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1.
EuropePMC; 2021.
Preprint in English | EuropePMC | ID: ppcovidwho-323775

ABSTRACT

Coronavirus disease 2019 (COVID-19) is a highly contagious virus spreading all around the world. Deep learning has been adopted as an effective technique to aid COVID-19 detection and segmentation from computed tomography (CT) images. The major challenge lies in the inadequate public COVID-19 datasets. Recently, transfer learning has become a widely used technique that leverages the knowledge gained while solving one problem and applying it to a different but related problem. However, it remains unclear whether various non-COVID19 lung lesions could contribute to segmenting COVID-19 infection areas and how to better conduct this transfer procedure. This paper provides a way to understand the transferability of non-COVID19 lung lesions. Based on a publicly available COVID-19 CT dataset and three public non-COVID19 datasets, we evaluate four transfer learning methods using 3D U-Net as a standard encoder-decoder method. The results reveal the benefits of transferring knowledge from non-COVID19 lung lesions, and learning from multiple lung lesion datasets can extract more general features, leading to accurate and robust pre-trained models. We further show the capability of the encoder to learn feature representations of lung lesions, which improves segmentation accuracy and facilitates training convergence. In addition, our proposed Hybrid-encoder learning method incorporates transferred lung lesion features from non-COVID19 datasets effectively and achieves significant improvement. These findings promote new insights into transfer learning for COVID-19 CT image segmentation, which can also be further generalized to other medical tasks.

2.
EuropePMC; 2020.
Preprint in English | EuropePMC | ID: ppcovidwho-322246

ABSTRACT

Background: Currently, coronavirus disease 2019 (COVID-19) has spread worldwide and become a global health concern. Here, we report a familial cluster of COVID-19 infection in a northern Chinese region and share our local experience. Methods A familial cluster of six patients infected with severe acute respiratory coronavirus 2 (SARS-CoV-2) was included for analysis. The demographic data, clinical features, laboratory examinations, and epidemiological characteristics of enrolled cases were collected and analyzed. Results Two family members (Cases 1 and 2) had Hubei exposure history and were admitted to the hospital with a confirmed diagnosis of COVID-19;eight familial members who had contact with them during the incubation period were isolated in a hospital. Finally, the condition of four members (Cases 3, 4, 5, and 6) was as follows. Case 3 had negative SARS-CoV-2 RT-PCR results but was suspected to have COVID-19 because of radiographic abnormalities. Cases 4 and 5 developed COVID-19. Due to positive SARS-CoV-2 RT-PCR results, Case 6 was considered an asymptomatic carrier. In addition, four close contacts did not have evidence of SARS-CoV-2 infection. Conclusions Our findings suggest that COVID-19 has infectivity during the incubation period and preventive quarantine is effective for controlling an outbreak of COVID-19 infection.

3.
Discrete Dynamics in Nature and Society ; 2022, 2022.
Article in English | ProQuest Central | ID: covidwho-1627119

ABSTRACT

Stock market is susceptible to various external shocks for its tight dependence on economic fundamentals, financial speculation, and fragile emotions in massive traders, making it a very risky market for investors. In this paper, we aim to identify whether commonly recognized safe-haven assets, that is, bitcoin, gold, and commodities, can provide investors with effective hedging utility in international stock markets, especially during periods of extreme market turbulence. By using the spillover index method based on the TVP-VAR model, we find that firstly, bitcoin, gold, and commodities can only offer weak hedging effects on stock markets. Furthermore, their abilities to act as a safe haven are ranked as: commodities > gold > bitcoin. Secondly, in general, we have observed the increasing hedging ability of these safe-haven assets in times of extreme market turmoil. Thirdly, among international stock and safe-haven asset markets, the world and the developed stock markets act as the net spillover transmitters, while bitcoin, gold, and commodities are the net recipients. Lastly, the total spillover effects are time-varying and increase significantly after the outbreak of extreme events.

4.
PLoS ONE ; 16(2), 2021.
Article in English | CAB Abstracts | ID: covidwho-1410711

ABSTRACT

Background: The Chinese government's early handling of COVID-19 has been perceived as aggressive and oppressive. Many of the most radical measures were adopted in Henan province, immediately north of Hubei, the pandemic's epicentre in China. However, little is known about how rural residents - a group systematically disadvantaged in Chinese society-responded to authorities' draconian restrictions.

5.
SciFinder; 2020.
Preprint | SciFinder | ID: ppcovidwho-3788

ABSTRACT

The medical teams from all over the country were sent to support Wuhan in order to control the pandemic of COVID-19 as soon as possible. The station infection control management experience of the third Sichuan provincial medical team supporting Wuhan including the establishment of the infection control management team, station layout, process standardization of entering and exiting the station, reinforcement of the personnel training and management of cleaning and disinfection was summarized and introduced to provide references for the other medical teams.

6.
Critical Asian Studies ; : 1-18, 2020.
Article in English | Taylor & Francis | ID: covidwho-900242
7.
Cmc-Computers Materials & Continua ; 64(3):1473-1490, 2020.
Article | WHO COVID | ID: covidwho-732585

ABSTRACT

New coronavirus disease (COVID-19) has constituted a global pandemic and has spread to most countries and regions in the world. Through understanding the development trend of confirmed cases in a region, the government can control the pandemic by using the corresponding policies. However, the common traditional mathematical differential equations and population prediction models have limitations for time series population prediction, and even have large estimation errors. To address this issue, we propose an improved method for predicting confirmed cases based on LSTM (Long -Short Term Memory) neural network. This work compares the deviation between the experimental results of the improved LSTM prediction model and the digital prediction models (such as Logistic and Hill equations) with the real data as reference. Furthermore, this work uses the goodness of fitting to evaluate the fitting effect of the improvement. Experiments show that the proposed approach has a smaller prediction deviation and a better fitting effect. Compared with the previous forecasting methods, the contributions of our proposed improvement methods are mainly in the following aspects: 1) we have fully considered the spatiotemporal characteristics of the data, rather than single standardized data. 2) the improved parameter settings and evaluation indicators are more accurate for fitting and forecasting. 3) we consider the impact of the epidemic stage and conduct reasonable data processing for different stage.

8.
Preprint in English | medRxiv | ID: ppmedrxiv-20021584

ABSTRACT

BackgroundSevere ill patients with 2019 novel coronavirus (2019-nCoV) infection progressed rapidly to acute respiratory failure. We aimed to select the most useful prognostic factor for severe illness incidence. MethodsThe study prospectively included 61 patients with 2019-nCoV infection treated at Beijing Ditan Hospital from January 13, 2020 to January 31, 2020. Prognostic factor of severe illness was selected by the LASSO COX regression analyses, to predict the severe illness probability of 2019-CoV pneumonia. The predictive accuracy was evaluated by concordance index, calibration curve, decision curve and clinical impact curve. ResultsThe neutrophil-to-lymphocyte ratio (NLR) was identified as the independent risk factor for severe illness in patients with 2019-nCoV infection. The NLR had a c-index of 0.807 (95% confidence interval, 0.676-0.38), the calibration curves fitted well, and the decision curve and clinical impact curve showed that the NLR had superior standardized net benefit. In addition, the incidence of severe illness was 9.1% in age [≥] 50 and NLR < 3.13 patients, and half of patients with age [≥] 50 and NLR [≥] 3.13 would develop severe illness. Based on the risk stratification of NLR with age, the study developed a 2019-nCoV pneumonia management process. ConclusionsThe NLR was the early identification of risk factors for 2019-nCoV severe illness. Patients with age [≥] 50 and NLR [≥] 3.13 facilitated severe illness, and they should rapidly access to intensive care unit if necessary.

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