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1.
EuropePMC; 2021.
Preprint in English | EuropePMC | ID: ppcovidwho-320071

ABSTRACT

Background: We investigated machine learning based identification of the pre-symptomatic coronavirus disease 2019 (COVID-19) and detection of infection-related changes in physiology using a wearable device (the Ava bracelet).Methods: Participants from an ongoing cohort study (GAPP) of the general population in Liechtenstein were included in the current sub-study (COVI-GAPP). Nightly they wore the fertility bracelet that measured every ten seconds skin temperature, heart rate, respiratory rate, skin perfusion, and heart rate variability. Participants reported daily symptoms in a complementary app. Laboratory reverse transcription polymerase chain reaction (RT-PCR) and/or COVID-19 serology samples were collected from all participants. Long short-term memory (LSTM) based recurrent neural networks (RNN) were chosen for the binary classification of an individual as healthy or infected on a given day in a derivation and validation procedure.Findings: A total of 1ž5 million hours of physiological data were recorded from 1163 participants (mean age 44 +/- 5ž5 years). COVID-19 was confirmed in 127 participants. Of these, 66 (52%) had worn their device from baseline to symptom onset and were included in the analysis and RNN. Multi-level modelling revealed significantly different values in pre- versus post-symptomatic respiratory rate, temperature, heart rate, heart rate variability ratio, and skin perfusion. The developed RNN algorithm had a recall of 0ž73 in the training set and 0ž68 in the testing set (overall recall of 0ž71) when detecting COVID-19 up to two days prior to symptom onset.Interpretation: Our proposed RNN algorithm identified 71% of COVID-19 positive participants two days prior to symptom onset. Wearable sensor technology can therefore enable COVID-19’ detection during the pre-symptomatic period.Funding: IMI grant agreement number 101005177, the Princely House of the Principality of Liechtenstein, the government of the Principality of Liechtenstein, and the Hanela Foundation in Switzerland.Declaration of Interest: Lorenz Risch, and Martin Risch are key shareholders of the Dr Risch Medical Laboratory. David Conen has received consulting fees from Roche Diagnostics, outside of the current work. The other authors have no financial or personal conflicts of interest to declare.Ethical Approval: The local ethics committee approved the study protocol, andwritten informed consent was obtained from each participant (BASEC 2020-00786).

2.
Wellcome Open Res ; 5: 139, 2020.
Article in English | MEDLINE | ID: covidwho-1140800

ABSTRACT

Background: The COVID-19 pandemic caused >1 million infections during January-March 2020. There is an urgent need for reliable antibody detection approaches to support diagnosis, vaccine development, safe release of individuals from quarantine, and population lock-down exit strategies. We set out to evaluate the performance of ELISA and lateral flow immunoassay (LFIA) devices. Methods: We tested plasma for COVID (severe acute respiratory syndrome coronavirus 2; SARS-CoV-2) IgM and IgG antibodies by ELISA and using nine different LFIA devices. We used a panel of plasma samples from individuals who have had confirmed COVID infection based on a PCR result (n=40), and pre-pandemic negative control samples banked in the UK prior to December-2019 (n=142). Results: ELISA detected IgM or IgG in 34/40 individuals with a confirmed history of COVID infection (sensitivity 85%, 95%CI 70-94%), vs. 0/50 pre-pandemic controls (specificity 100% [95%CI 93-100%]). IgG levels were detected in 31/31 COVID-positive individuals tested ≥10 days after symptom onset (sensitivity 100%, 95%CI 89-100%). IgG titres rose during the 3 weeks post symptom onset and began to fall by 8 weeks, but remained above the detection threshold. Point estimates for the sensitivity of LFIA devices ranged from 55-70% versus RT-PCR and 65-85% versus ELISA, with specificity 95-100% and 93-100% respectively. Within the limits of the study size, the performance of most LFIA devices was similar. Conclusions: Currently available commercial LFIA devices do not perform sufficiently well for individual patient applications. However, ELISA can be calibrated to be specific for detecting and quantifying SARS-CoV-2 IgM and IgG and is highly sensitive for IgG from 10 days following first symptoms.

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