New Approach to Predict the Probability of COVID-19, Despite Other Flu Seasons

Throughout the COVID-19 outbreak, testing difficulties, long delays for results and an overburdened healthcare system have garnered headlines. These problems can be compounded in small or rural towns in the United States and around the world. COVID-19 respiratory symptoms, such as fever and cough, are also linked to the flu, making non-lab diagnoses more difficult during certain seasons.

A recent study conducted by experts from the College of Health and Human Services aims to understand which symptoms are more likely to signal COVID during flu season. This is the first research to factor in seasonality.

Based on the season, Farrokh Alemi, principal investigator and professor of Health Administration and Policy, and other Mason researchers can predict whether a patient has COVID-19, flu, or some other respiratory ailment before testing. This can assist doctors in prioritizing patients who are most likely to have COVID-19.

Alemi, as he was witnessing these difficulties at points during the pandemic, stated, “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 prioritize testing if there are testing shortages.”

The results imply that based on the period of the year, community-based health care professionals should look for distinct signs and symptoms when identifying COVID. Fever is an even better predictor of COVID outside of flu season than it is during flu season. During flu season, a coughing individual is more likely to get the flu than someone who has COVID.

According to the findings, presuming that anybody with a fever during flu season has COVID is inaccurate. The algorithm was based on several symptoms for individuals of various ages and genders. The study also found that symptom clusters are more significant than symptoms alone in diagnosing COVID-19.

The algorithms were developed using data from 774 COVID patients in China and 273 COVID patients in the United States. In addition, 2,885 influenza and influenza-like infections in the US patients were included in the study.

The article “Modeling the Probability of COVID-19 Based on Symptom Screening and Prevalence of Influenza and Influenza-Like Illnesses” appeared in the April/June 2022 issue of Quality Management in Health Care.

Professor of Global Health and Epidemiology Health Amira Roess, Affiliate Faculty Jee Vang, and doctorate candidate Elina Guralnik round up the research group.

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 before the visit.

Farrokh Alemi, Principle Investigator and Professor, Health Administration and Policy, George Mason University

While waiting for official lab reports, physicians might make critical judgments about how to care for the patient.

COVID-19 participants with no respiratory symptoms, including those who are asymptomatic, are excluded from the trial. Furthermore, the research did not distinguish between the first and second weeks of symptom onset, which can differ.

This study was a test of how current information can be used to identify new illness hallmark symptoms. The technique could be useful beyond the pandemic.

When there is a new outbreak, collecting data is time-consuming. Rapid analysis of existing data can reduce the time to differentiate the presentation of new diseases from illnesses with overlapping symptoms. The method in this paper is useful for rapid response to the next pandemic.

Farrokh Alemi, Principle Investigator and Professor, Health Administration and Policy, George Mason University

Journal Reference:

Alemi, F., et al. (2022) Modeling the Probability of COVID-19 Based on Symptom Screening and Prevalence of Influenza and Influenza-Like Illnesses. Quality Management in Health Care. doi.org/10.1097/QMH.0000000000000339.