Real-time alerting system for COVID-19 and other stress events using wearable data.

Early detection of infectious diseases is crucial for reducing transmission and facilitating early intervention. In this study, we built a real-time smartwatch-based alerting system that detects aberrant physiological and activity signals (heart rates and steps) associated with the onset of early infection and implemented this system in a prospective study. In a cohort of 3,318 participants, of whom 84 were infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), this system generated alerts for pre-symptomatic and asymptomatic SARS-CoV-2 infection in 67 (80%) of the infected individuals. Pre-symptomatic signals were observed at a median of 3 days before symptom onset. Examination of detailed survey responses provided by the participants revealed that other respiratory infections as well as events not associated with infection, such as stress, alcohol consumption and travel, could also trigger alerts, albeit at a much lower mean frequency (1.15 alert days per person compared to 3.42 alert days per person for coronavirus disease 2019 cases). Thus, analysis of smartwatch signals by an online detection algorithm provides advance warning of SARS-CoV-2 infection in a high percentage of cases. This study shows that a real-time alerting system can be used for early detection of infection and other stressors and employed on an open-source platform that is scalable to millions of users.

Detection-based prioritisation: Framework of multi-laboratory characteristics for asymptomatic COVID-19 carriers based on integrated Entropy-TOPSIS methods.

CONTEXT AND BACKGROUND: Corona virus (COVID) has rapidly gained a foothold and caused a global pandemic. Particularists try their best to tackle this global crisis. New challenges outlined from various medical perspectives may require a novel design solution. Asymptomatic COVID-19 carriers show different health conditions and no symptoms; hence, a differentiation process is required to avert the risk of chronic virus carriers. OBJECTIVES: Laboratory criteria and patient dataset are compulsory in constructing a new framework. Prioritisation is a popular topic and a complex issue for patients with COVID-19, especially for asymptomatic carriers due to multi-laboratory criteria, criterion importance and trade-off amongst these criteria. This study presents new integrated decision-making framework that handles the prioritisation of patients with COVID-19 and can detect the health conditions of asymptomatic carriers. METHODS: The methodology includes four phases. Firstly, eight important laboratory criteria are chosen using two feature selection approaches. Real and simulation datasets from various medical perspectives are integrated to produce a new dataset involving 56 patients with different health conditions and can be used to check asymptomatic cases that can be detected within the prioritisation configuration. The first phase aims to develop a new decision matrix depending on the intersection between 'multi-laboratory criteria' and 'COVID-19 patient list'. In the second phase, entropy is utilised to set the objective weight, and TOPSIS is adapted to prioritise patients in the third phase. Finally, objective validation is performed. RESULTS: The patients are prioritised based on the selected criteria in descending order of health situation starting from the worst to the best. The proposed framework can discriminate among mild, serious and critical conditions and put patients in a queue while considering asymptomatic carriers. Validation findings revealed that the patients are classified into four equal groups and showed significant differences in their scores, indicating the validity of ranking. CONCLUSIONS: This study implies and discusses the numerous benefits of the suggested framework in detecting/recognising the health condition of patients prior to discharge, supporting the hospitalisation characteristics, managing patient care and optimising clinical prediction rule.

Alteration of Smell and Taste in Asymptomatic and Symptomatic COVID-19 Patients in Sicily, Italy.

OBJECTIVES: Alteration of smell and taste has been reported in patients with coronavirus disease 2019 (COVID-19). The incidence and clinical-symptomatic manifestation of COVID-19 is different between northern and southern Italy. This study aims to evaluate the onset of alteration of smell and taste in asymptomatic and symptomatic patients in Sicily (extreme south of Italy). METHODS: This prospective cross-sectional study was performed on asymptomatic and symptomatic COVID-19 patients tested for severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) from May 1 to May 15, 2020. A questionnaire was used for evaluating the prevalence of smell and taste disorders in COVID-19 patients before performing nasopharyngeal swab. RESULTS: Of the total 292 patients, 242 (83.2%) were negative for SARS-CoV-2 and 50 were positive (16.8%). Twenty-six of the 50 (52%) SARS-CoV-2 positive patients reported smell/taste disorders. Twenty-eight of the 50 (57.1%) SARS-CoV-2 positive patients were hospitalized (group A), and 22 (42.9%) were nonhospitalized (group B). The mean age in group A and group B was 45.4 ± 13.7 years and 57.0 ± 15.0, respectively (P = .007). The symptoms reported by hospitalized patients were fever (71.4%), cough (64.2%), fatigue (82.1%), and dyspnea (100%), while in nonhospitalized patients, the most reported symptoms were sore throat (72.7%), rhinorrhea (77.2%), and altered smell (81.8%). Anosmia/hyposmia reported in group A and group B was 28.5% and 81.8%, respectively (P = .001). CONCLUSION: These preliminary results indicate that the majority of SARS-Cov-2 positive patients in southern Italy did not require hospitalization and presented with milder symptoms or no symptoms and the alterations in smell and taste occurred.