Imperial College London
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ProfessorChristopherChiu

Faculty of Medicine, Department of Infectious Disease

Professor of Infectious Diseases
 
 
 
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Contact

 

+44 (0)20 3313 2301c.chiu Website

 
 
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Location

 

8N.15Commonwealth BuildingHammersmith Campus

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Summary

 

Publications

Citation

BibTex format

@article{Temple:2023:infdis/jiac262,
author = {Temple, DS and Hegarty-Craver, M and Furberg, RD and Preble, EA and Bergstrom, E and Gardener, Z and Dayananda, P and Taylor, L and Lemm, NM and Papargyris, L and McClain, MT and Nicholson, BP and Bowie, A and Miggs, M and Petzold, E and Woods, CW and Chiu, C and Gilchrist, KH},
doi = {infdis/jiac262},
journal = {Journal of Infectious Diseases},
pages = {864--872},
title = {Wearable sensor-based detection of influenza in presymptomatic and asymptomatic individuals},
url = {http://dx.doi.org/10.1093/infdis/jiac262},
volume = {227},
year = {2023}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - BACKGROUND: The COVID-19 pandemic highlighted the need for early detection of viral infections in symptomatic and asymptomatic individuals to allow for timely clinical management and public health interventions. METHODS: Twenty healthy adults were challenged with an influenza A (H3N2) virus and prospectively monitored from 7 days before through 10 days after inoculation, using wearable electrocardiogram and physical activity sensors (Clinical Trial: NCT04204493; https://clinicaltrials.gov/ct2/show/NCT04204993). This framework allowed for responses to be accurately referenced to the infection event. For each participant, we trained a semi-supervised multivariable anomaly detection model on data acquired before inoculation and used it to classify the post-inoculation dataset. RESULTS: Inoculation with this challenge virus was well-tolerated with an infection rate of 85%. With the model classification threshold set so that no alarms were recorded in the 170 healthy days recorded, the algorithm correctly identified 16 of 17 (94%) positive presymptomatic and asymptomatic individuals, on average 58 hours post inoculation and 23 hrs before the symptom onset. CONCLUSION: The data processing and modeling methodology show promise for the early detection of respiratory illness. The detection algorithm is compatible with data collected from smartwatches using optical techniques but needs to be validated in large heterogeneous cohorts in normal living conditions.
AU  - Temple,DS
AU  - Hegarty-Craver,M
AU  - Furberg,RD
AU  - Preble,EA
AU  - Bergstrom,E
AU  - Gardener,Z
AU  - Dayananda,P
AU  - Taylor,L
AU  - Lemm,NM
AU  - Papargyris,L
AU  - McClain,MT
AU  - Nicholson,BP
AU  - Bowie,A
AU  - Miggs,M
AU  - Petzold,E
AU  - Woods,CW
AU  - Chiu,C
AU  - Gilchrist,KH
DO  - infdis/jiac262
EP  - 872
PY  - 2023///
SN  - 0022-1899
SP  - 864
TI  - Wearable sensor-based detection of influenza in presymptomatic and asymptomatic individuals
T2  - Journal of Infectious Diseases
UR  - http://dx.doi.org/10.1093/infdis/jiac262
UR  - https://www.ncbi.nlm.nih.gov/pubmed/35759279
UR  - https://academic.oup.com/jid/advance-article/doi/10.1093/infdis/jiac262/6618641
UR  - http://hdl.handle.net/10044/1/98097
VL  - 227
ER  -
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