Is there any correlation between digital currency price fluctuation? Based on the DCC-GARCH and wavelet coherence analysis

The existing studies rarely reveal the reasons for the digital currency price fluctuation from the perspective of internal interaction and contagion. Therefore, to fill this research gap, this paper comprehensively adopts the dynamic conditional correlation (DCC-) GARCH model and wavelet coherence analysis (WTC) to reveal the internal correlation and formation reasons of digital currency price fluctuations. Our research has the following findings: (1) the price fluctuations of digital currency are highly related. Through the observation of the dynamic conditional correlation coefficient graph, it is found that the price fluctuations have a strong time-varying trend, manifested as a 'contagious' characteristic. (2) During the outbreak of COVID-19, most digital currencies have shown positive resonance in the short, medium, and long term, suggesting that the COVID-19 pandemic has increased the correlation and contagion of digital currency price fluctuations. (3) In the short term, Bitcoin is the main 'contagious source' of digital currency price fluctuation. But in the medium and long term, Ethereum and Ripple, which are closely related to the real economy, have a greater impact and become the new 'contagious source'. Generally speaking, Bitcoin, Ethereum, and Ripple are the internal causes of instability in the digital currency market. Finally, based on the empirical conclusion, this paper proposes that the digital currency portfolio should be optimized to meet the investment demand;strengthen digital currency regulatory cooperation, and improve regulatory efficiency. Let the digital currency return to the 'currency' attribute and serve the real economy.

Discussion on Influencing Factors and Control Range of Disinfection Effect in Waterworks during the COVID-19 Outbreak

This paper studied the influencing factors of disinfection effect in water purification process and the influence of external demand on the water purification process to ensure that the effective virus inactivation rate of waterworks can meet the requirements of microbiological safety during the COVID-19 outbreak. The results showed that the effluent turbidity should be no more than 0. 3 NTU to meet the requirements of coagulation sedimentation filtration process for virus 2-lg removal rate under the condition of the fixed source water temperature and pH value. On the basis of the above,with the monitoring of the effluent turbidity,water level of clean water tank,water quantity of waterworks and residual chlorine by real-time online instruments,the CT value of the clean water tank was controlled and adjusted within an appropriate range in real time,so that it not only met the 4-lg virus inactivation rate but also reduced the risk of disinfection by-products. Finally,a virus reduction rate of above 6-lg was achieved with the treatment process of waterworks,which could meet the biological safety requirements of drinking water during the epidemic,and have a sufficient safety margin.

Machine Learning-based CT Radiomics Model Distinguishes COVID-19 From Non-COVID-19 Pneumonia (preprint)

Background: To develop a machine learning-based CT radiomics model is critical for the accurate diagnosis of the rapid spread Coronavirus disease 2019 (COVID-19).Methods: In this retrospective study, a total of 326 chest CT exams from 134 patients (63 confirmed COVID-19 patients and 71 non-COVID-19 patients) were collected from January 20 to February 8, 2020. A semi-automatic segmentation procedure was used to delineate the region of interest (ROI), and the radiomic features were extracted. The Support Vector Machine(SVM) model was built on the combination of the 4 groups of features, including radiomic features, traditional radiological features, quantifying features and clinical features, by repeated cross-validation procedure and the performance on the time-independent testing cohort was evaluated by the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity and specificity. Results: For the SVM model that built on the combination of 4 groups of features(integrated model), the per-exam AUC of 0.925(95% CI: 0.856 to 0.994) was reached for differentiating COVID-19 on the testing cohort, and the sensitivity and specificity were 0.816(95% CI: 0.651 to 0.917) and 0.923(95% CI: 0.621 to 0.996), respectively. For the SVM models that built on radiomic features, radiological features, quantifying features and clinical features individually, the AUC on the testing cohort reached 0.765, 0.818, 0.607 and 0.739 respectively, significantly lower than the integrated model, except for the radiomic model.Conclusion: The machine learning-based CT radiomics models may accurately detect COVID-19, helping clinicians and radiologists to identify COVID-19 positive cases.

Clinical and Radiological Features of Patients with Coronavirus Disease 2019 (preprint)

Background: Confirmed cases of coronavirus disease 2019 (COVID-19) is still increasing, detailed analysis of confirmed cases may be beneficial for disease control.Methods: To describe the clinical and radiological findings of patients confirmed with COVID-19 infection in Haikou, China.Results: A total of 67 patients confirmed with COVID-19 infection were included in this study. 50 were imported cases. Most infected patients presented with fever and cough. The typical CT findings of lung lesions were bilateral, multifocal lung lesions (52[78%]), with subpleural distribution, and more than two lobes involved (51[78%]). 54 (81%) patients of COVID-19 pneumonia had ground glass opacities. Consolidation was in 30 (45%) patients, crazy paving pattern or interlobular thickening in 17 (25%), adjacent pleura thickening in 23 (34%) patients. Additionally, baseline chest CT did not reveal positive CT findings in 7 patients (23%), but 3 patients presented unilateral ground glass opacities at follow-up. Importantly, the follow-up CT findings were fitted well with the clinical outcomes.Conclusions: Chest CT could be used as an important tool for early diagnosis of COVID-19, monitoring the disease evolution, judging the treatment effectiveness and predicting the clinical outcomes.

Machine learning-based CT radiomics model Distinguishes COVID-19 from other viral pneumonia (preprint)

Purpose To develop a machine learning-based CT radiomics model is critical for the accurate diagnosis of the rapid spread Coronavirus disease 2019 (COVID-19).Methods In this retrospective study, a machine learning-based CT radiomics model was developed to extract features from chest CT exams for the detection of COVID-19. Other viral-pneumonia CT exams of the corresponding period were also included. The radiomics features extracted from the region of interest (ROI), the radiological features evaluated by the radiologists, the quantity features calculated by the AI segmentation and evaluation, and the clinical parameters including clinical symptoms, epidemiology history and biochemical results were enrolled in this study. The SVM model was built and the performance on the testing cohort was evaluated by the area under the receiver operating characteristic curve (AUC), sensitivity and specificity. Results For the SVM model that built on the radiomics features only, it reached an AUC of 0.688(95% CI 0.496 to 0.881) on the testing cohort. After the radiological features were enrolled, the AUC achieved 0.696(95% CI 0.501 to 0.892), then the AUC reached 0.753(95% CI 0.596 to 0.910) after the quantity features were included. Our final model employed all the features, reached the per-exam sensitivity and specificity for differentiating COVID-19 was 29 of 38 (0.763, 95% CI: 0.598 to 0.886]) and 12 of 13 (0.923, 95% CI: 0.640 to 0.998]), respectively, with an AUC of 0.968(95% CI 0.911 to 1.000). Conclusion The machine learning-based CT radiomics models may accurately detect COVID-19 and differentiate it from other viral pneumonia.

Clinical Characteristics of SARS-CoV-2 Infection in Haikou: a study of a family cluster (preprint)

Background The outbreak of sever acute respiratory syndrome coronavirus 2(SARS-CoV-2) has become a great threat to the world. No study has been done on the mild or asymptomatic SARS-CoV-2 in a family cluster.Methods We report the epidemiological, clinical, laboratory, radiological, and clinical outcomes of five patients in a family cluster.Results We enrolled a family of five patients who was confirmed with SARS-CoV-2 infection. One of them worked in Wuhan and returned to Danzhou, Hainan on January 22,2020. The other four family members, who did not travel to Wuhan, became infected with the virus after several days of contact with the family member. Five family members (aged 33–57 years) presented with fever, cough or no symptom onset. Three of them had negative nucleic test on first swab sampling. One of them was not confirmed until the third nucleic acid test. Two of them had radiological ground-glass lung opacities. Two patients presenting with fever had lymphopenia or decreased white blood cells. No one had increased C-reactive protein or lactate dehydrogenase levels. After treatment, they were discharged.Conclusions Person-to-person transmission of SARS-CoV-2 was confirmed in family setting. Concerns should be raised for the asymptomatic persons in a family cluster.

Clinical Characteristics of SARS-CoV-2 Infection in Haikou: a study of a family cluster (preprint)

Background: The outbreak of sever acute respiratory syndrome coronavirus 2(SARS-CoV-2) has become a great threat to the world. No study has been done on the mild or asymptomatic SARS-CoV-2 in a family cluster.Methods: We report the epidemiological, clinical, laboratory, radiological, and clinical outcomes of five patients in a family cluster.Results: We enrolled a family of five patients who was confirmed with SARS-CoV-2 infection. One of them worked in Wuhan and returned to Danzhou, Hainan on January 22,2020. The other four family members, who did not travel to Wuhan, became infected with the virus after several days of contact with the family member. Five family members (aged 33–57years) presented with fever, cough or no symptom onset. Three of them had negative nucleic test on first swab sampling. One of them was not confirmed until the third nucleic acid test. Two of them had radiological ground-glass lung opacities. Two patients presenting with fever had lymphopenia or decreased white blood cells. No one had increased C-reactive protein or lactate dehydrogenase levels. After treatment, they were discharged.Conclusions: Person-to-person transmission of SARS-CoV-2 was confirmed in family setting. Concerns should be raised for the asymptomatic persons in a family cluster.

Clinical and radiological features of patients with 2019 novel coronavirus (SARS-CoV-2) in Haikou, China (preprint)

To describe the clinical and radiological findings of patients confirmed with 2019 novel coronavirus disease (COVID-19) infection in Haikou, China. A total of 67 patients confirmed with COVID-19 infection were included in this study. 50 were imported cases. Most infected patients presented with fever and cough. The typical CT findings of lung lesions were bilateral, multifocal lung lesions (52[78%]), with subpleural distribution, and more than two lobes involved (51[78%]). 54 (81%) patients of COVID-19 pneumonia had ground glass opacities. Consolidation was in 30 (45%) patients, crazy paving pattern or interlobular thickening in 17 (25%), adjacent pleura thickening in 23 (34%) patients. Additionally, baseline chest CT did not reveal positive CT findings in 7 patients (23%), but 3 patients presented unilateral ground glass opacities at follow-up. Importantly, the follow-up CT findings were fitted well with the clinical outcomes.

Early Chest CT Features of Patients with 2019 Novel Coronavirus (COVID-19) Pneumonia: Relationship to Diagnosis and Prognosis (preprint)

Objective: To elucidate the consistency between CT findings and real time reverse-transcription–polymerase chain- reaction (RT-PCR) results and investigate the relationship between CT features and clinical prognosis in COVID-19.Methods: The clinical manifestations, laboratory parameters and CT imaging findings were analyzed in thirty-four patients with COVID-19 confirmed by RT-PCR from January 20 to February 4 in Hainan province. CT score was compared between the discharged patients and ICU patients.Results: Fever (85%) and cough (79%) were most commonly seen. 10 (29%) patients demonstrated negative results on their first RT-PCR.22/34(65%) patients showed pure ground glass opacity (GGO). 17/34 (50%) patients had five lobes of lung involvement, while the 23(68%) patients had lower lobes were involved and 24/34 (71%) were subpleural. Lesions of 24 (71%) patients were distributed mainly in the subpleural. During follow-up, the initial CT lesions of ICU patients are distributed in both subpleural and parenchyma (80%) and the lesions are scattered. 60% of ICU patients had five lobes involved, while this was seen in only 25% discharged patients. Lesions of discharged patients are mainly in the subpleural (75%). 62.5% of discharged patients showed pure ground-glass opacity. 80% ICU demonstrated progressive stage on their first CT scan. 75 % discharged patients were at an early stage. CT score of ICU patients were significantly higher than that of the discharged patients.Conclusion: Chest CT plays a crucial role in the early diagnosis of COVID-19, particularly for those patients with negative RT-PCR. The initial features in CT may be associated with prognosis.Authors Hui Juan Chen and Jie Qiu contributed equally to this work.