Observing the silent world under COVID-19 with a comprehensive impact analysis based on human mobility.

Since spring 2020, the human world seems to be exceptionally silent due to mobility reduction caused by the COVID-19 pandemic. To better measure the real-time decline of human mobility and changes in socio-economic activities in a timely manner, we constructed a silent index (SI) based on Google's mobility data. We systematically investigated the relations between SI, new COVID-19 cases, government policy, and the level of economic development. Results showed a drastic impact of the COVID-19 pandemic on increasing SI. The impact of COVID-19 on human mobility varied significantly by country and place. Bi-directional dynamic relationships between SI and the new COVID-19 cases were detected, with a lagging period of one to two weeks. The travel restriction and social policies could immediately affect SI in one week; however, could not effectively sustain in the long run. SI may reflect the disturbing impact of disasters or catastrophic events on the activities related to the global or national economy. Underdeveloped countries are more affected by the COVID-19 pandemic.

A rapid screening classifier for diagnosing COVID-19.

Rationale: Coronavirus disease 2019 (COVID-19) has caused a global pandemic. A classifier combining chest X-ray (CXR) with clinical features may serve as a rapid screening approach. Methods: The study included 512 patients with COVID-19 and 106 with influenza A/B pneumonia. A deep neural network (DNN) was applied, and deep features derived from CXR and clinical findings formed fused features for diagnosis prediction. Results: The clinical features of COVID-19 and influenza showed different patterns. Patients with COVID-19 experienced less fever, more diarrhea, and more salient hypercoagulability. Classifiers constructed using the clinical features or CXR had an area under the receiver operating curve (AUC) of 0.909 and 0.919, respectively. The diagnostic efficacy of the classifier combining the clinical features and CXR was dramatically improved and the AUC was 0.952 with 91.5% sensitivity and 81.2% specificity. Moreover, combined classifier was functional in both severe and non-serve COVID-19, with an AUC of 0.971 with 96.9% sensitivity in non-severe cases, which was on par with the computed tomography (CT)-based classifier, but had relatively inferior efficacy in severe cases compared to CT. In extension, we performed a reader study involving three experienced pulmonary physicians, artificial intelligence (AI) system demonstrated superiority in turn-around time and diagnostic accuracy compared with experienced pulmonary physicians. Conclusions: The classifier constructed using clinical and CXR features is efficient, economical, and radiation safe for distinguishing COVID-19 from influenza A/B pneumonia, serving as an ideal rapid screening tool during the COVID-19 pandemic.

Detection of Viruses by Multiplex Real-Time Polymerase Chain Reaction in Bronchoalveolar Lavage Fluid of Patients with Nonresponding Community-Acquired Pneumonia.

Background: Nonresponding pneumonia is responsible for the most mortality of community-acquired pneumonia (CAP). However, thus far, it is not clear whether viral infection plays an important role in the etiology of nonresponding CAP and whether there is a significant difference in the clinical characteristics between viral and nonviral nonresponding CAP. Methods: From 2016 to 2019, nonresponding CAP patients were retrospectively enrolled in our study. All patients received bronchoalveolar lavage (BAL) and virus detection in BAL fluid by multiplex real-time polymerase chain reaction (PCR), and clinical, laboratory, and radiographic data were collected. Results: A total of 43 patients were included. The median age was 62 years, and 65.1% of patients were male. Overall, 20 patients (46.5%) were identified with viral infection. Of these viruses, influenza virus (n = 8) and adenovirus (n = 7) were more frequently detected, and others included herpes simplex virus, human enterovirus, cytomegalovirus, human coronavirus 229E, rhinovirus, and parainfluenza virus. Compared with nonviral nonresponding CAP, only ground-glass opacity combined with consolidation was a more common imaging manifestation in viral nonresponding CAP. However, no obvious differences were found in clinical and laboratory findings between the presence and the absence of viral infections. Conclusions: Viral infections were particularly frequent in adults with nonresponding CAP. The ground-glass opacity combined with consolidation was a specific imaging manifestation for viral nonresponding CAP, while the clinical and laboratory data showed no obvious differences between viral and nonviral nonresponding CAP.