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Results Phys ; 34: 105224, 2022 Mar.
Article in English | MEDLINE | ID: covidwho-1655111

ABSTRACT

In New York City, the situation of COVID-19 is so serious that it has caused hundreds of thousands of people to be infected due to its strong infectivity. The desired effect of wearing masks by the public is not ideal, though increasingly recommended by the WHO. In order to reveal the potential effect of mask use, we posed a dynamical model with the effective coverage of wearing face masks to assess the impact of mask use on the COVID-19 transmission. We obtained the basic reproduction number R 0 which determined the global dynamics. According to the implement of policies in New York City, we divided the transmission of COVID-19 in three stages. Based on mathematical model and data, we obtain the mean value R 0 = 1 . 822 in the first stage of New York City, while R 0 = 0 . 6483 in the second stage due to that the US Centers for Disease Control and Prevention (CDC) recommended the public wear masks on April 3, 2020, R 0 = 1 . 024 in the third stage after reopening. It was found that if the effective coverage rate of mask use α exceed a certain value α c = 0 . 182 , COVID-19 can be well controlled in the second stage of New York City. Additionally, when the effective coverage of masks reaches a certain level α = 0 . 5 , the benefits are not obvious with the increased coverage rate compared to the cost of medical resources. Moreover, if the effective coverage of mask use in public reaches 20% in the first stage, then the cumulative confirmed cases will be reduced about 50% by 03 April, 2020. Our results demonstrated a new insight on the effect of mask use in controlling the transmission of COVID-19.

2.
Optik (Stuttg) ; 241: 167100, 2021 Sep.
Article in English | MEDLINE | ID: covidwho-1220866

ABSTRACT

Since discovered in Hubei, China in December 2019, Corona Virus Disease 2019 named COVID-19 has lasted more than one year, and the number of new confirmed cases and confirmed deaths is still at a high level. COVID-19 is an infectious disease caused by SARS-CoV-2. Although RT-PCR is considered the gold standard for detection of COVID-19, CT plays an important role in the diagnosis and evaluation of the therapeutic effect of COVID-19. Diagnosis and localization of COVID-19 on CT images using deep learning can provide quantitative auxiliary information for doctors. This article proposes a novel network with multi-receptive field attention module to diagnose COVID-19 on CT images. This attention module includes three parts, a pyramid convolution module (PCM), a multi-receptive field spatial attention block (SAB), and a multi-receptive field channel attention block (CAB). The PCM can improve the diagnostic ability of the network for lesions of different sizes and shapes. The role of SAB and CAB is to focus the features extracted from the network on the lesion area to improve the ability of COVID-19 discrimination and localization. We verify the effectiveness of the proposed method on two datasets. The accuracy rate of 97.12%, specificity of 96.89%, and sensitivity of 97.21% are achieved by the proposed network on DTDB dataset provided by the Beijing Ditan Hospital Capital Medical University. Compared with other state-of-the-art attention modules, the proposed method achieves better result. As for the public COVID-19 SARS-CoV-2 dataset, 95.16% for accuracy, 95.6% for F1-score and 99.01% for AUC are obtained. The proposed network can effectively assist doctors in the diagnosis of COVID-19 CT images.

3.
Clinical eHealth ; 3:7-15, 2020.
Article in English | PMC | ID: covidwho-822402

ABSTRACT

The aim is to diagnose COVID-19 earlier and to improve its treatment by applying medical technology, the “COVID-19 Intelligent Diagnosis and Treatment Assistant Program (nCapp)” based on the Internet of Things. Terminal eight functions can be implemented in real-time online communication with the “cloud” through the page selection key. According to existing data, questionnaires, and check results, the diagnosis is automatically generated as confirmed, suspected, or suspicious of 2019 novel coronavirus (2019-nCoV) infection. It classifies patients into mild, moderate, severe or critical pneumonia. nCapp can also establish an online COVID-19 real-time update database, and it updates the model of diagnosis in real time based on the latest real-world case data to improve diagnostic accuracy. Additionally, nCapp can guide treatment. Front-line physicians, experts, and managers are linked to perform consultation and prevention. nCapp also contributes to the long-term follow-up of patients with COVID-19. The ultimate goal is to enable different levels of COVID-19 diagnosis and treatment among different doctors from different hospitals to upgrade to the national and international through the intelligent assistance of the nCapp system. In this way, we can block disease transmission, avoid physician infection, and epidemic prevention and control as soon as possible.

4.
Expert Rev Respir Med ; 14(12): 1257-1260, 2020 12.
Article in English | MEDLINE | ID: covidwho-671634

ABSTRACT

BACKGROUND: The aim was to compare the laboratory data of patients with suspected and confirmed new coronavirus pneumonia (COVID-19) and look for diagnostic predictive and early warning indicators, which will help to better manage the disease. METHODS: A total of 36 confirmed COVID-19 patients were divided into the general (n = 17) and critical group (n = 19). The suspected group enrolled 23 suspected COVID-19 patients with the negative nucleic acid test result. We collected all patients' clinical characteristics and some laboratory indicators at the time of admission and conducted Logistic regression analysis after comparing the differences between groups. RESULTS: There were no significant differences in age, gender, disease duration, fever history, and comorbidities between the suspected and general group (P > 0.05); however, fibrinogen was statistically different (P < 0.05). Compared with the general group, the oxygenation index and lymphocytes were significantly reduced and the Neutrophil-to-lymphocyte Ratio (NLR) and total bilirubin were increased in the critical group (P < 0.05). The fibrinogen OR value was 2.52 (95% CI 1.18-5.36, P = 0.017) and the NLR OR value was 2.91 (95% CI 1.36-6.21, P = 0.006). CONCLUSIONS: Fibrinogen is a valuable diagnostic predictor for patients with suspected COVID-19. For confirmed COVID-19 patients, the NLR is a valuable early warning indicator.


Subject(s)
COVID-19/diagnosis , Fibrinogen/analysis , Adult , Bilirubin/analysis , Biomarkers/analysis , Case-Control Studies , Critical Illness , Early Diagnosis , Female , Humans , Lymphocyte Count , Male , Middle Aged , Neutrophils/metabolism , Oxygen/blood , Retrospective Studies , SARS-CoV-2
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