Methods for detecting probable COVID-19 cases from large-scale survey data also reveal probable sex differences in symptom profiles

Klein, A., Puldon, K., Dilchert, S., Hartogensis, W., Chowdhary, A., Anglo, C., Pandya, L. S., Hecht, F. M., Mason, A. E., & Smarr, B. L.
Frontiers in Big Data, 5.

Daily symptom reporting collected via web-based symptom survey tools holds the potential to improve disease monitoring. Such screening tools might be able to not only discriminate between states of acute illness and non-illness, but also make use of additional demographic information so as to identify how illnesses may differ across groups, such as biological sex. These capabilities may play an important role in the context of future disease outbreaks. We used daily symptom profiles to plot symptom progressions for COVID-19, influenza (flu), and the common cold. We then built a Bayesian network to discriminate between these three illnesses based on daily symptom reports. We identified key symptoms that contributed to a COVID-19 prediction in both males and females using a logistic regression model. Although the Bayesian model performed only moderately well in identifying a COVID-19 diagnosis (71.6% true positive rate), the model showed promise in being able to differentiate between COVID-19, flu, and the common cold, as well as periods of acute illness vs. non-illness. Additionally, COVID-19 symptoms differed between the biological sexes; specifically, fever was amore important symptom in identifying subsequent COVID-19 infection among males than among females.