms of COVID-19 such as fever and cough are also associated with influenza, which complicates non-laboratory diagnoses during certain seasons. A new study by researchers at the George Mason University College of Health and Human Services is designed to help identify the symptoms most likely to indicate COVID during flu season. This is the first study to take seasonality into account.

Farrokh Alemi, principal investigator and professor of health administration and policy, and other Mason researchers predict a patient’s likelihood of having COVID-19, influenza or another respiratory illness before testing, according to the season. This can help clinicians triage patients most suspected of having COVID-19.

“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 lab results to diagnose COVID-19,” said Alemi, who observed these challenges at times during the pandemic. “Our algorithm can help healthcare providers triage patient care while they wait for lab tests or prioritize testing in the event of a test shortage. »

The findings suggest that community health care providers should track different signs and symptoms to diagnose COVID depending on the time of year. Outside of flu season, fever is an even stronger predictor of COVID than during flu season. During flu season, a person with a cough is more likely to have the flu than COVID. The study showed that it would be incorrect to assume that anyone with a fever during flu season has COVID. The algorithm was based on different symptoms for patients of different age and sex. The study also showed that clusters of symptoms are more important in diagnosing COVID-19 than symptoms alone.

The algorithms were created by analyzing the symptoms reported by 774 COVID patients in China and 273 COVID patients in the United States. The analysis also included 2,885 cases of influenza and 884 flu-like illnesses in US patients. “Modeling the Probability of COVID-19 Based on Symptom Screening and Prevalence of Influenza and Influenza-Like Illnesses” was published in the Quality management in healthcare April/June 2022 issue. The rest of the research team are also from George Mason University: Professor of Global Health and Health Epidemiology Amira Roess, Affiliate Professor Jee Vang and PhD candidate Elina Guralnik.

“While useful, the algorithms are too complex to expect clinicians to perform these calculations while providing care. The next step is to create a web-based, AI-powered calculator that can be used in the field. This would allow clinicians to arrive at a diagnosis before the visit,” Alemi said. From there, clinicians can make triage decisions on how to care for the patient while awaiting official lab results.

The study does not include any COVID-19 patients without respiratory symptoms, which includes asymptomatic people. Additionally, the study did not distinguish between the first and second week of symptom onset, which can vary.

This research was a prototype of how existing data can be used to find characteristic symptoms of a new disease. The methodology may have relevance beyond this pandemic.

“When there is a new outbreak, data collection takes time. Rapid analysis of existing data can reduce the time required to differentiate the presentation of new diseases from those with overlapping symptoms. The method described in this article is useful for a rapid response to the next pandemic,” says Alémi.

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