ABSTRACT
Capabilities in continuous monitoring of key physiological parameters of disease have never been more important than in the context of the global COVID-19 pandemic. Soft, skin-mounted electronics that incorporate high-bandwidth, miniaturized motion sensors represent a powerful class of technology for digital, wireless measurements of mechano-acoustic (MA) signatures of both core vital signs (heart rate, respiratory rate, and temperature) and underexplored biomarkers (coughing count) with high fidelity and immunity to ambient noises. Here, we introduce an effort that integrates such an MA sensor, a cloud data infrastructure and data analytics approaches based on digital filtering and convolutional neural networks for comprehensive monitoring of COVID-19 infections in sick and healthy individuals in a population, both in the hospital and the home. This hardware/software system extracts diverse signatures of health status in an automated fashion from a single device and time series data stream. Unique features are in quantitative measurements of coughing and other vocal events, as indicators of both disease and infectiousness. Systematic imaging studies demonstrate direct correlations between the time and intensity of coughing, speaking and laughing and the total droplet production, as an approximate indicator of the probability for disease spread. The sensors, deployed on COVID-19 patients along with healthy controls in both inpatient and home settings, record coughing frequency and intensity continuously, along with a comprehensive collection of other biometrics, with recording times for individuals of more than a month after disease diagnosis. These pilot studies include 3,111 hours of data spanning 363 days from 37 COVID-19 patients (20 females, 17 males) with 27,651 coughs detected in total along with continuous measurements of heart rate, respiratory rate, physical activity, and skin temperature. Manual labeling of randomly sampled 10,258 vocal events from 11 COVID-19 patients (6 females, 5 males) suggests a sensitivity of 85% and a specificity of 96% in cough detection using automated algorithms. The collective results indicate a decaying trend of coughing frequency and intensity through the course of disease recovery, but with wide variations across patient populations. The methodology also opens opportunities to study patterns in biometrics across individuals and among different demographic groups.
Subject(s)
COVID-19 , Communicable DiseasesABSTRACT
Purpose: Because of the rapid increase in confirmed cases of COVID-19, in particular those with severe or critical status, overwhelming of health systems is a worldwide concern. Therefore, identifying high-risk COVID-19 patients, can help service providers for priority setting and hospital resource allocation. Methods: 4542 adult patients with confirmed COVID-19 admitted in 15 hospitals in Tehran, Iran, from Feb 20 to April 18, 2020 were included in this retrospective cohort study with final outcomes of survived and died patients. Demographic features including age and sex, and laboratory data measured at admission were extracted and compared between recovered and died patients. Data analysis was performed applying SPSS modeler software using a logistic regression method.Results: Of 4542 hospitalized adult patients, 822 patients (18.09%) died during hospitalization, and 3720 (81.90%) recovered and discharged. Based on logistic regression model, older age, 40-49 (RR= 1.80, CI: 1.13-2.87), 50-59 (RR=2.63, CI: 1.71-4.02), 60-69 (RR= 4.40, CI: 2.92-6.63), 70-79 (RR=7.49, CI: 5.01-11.19), Above 80 (RR=13.85, CI: 9.23-2.77), ALT ≥ 55 IU/ (RR=2.20, CI: 1.69-2.86), AST ≥ 100 IU/L (RR=5.93, CI: 4.75-7.39), ALP ≥ 200 IU/L (RR=2.46, CI: 1.80-3.37), sodium < 135 mEq/l (RR=1.69, CI: 1.35-2.11) or more than 145 mEq/l (RR=7.24, CI: 5.07-10.33), potassium > 5.50 mEq/l (RR=7.53, CI: 4.15-13.64), and calcium < 8.50 mEq/l (RR=3.39, CI: 2.81-4.09), CPK between 307-600 IU/L (RR=2.73, CI: 2.12-3.53) and above 600 IU/L (RR=4.41, CI: 3.40-5.71) in men, and 192-400 IU/L (RR=2.73, CI: 2.12-3.53), and above 400 (RR=4.41, CI: 3.40-5.71) in women, CRP > 3 mg/l (RR=3.22, CI: 1.99-5.20), and creatinine > 1.5 mg/l (RR=6.37, CI: 5.30-7.66) were significantly associated with COVID-19 mortality. Conclusion: Our findings suggested less than one in five hospitalized patients with COVID-19 die mostly due to electrolyte disbalance, liver, and renal dysfunctions. Better supportive care is needed to improve outcomes for patients with COVID-19.
Subject(s)
Atherosclerosis , Kidney Diseases , COVID-19ABSTRACT
Background: Iran is one of the countries that has been overwhelmed with COVID-19. We aimed to estimate the total number of COVID-19 related infections, deaths, and hospitalizations in Iran under different physical distancing and isolation scenarios. Methods: We developed a Susceptible-Exposed-Infected-Removed (SEIR) model, parameterized to the COVID-19 pandemic in Iran. We used the model to quantify the magnitude of the outbreak in Iran and assess the effectiveness of isolation and physical distancing under five different scenarios (A: 0% isolation, through E: 40% isolation of all infected cases). We used Monte-Carlo simulation to calculate the 95% uncertainty intervals (UI). Findings: Under scenario A, we estimated 5,196,000 (UI 1,753,000 - 10,220,000) infections to happen till mid-June with 966,000 (UI 467,800 - 1,702,000) hospitalizations and 111,000 (UI 53,400 - 200,000) deaths. Successful implantation of scenario E would reduce the number of infections by 90% (i.e. 550,000) and change the epidemic peak from 66,000 on June 9th to 9,400 on March 1st. Scenario E also reduces the hospitalizations by 92% (i.e. 74,500), and deaths by 93% (i.e. 7,800). Interpretation: With no approved vaccination or therapy, we found physical distancing and isolation that includes public awareness and case-finding/isolation of 40% of infected people can reduce the burden of COVID-19 in Iran by 90% by mid-June.
Subject(s)
COVID-19ABSTRACT
Background: Our understanding of the corona virus disease 2019 (COVID-19) continues to evolve. However, there are many unknowns about its epidemiology. Purpose: To synthesize the number of deaths from confirmed COVID-19 cases, incubation period, as well as time from onset of COVID-19 symptoms to first medical visit, ICU admission, recovery and death of COVID-19. Data Sources: MEDLINE, Embase, and Google Scholar from December 01, 2019 through to March 11, 2020 without language restrictions as well as bibliographies of relevant articles. Study Selection: Quantitative studies that recruited people living with or died due to COVID-19. Data Extraction: Two independent reviewers extracted the data. Conflicts were resolved through discussion with a senior author. Data Synthesis: Out of 1675 non-duplicate studies identified, 57 were included. Pooled mean incubation period was 5.84 (99% CI: 4.83, 6.85) days. Pooled mean number of days from the onset of COVID-19 symptoms to first clinical visit was 4.82 (95% CI: 3.48, 6.15), ICU admission was 10.48 (95% CI: 9.80, 11.16), recovery was 17.76 (95% CI: 12.64, 22.87), and until death was 15.93 (95% CI: 13.07, 18.79). Pooled probability of COVID-19-related death was 0.02 (95% CI: 0.02, 0.03). Limitations: Studies are observational and findings are mainly based on studies that recruited patient from clinics and hospitals and so may be biased toward more severe cases. Conclusion: We found that the incubation period and lag between the onset of symptoms and diagnosis of COVID-19 is longer than other respiratory viral infections including MERS and SARS; however, the current policy of 14 days of mandatory quarantine for everyone might be too conservative. Longer quarantine periods might be more justified for extreme cases.