Phylogenomic tracing of asymptomatic transmission in a COVID-19 outbreak.

SARS-CoV-2 has caused over 100 million deaths and continues to spread rapidly around the world. Asymptomatic transmission of SARS-CoV-2 is the Achilles' heel of COVID-19 public health control measures. Phylogenomic data on SARS-CoV-2 could provide more direct information about asymptomatic transmission. In this study, using a novel MINERVA sequencing technology, we traced asymptomatic transmission of COVID-19 patients in Beijing, China. One hundred and seventy-eight close contacts were quarantined, and 14 COVID-19 patients were laboratory confirmed by RT-PCR. We provide direct phylogenomic evidence of asymptomatic transmission by constructing the median joining network in the cluster. These data could help us to determine whether the current symptom-based screening should cover asymptomatic persons.

High prevalence of occult thrombosis in cases of mild/moderate COVID-19.

BACKGROUND AND PURPOSE: An increasing number of reports have observed thrombosis in severe cases of COVID-19. The aim of this study was to evaluate the incidence of thromboembolism in mild/moderate cases of COVID-19. All of the patients had normal coagulation tests and none had any overt thrombotic complications. Our findings indicate that it is important to screen the thrombotic status of cases with mild/moderate COVID-19. METHODS: Between 11 June and 8 July 2020, 23 patients with mild/moderate COVID-19 pneumonia consented to having computed tomography pulmonary angiography (CPTA) and computed tomography venography (CTV) scans of the lungs and extremity veins. Doppler ultrasound (DUS) was also performed in all patients for screening. The incidence, clinical manifestations, laboratory examinations, imaging features, and prognosis, of patients with venous thromboembolism (VTE) were analyzed and compared with those of patients with COVID-19 pneumonia without VTE. RESULTS: Nineteen patients (82.6%) had VTE, mainly distal limb thrombosis. Only one of the VTE patients was positive when screened by DUS; the other VTE patients were negative by DUS. All of the mild/moderate patients with VTE were screened by CTPA + CTV. Blood tests for inflammatory, coagulation, and biochemical, parameters were all within the normal range, except for WBC and LDH. CONCLUSIONS: When using CTV screening for DVT, we found that the incidence of thrombosis in patients with mild/moderate COVID-19 markedly increased to 82.6% (19/23). Screening for thrombosis is therefore important in patients with COVID-19. CTV is more sensitive than DUS for the detection of thrombosis. More research is now needed to evaluate the significance of thrombosis in COVID-19 pneumonia.

Chest CT Evaluation of 11 Persistent Asymptomatic Patients with SARS-CoV-2 Infection.

In total, 11 asymptomatic carriers who underwent nasal or oropharyngeal swab tests for SARS-CoV-2 after being in close contact with patients who developed symptomatic 2019 coronavirus disease (COVID-19) were enrolled in this study. The chest multidetector computed tomography (CT) images of the enrolled patients were qualitatively and quantitatively analyzed. The findings of the first chest CT were normal in 3 (27.3%) patients, 2 of whom were aged below 15 years. The lesions of 2 (18.2%) patients involved 1 lobe with unifocal presence. Subpleural lesions were observed in 7 (63.6%) patients. Ground glass opacity (GGO) was the most common sign observed in 7 (63.6%) patients. Crazy-paving pattern and consolidation were detected in 2 (18.2%) and 4 (36.4%) patients, respectively. Based on deep learning and quantitative analysis, the mean volume of intrapulmonary lesions in the first CT image was 85.73 ± 84.46 cm3. In patients with positive findings on CT images, the average interval between positive real-time reverse transcriptase polymerase chain reaction assay and peak volume on CT images was 5.1 ± 3.1 days. In conclusion, typical CT findings can be detected in over 70% of asymptomatic SARS-CoV-2 carriers. The initial presentation is typically GGO along the subpleural regions and bronchi, which absorbs in approximately 5 days.

Optimization of imaging parameters in chest CT for COVID-19 patients: an experimental phantom study.

BACKGROUND: With the global outbreak of coronavirus disease 2019 (COVID-19), chest computed tomography (CT) is vital for diagnosis and follow-up. The increasing contribution of CT to the population-collected dose has become a topic of interest. Radiation dose optimization for chest CT of COVID-19 patients is of importance in clinical practice. The present study aimed to investigate the factors affecting the detection of ground-glass nodules and exudative lesions in chest CT among COVID-19 patients and to find an appropriate combination of imaging parameters that optimize detection while effectively reducing the radiation dose. METHODS: The anthropomorphic thorax phantom, with 9 spherical nodules of different diameters and CT values of -800, -630, and 100 HU, was used to simulate the lesions of COVID-19 patients. Four custom-simulated lesions of porcine fat and ethanol were also scanned at 3 tube potentials (120, 100, and 80 kV) and corresponding milliampere-seconds (mAs) (ranging from 10 to 100). Separate scans were performed at pitches of 0.6, 0.8, 1.0, 1.15, and 1.49, and at collimations of 10, 20, 40, and 80 mm at 80 kV and 100 mAs. CT values and standard deviations of simulated nodules and lesions were measured, and radiation dose quantity (volume CT dose index; CTDIvol) was collected. Contrast-to-noise ratio (CNR) and figure of merit (FOM) were calculated. All images were subjectively evaluated by 2 radiologists to determine whether the nodules were detectable and if the overall image quality met diagnostic requirements. RESULTS: All simulated lesions, except -800 HU nodules, were detected at all scanning conditions. At a fixed voltage of 120 or 100 kV, with increasing mAs, image noise tended to decrease, and the CNR tended to increase (F=9.694 and P=0.033 for 120 kV; F=9.028 and P=0.034 for 100 kV). The FOM trend was the same as that of CNR (F=2.768 and P=0.174 for 120 kV; F=1.915 and P=0.255 for 100 kV). At 80 kV, the CNRs and FOMs had no significant change with increasing mAs (F=4.522 and P=0.114 for CNRs; F=1.212 and P=0.351 for FOMs). For the 4 nodules of -800 and -630 HU, CNRs had no statistical differences at each of the 5 pitches (F=0.673, P=0.476). The CNRs and FOMs at each of the 4 collimations had no statistical differences (F=2.509 and P=0.125 for CNRs; F=1.485 and P=0.309 for FOMs) for each nodule. CNRs and subjective evaluation scores increased with increasing parameter values for each imaging iteration. The CNRs of 4 -800 HU nodules in the qualified images at the thresholds of scanning parameters of 120 kV/20 mAs, 100 kV/40 mAs, and 80 kV/80 mAs, had statistical differences (P=0.038), but the FOMs had no statistical differences (P=0.085). Under the 3 threshold conditions, the CNRs and FOMs of the 4 nodules were highest at 100 kV and 40 mAs (1.6 mGy CTDIvol). CONCLUSIONS: For chest CT among COVID-19 patients, it is recommended that 100 kV/40 mAs is used for average patients; the radiation dose can be reduced to 1.6 mGy with qualified images to detect ground-glass nodules and exudation lesions.

Value of CT findings in predicting transformation of clinical types of COVID-19

Objective: To investigate the value of CT findings in predicting thetransformation of clinical types of COVID-19. Methods: From January 24 to February 6, 2020, the clinical and chest CT data of patients with common COVID-19 were analyzed retrospectively. A total of 64 patients were enrolled, including 32 males and 32 females, aged 18-76 (45±15) years. Based on the fact whether patients’ conditions had deteriorated into severe type, all the cases were divided into common type group (51 cases) and deteriorated type group (13 cases). Differences of CT findings in the two groups of patients were analyzed, and visual semi-quantitative scores were introduced to evaluate the pneumonia. Results: Compared with the common type group, the deteriorated type group was more likely to involve the left upper lobe, the right middle lobe and the lung far away from the pleura. The differences between the two groups were statistically significant (χ²= 5.897, P=0.027;χ²=8.549, P=0.005;χ²=10.169,P=0.002). The median of the involved lobes were 2 (1,5) in the common type group and 5 (4,5) in the deteriorated type group. The difference between the two groups was statistically significant (Z =-3.303, P=0.001). Taking the involved lobes (n=4) as the threshold, the sensitivity and specificity of the diagnosis of the common type to the deteriorated type patients were the highest, 76.9% and 74.5% respectively, and the area under the ROC curve was 0.787. Pneumonia score of the deteriorated group was 10 (4,16), higher than that of the common group [4 (1,13)], and the difference was statistically significant (Z=-4.040, P<0.001). Pneumonia score 8 as the threshold, the sensitivity and specificity of the general severe group were the highest, 69.2% and 86.3% respectively, and the area under ROC curve was 0.863. Conclusions: CT imaging has a profound value in the early prediction of deterioration in clinical type of COVID-19. It can help evaluate the severity of pneumonia in early stage. Range of lesions might be an important indicator for prognosis of common type COVID-19.

Curating a COVID-19 data repository and forecasting county-level death counts in the United States (preprint)

As the COVID-19 outbreak evolves, accurate forecasting continues to play an extremely important role in informing policy decisions. In this paper, we present our continuous curation of a large data repository containing COVID-19 information from a range of sources. We use this data to develop predictions and corresponding prediction intervals for the short-term trajectory of COVID-19 cumulative death counts at the county-level in the United States up to two weeks ahead. Using data from January 22 to June 20, 2020, we develop and combine multiple forecasts using ensembling techniques, resulting in an ensemble we refer to as Combined Linear and Exponential Predictors (CLEP). Our individual predictors include county-specific exponential and linear predictors, a shared exponential predictor that pools data together across counties, an expanded shared exponential predictor that uses data from neighboring counties, and a demographics-based shared exponential predictor. We use prediction errors from the past five days to assess the uncertainty of our death predictions, resulting in generally-applicable prediction intervals, Maximum (absolute) Error Prediction Intervals (MEPI). MEPI achieves a coverage rate of more than 94% when averaged across counties for predicting cumulative recorded death counts two weeks in the future. Our forecasts are currently being used by the non-profit organization, Response4Life, to determine the medical supply need for individual hospitals and have directly contributed to the distribution of medical supplies across the country. We hope that our forecasts and data repository at https://covidseverity.com can help guide necessary county-specific decision-making and help counties prepare for their continued fight against COVID-19.

Imaging Features of Coronavirus disease 2019 (COVID-19): Evaluation on Thin-Section CT.

RATIONALE AND OBJECTIVES: To retrospectively analyze the chest imaging findings in patients with coronavirus disease 2019 (COVID-19) on thin-section CT. MATERIALS AND METHODS: Fifty-three patients with confirmed COVID-19 infection underwent thin-section CT examination. Two chest radiologists independently evaluated the imaging in terms of distribution, ground-glass opacity (GGO), consolidation, air bronchogram, stripe, enlarged mediastinal lymph node, and pleural effusion. RESULTS: Fourty-seven cases (88.7%) had findings of COVID-19 infection, and the other six (11.3%) were normal. Among the 47 cases, 78.7% involved both lungs, and 93.6% had peripheral infiltrates distributed along the subpleural area. All cases showed GGO, 59.6% of which were round and 40.4% patchy. Other imaging features included "crazy-paving pattern" (89.4%), consolidation (63.8%), and air bronchogram (76.6%). Air bronchograms were observed within GGO (61.7%) and consolidation (70.3%). Neither enlarged mediastinal lymph nodes nor pleural effusion were present. Thirty-three patients (62.3%) were followed an average interval of 6.2 ± 2.9 days. The lesions increased in 75.8% and resorbed in 24.2% of patients. CONCLUSION: COVID-19 showed the pulmonary lesions in patients infected with COVID-19 were predominantly distributed peripherally in the subpleural area.