A Caveat to Using Wearable Sensor Data for COVID-19 Detection: The Role of Behavioral Change after Receipt of Test Results (preprint)

Recent studies indicate that wearable sensors have the potential to capture subtle within-person changes that signal SARS-CoV-2 infection. However, it remains unclear the extent to which observed discriminative performance is attributable to behavioral change after receiving test results. We conducted a retrospective study in a sample of medical interns who received COVID-19 test results from March to December 2020. Our data confirmed that sensor data were able to differentiate between symptomatic COVID-19 positive and negative individuals with good accuracy (area under the curve (AUC) = 0.75). However, removing post-result data substantially reduced discriminative capacity (0.75 to 0.63; delta= −0.12, p=0.013). Removing data in the symptomatic period prior to receipt of test results did not produce similar reductions in discriminative capacity. These findings suggest a meaningful proportion of the discriminative capacity of wearable sensor data for SARS-CoV-2 infection may be due to behavior change after receiving test results.

Characterization of clinical progression of COVID-19 patients in Shenzhen, China (preprint)

Background: Understanding clinical progression of COVID-19 is a key public health priority that informs resource allocation during an emergency. We characterized clinical progression of COVID-19 and determined important predictors for faster clinical progression to key clinical events and longer use of medical resources. Methods and Findings: The study is a single-center, observational study with prospectively collected data from all 420 patients diagnosed with COVID-19 and hospitalized in Shenzhen between January 11th and March 10th, 2020 regardless of clinical severity. Using competing risk regressions according to the methods of Fine and Gray, we found that males had faster clinical progression than females in the older age group and the difference could not be explained by difference in baseline conditions or smoking history. We estimated the proportion of cases in each severity stage over 80 days following symptom onset using a nonparametric method built upon estimated cumulative incidence of key clinical events. Based on random survival forest models, we stratified cases into risk sets with very different clinical trajectories. Those who progressed to the severe stage (22%,93/420), developed acute respiratory distress syndrome (9%,39/420), and were admitted to the intensive care unit (5%,19/420) progressed on average 9.5 days (95%CI 8.7,10.3), 11.0 days (95%CI 9.7,12.3), and 10.5 days (95%CI 8.2,13.3), respectively, after symptom onset. We estimated that patients who were admitted to ICUs remained there for an average of 34.4 days (95%CI 24.1,43.2). The median length of hospital stay was 21.3 days (95%CI, 20.5,22.2) for cases who did not progress to the severe stage, but increased to 52.1 days (95%CI, 43.3,59.5) for those who required critical care. Conclusions: Our analyses provide insights into clinical progression of cases starting early in the course of infection. Patient characteristics near symptom onset both with and without lab parameters have tremendous potential for predicting clinical progression and informing strategic response.