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Prediction of viral symptoms using artificial intelligence

Virus transmission from asymptomatic or pre-symptomatic individuals is a key factor contributing to the SARS-CoV-2 pandemic spread. High levels of the SARS-CoV-2 virus have been observed 48–72 hours before symptom onset.
D’Haese et al. describe their strategy using an AI model that can predict, with 82% accuracy (negative predictive value 97%, specificity 83%, sensitivity 79%, precision 34%), the likelihood of developing symptoms consistent with a viral infection three days before symptom onset.
This model uses a conservative framework, warning potentially pre-symptomatic individuals to socially isolate while minimizing warnings to individuals with a low likelihood of developing viral-like symptoms in the next three days.

They asked each participant to

1) wear a smart ring device with sensors that collect physiological measures such as body temperature, sleep, activity, heart rate, respiratory rate, heart rate variability;

2) use a custom mobile health app to complete a brief symptoms diary, social exposure to potentially infected contacts, and measures of physical, emotional, and cognitive workload; as well as the psychomotor vigilance cognitive task (PVT) to measure attention and fatigue twice a day.

All data are collected, structured, and organized into the RNI Cloud data lake for analysis. Conversely, the model precision is 34%. That precision is defined as the ratio of true positives (TP) over positives (P). In other words, if the model flags someone to develop viral-like symptoms in the next three days, the model is correct 34% of the time. Finally, the very little difference in AUCs between each fold suggests that the model is consistently generalizable. This framework can be applied as a digital decision-making management tool for public health safety in addition to conventional infection-control strategies.

References:
D’Haese P-F, Finomore V, Lesnik D, Kornhauser L, Schaefer T, Konrad PE, et al. (2021) Prediction of viral symptoms using wearable technology and artificial intelligence: A pilot study in healthcare workers. PLoS ONE 16(10): e0257997. https://doi.org/10.1371/journal.pone.0257997




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