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Abstract
Early detection of influenza may improve responses against outbreaks. This study was part of a clinical study assessing the efficacy of a novel influenza vaccine, aiming to discover distinct, highly predictive patterns of pre-symptomatic illness based on changes in advanced physiological parameters using a novel wearable sensor. Participants were frequently monitored 24 h before and for nine days after the influenza challenge. Viral load was measured daily, and self-reported symptoms were collected twice a day. The Random Forest classifier model was used to classify the participants based on changes in the measured parameters. A total of 116 participants with ~3,400,000 data points were included. Changes in parameters were detected at an early stage of the disease, before the development of symptomatic illness. Heart rate, blood pressure, cardiac output, and systemic vascular resistance showed the greatest changes in the third post-exposure day, correlating with viral load. Applying the classifier model identified participants as flu-positive or negative with an accuracy of 0.81 ± 0.05 two days before major symptoms appeared. Cardiac index and diastolic blood pressure were the leading predicting factors when using data from the first and second day. This study suggests that frequent remote monitoring of advanced physiological parameters may provide early pre-symptomatic detection of flu.
Highlights
The model with the highest overall score was selected for the calculation and analysis of feature importance. In this double-blinded, controlled study, 116 participants (67 females) were included in the analysis out of 145 participants recruited to the vaccine study, with a mean age of 40.2 ± 10.5 years. 71 participants
In 11 participants (10 from the MVA group and 1 from the placebo group), virus levels in the routine laboratory tests collected were under the detection threshold, and they were regarded as flu-negative
As early detection of a biological outbreak is a major mission of national health systems worldwide, as recently highlighted in the COVID-19 pandemic, this tool could allow for improved response due to its automatic, continuous, and real-time collection and transmission of objective physiological data
Summary
Influenza causes a wide range of clinical signs and symptoms ranging from non-febrile, mild upper-respiratory-tract infection that resolves within up to two weeks to severe or even fatal complications such as pneumonia and sepsis [1]. Influenza can worsen and complicate asthma, congestive heart failure, pregnancy, morbid obesity, and various other chronic medical conditions [2,3,4,5,6]. Pandemics of influenza can cause catastrophic illness and societal disruption and rank high among natural threats that necessitate ongoing public health and medical preparedness [7]. Several influenza pandemics have appeared since the beginning of the 20th Century [8,9,10,11,12] and recently it was shown that a 24-h delay in identification significantly increases the odds of death [6]
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