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This paper proposes an automated classification method of COVID-19 chest CT volumes using improved 3D MLP-Mixer. Novel coronavirus disease 2019 (COVID-19) spreads over the world, causing a large number of infected patients and deaths. Sudden increase in the number of COVID-19 patients causes a manpower shortage in medical institutions. Computer-aided diagnosis (CAD) system provides quick and quantitative diagnosis results. CAD system for COVID-19 enables efficient diagnosis workflow and contributes to reduce such manpower shortage. In image-based diagnosis of viral pneumonia cases including COVID-19, both local and global image features are important because viral pneumonia cause many ground glass opacities and consolidations in large areas in the lung. This paper proposes an automated classification method of chest CT volumes for COVID-19 diagnosis assistance. MLP-Mixer is a recent method of image classification using Vision Transformer-like architecture. It performs classification using both local and global image features. To classify 3D CT volumes, we developed a hybrid classification model that consists of both a 3D convolutional neural network (CNN) and a 3D version of the MLP-Mixer. Classification accuracy of the proposed method was evaluated using a dataset that contains 1205 CT volumes and obtained 79.5% of classification accuracy. The accuracy was higher than that of conventional 3D CNN models consists of 3D CNN layers and simple MLP layers. © 2023 SPIE.
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Coronavirus disease 2019 (COVID-19) induced by the severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) has impacted the lives and wellbeing of many people. This globally widespread disease poses a significant public health concern that urges to discover an effective treatment. This review paper discusses the effectiveness of repurposed drugs used to treat COVID-19 and potential novel therapies for COVID-19. Among the various repurposed drugs, remdesivir is the only agent approved by the Food and Drug Administration (FDA) to treat COVID-19. On the other hand, several drugs have been listed in the Emergency Use Authorization (EUA) by the FDA to treat COVID-19, including casirivimab and imdevimab, baricitinib (in combination with remdesivir), bamlanivimab, tocilizumab, and IL-6 inhibitors. In addition, in vitro and clinical studies have suggested cepharanthine, sotrovimab, and XAV-19 as potential treatments to manage COVID-19. Due to inadequate understanding of COVID- 19 and the rapid mutation of SARS-CoV-2, COVID-19 remains a threat to global public health, with vaccination considered the most effective method to decrease COVID-19 transmission currently. Nevertheless, with the intense efforts of clinical researchers globally, more promising treatments for COVID-19 will be established in the future.
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Background. Our understanding of SARS-CoV-2 evolution is limited. Most estimates arise from analysis of global databases populated with unrelated sequences and is currently estimated at ~27.7 substitutions/genome/year. SARS-CoV-2 polymerase contains a proofreading function encoded by NSP-14 limiting change. Additionally, virus evolution may be influenced by patient comorbidity. Intra-host mutational rate (MR) during infection remain poorly studied. Methods. To minimize effect of vaccination and/or natural immunity on MR analysis, paired samples from adults originating from the initial pandemic wave (3/17/2020 through 5/27/2020) were retrieved and analyzed at Cleveland Clinic. Viral genome analysis was performed using next generation sequencing, and mutations between paired samples were quantified at allele frequencies (AF) >= 0.1, >= 0.5 and >= 0.75 and compared. MR was calculated employing F81 and JC69 evolution models and compared between isolates with (DELTA NSP-14) and without (wildtype, wt) non-synonymous mutations in NSP-14 and by comorbidity. Results. A total of 40 patients (80 sample pairs) were identified. Median interval between paired tests was 15 days [range 5-32]. The estimatedMRby F81 modeling was 317.2 (95%CI 312.0-322.3), 54.6 (95%CI 52.5-56.7) and 45.1 (95%CI 43.1-47.0) substitutions/ genome/year at AF of >=0.1, >=0.5, >=0.75 respectively. Rates in DELTANSP-14 (n=13) vs wt (n=27) groups were 557.7 (95%CI 537.0-578.2) vs 193.1 (95%CI 187.1-199.1) p-value 0.001, 50.8 (95%CI 44.3-57.3) vs 56.3 (95%CI 53.1-59.4) p-value 0.144, and 31.0 (95%CI 25.9-36.1) vs 51.3 (95%CI 48.3-54.3) p- value < 0.001 at AF >=0.1, >=0.5, >=0.75 respectively. Patients with immune comorbidities (n=6) had significantly higher MR of 137.6 (95%CI 114.6-160.5) vs 40.5 (95%CI 38.4-42.7) p-value < 0.001, and 113.7 (95%CI 92.8-134.5) vs 33.4 (95%CI 31.5-35.4) p-value < 0.001 at AF >=0.5 and >=0.75 respectively. Similar results were obtained when using the JC69 model. Conclusion. Intra-host SARS-CoV-2 mutation rates are higher than those reported through population analysis. Virus strains with altered NSP-14 have accelerated MR at low AF. Immunosuppressed patients have elevated MR at higher AF. Understanding intra-host virus evolution will aid in current and future pandemic modeling.
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The unprecedented coronavirus disease 2019 (COVID-19) pandemic is still raging (in year 2021) in many countries worldwide. Various response strategies to study the characteristics and distributions of the virus in various regions of the world have been developed to assist in the prevention and control of this epidemic. Descriptive statistics and regression analysis on COVID-19 data from different countries were conducted in this study to compare and evaluate various regression models. Results showed that the extreme random forest regression (ERFR) model had the best performance, and factors such as population density, ozone, median age, life expectancy, and Human Development Index (HDI) were relatively influential on the spread and diffusion of COVID-19 in the ERFR model. In addition, the epidemic clustering characteristics were analyzed through the spectral clustering algorithm. The visualization results of spectral clustering showed that the geographical distribution of global COVID-19 pandemic spread formation was highly clustered, and its clustering characteristics and influencing factors also exhibited some consistency in distribution. This study aims to deepen the understanding of the international community regarding the global COVID-19 pandemic to develop measures for countries worldwide to mitigate potential large-scale outbreaks and improve the ability to respond to such public health emergencies. © 2018 Tsinghua University Press.
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This paper proposes an automated classification method of chest CT volumes based on likelihood of COVID-19 cases. Novel coronavirus disease 2019 (COVID-19) spreads over the world, causing a large number of infected patients and deaths. Sudden increase in the number of COVID-19 patients causes a manpower shortage in medical institutions. Computer-aided diagnosis (CAD) system provides quick and quantitative diagnosis results. CAD system for COVID-19 enables efficient diagnosis workflow and contributes to reduce such manpower shortage. This paper proposes an automated classification method of chest CT volumes for COVID-19 diagnosis assistance. We propose a COVID-19 classification convolutional neural network (CNN) that has a 2D/3D hybrid feature extraction flows. The 2D/3D hybrid feature extraction flows are designed to effectively extract image features from anisotropic volumes such as chest CT volumes for diagnosis. The flows extract image features on three mutually perpendicular planes in CT volumes and then combine the features to perform classification. Classification accuracy of the proposed method was evaluated using a dataset that contains 1288 CT volumes. An averaged classification accuracy was 83.3%. The accuracy was higher than that of a classification CNN which does not have 2D and 3D hybrid feature extraction flows. © 2022 SPIE.
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Since the end of December 2019, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has posed a serious threat to global public health security. Many coronaviruses, including SARS-CoV-2, are of animal origin. Therefore, monitoring of animal coronavirus must be strengthened. Herein, the common sample types, cell types used for viral isolation and culture, viral molecular detection methods, and immunological detection of animal coronaviruses are reviewed to provide a reference for follow-up studies of animal coronaviruses.
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Many countries have adopted border closures and other jurisdictions (provinces, states, cities, etc.) to control the spread of SARS-CoV-2. Such measures have significantly restricted population movement and have thus led to immense economic and social fallouts. We build a Susceptible-Exposed-Asymptomatic- Infectious (prodromal phase)- Infectious (with symptoms) -Recovered (SEAIR) model with a household structure to investigate the potential of a safe reopening of a border, which can control disease spread but also allow for economic growth. We focus on the Ontario-USA border, considering an opening date of September 21, 2020. In addition to the instantaneous reproduction number, we also define a novel risk indicator by calculating daily new infections’ percentile to inform risk levels promptly. Under ideal conditions, assuming extremely efficient border testing and strict traveler adherence to quarantine policy, the Ontario-USA can be reopened for a maximum daily number of 500 travelers entering Canada. A number exceeding 500 will stem an uncontrollable spread of the virus. Additionally, the current quarantine policy may not be sufficient under specific scenarios;hence testing measures at the border must be extremely efficient. Reopening of the Ontario-USA must consider the potential for disease spread (which can overburden healthcare resources) and economic growth. If a reopening plan is implemented, the local government must limit the number of daily entrances to 500 and enforce a mandatory quarantine, which may need to be stricter than current policy practice. © 2022, Springer Nature Switzerland AG.
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Boarding school is one of the places where people usually live in densely crowed conditions. In order to control the risk of COVID-19 epidemic in boarding schools, five levels of practicable pandemic prevention measures and their effects on infection risks in five typical campus living scenes, including going to washroom, going out, going to class, having meal, and returning to dormitory were proposed, and the susceptible-infective (SI) model based on statistics and probability hypotheses was developed. Then the SARS-CoV-2 infection rates among students in 14 days were simulated in two typical apartment types: four-person dormitory with two public washrooms on each floor (apartment A) and six-person dormitory with a private washroom (apartment B). Results show that for apartment A, once there was an infected person, the epidemic spread rapidly in the whole building even under the most stringent prevention and control measures (level Ⅴ). While for apartment B, when the most stringent prevention and control measures (level Ⅴ) were taken, the epidemic could be controlled within the range of less than 10 people in two weeks. In addition, full vaccination would significantly inhibit the infection rate, and the number of washrooms would no longer be a significant factor. Even if no prevention and control measures were taken, the number of infected people would decrease significantly, and the number of persons in one dormitory became the main factor affecting the spread of the virus. The research results can provide information support for campus epidemic prevention and control. Copyright ©2022 Journal of Harbin Institute of Technology.All rights reserved.
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Objective During the COVID-19 epidemic period, we investigated the cognitive level of COVID-19 knowledge of medical staffs in Anhui Province and analyzed the influencing factors of cognitive level of COVID-19 knowledge. Methods From February 12, 2020 to March 4, 2020, a self-made questionnaire was used to evaluate the knowledge of COVID-19 among medical staff in Anhui Province. A total of 15 342 valid questionnaires were obtained. By SPSS 17.0 statistical software, and descriptive analysis, t-test, ANOVA analysis, and multiple linear regression were used to analyze the cognitive level of COVID-19 knowledge of medical staffs and the influencing factors. Results The total score of COVID-19 knowledge of medical staffs in Anhui Province was (6.95±2.67) points, the average score of diagnosis knowledge was (2.58±1.74) points, the average score of treatment knowledge was (1.53±1.03) points, and the score of nosocomial infections knowledge was (2.84±1.01) points. There were significant differences in COVID-19 diagnosis knowledge, nosocomial infections knowledge and total score between doctors and nurses (all P < 0.05). Multivariate linear regression analysis showed that the scores in senior and intermediate professional title groups were higher than those in primary professional title group;the scores in master′s degree group and above and undergraduate education group were higher than those in junior college education group;the knowledge scores in municipal, county-level hospitals, primary medical institutions and private medical institutions were lower than those in provincial hospital group;the scores in patients aged 30~ years and ≥40 years were lower than those in group < 30 years. The scores in senior and intermediate professional title groups were higher than those in junior professional title group;the scores in municipal, county-level hospitals, primary medical institutions and private medical institutions were lower than those in provincial hospitals;the scores of 30~ years old and ≥40 years old were lower than those of < 30 years old group, and the scores of nurses with bachelor′s degree were higher than junior college degree or below (all P < 0.05). Conclusions The score of COVID-19 knowledge of medical staffs in Anhui Province is low, so we should train them COVID-19 knowledge systematically. We should pay attention to the influencing factors like occupation, title, education background, age and hospital rank when selecting and training anti-epidemic medical staffs. © 2021, Publication Centre of Anhui Medical University. All rights reserved.
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This paper proposes a segmentation method of infection regions in the lung from CT volumes of COVID-19 patients. COVID-19 spread worldwide, causing many infected patients and deaths. CT image-based diagnosis of COVID-19 can provide quick and accurate diagnosis results. An automated segmentation method of infection regions in the lung provides a quantitative criterion for diagnosis. Previous methods employ whole 2D image or 3D volume-based processes. Infection regions have a considerable variation in their sizes. Such processes easily miss small infection regions. Patch-based process is effective for segmenting small targets. However, selecting the appropriate patch size is difficult in infection region segmentation. We utilize the scale uncertainty among various receptive field sizes of a segmentation FCN to obtain infection regions. The receptive field sizes can be defined as the patch size and the resolution of volumes where patches are clipped from. This paper proposes an infection segmentation network (ISNet) that performs patch-based segmentation and a scale uncertainty-aware prediction aggregation method that refines the segmentation result. We design ISNet to segment infection regions that have various intensity values. ISNet has multiple encoding paths to process patch volumes normalized by multiple intensity ranges. We collect prediction results generated by ISNets having various receptive field sizes. Scale uncertainty among the prediction results is extracted by the prediction aggregation method. We use an aggregation FCN to generate a refined segmentation result considering scale uncertainty among the predictions. In our experiments using 199 chest CT volumes of COVID-19 cases, the prediction aggregation method improved the dice similarity score from 47.6% to 62.1%. © 2021, Springer Nature Switzerland AG.
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The Covid-19 pandemic has fueled the shift towards a more digital world and incremental online shopping behavior changes. It also highlights how decision-makers can leverage the potential of digital transformation in retail and related areas to support business adaption and enhance their competitive ability. This paper measures the top eight e-commerce sites' technological innovation efficiency in the US market from 2016 to 2019 using the data envelopment analysis (DEA) Malmquist model. The proposed model considers total assets, total equity, portfolio investment, and total liabilities as input factors, while total revenue and net income as output factors. This paper aims to provide comprehensive managerial implications to the e-commerce industry for assessing technological evolution effects with other competitors. The results show that the best performing e-commerce site is DMU2-eBay, followed by DMU4-Target and DMU1-Amazon. In contrast, DMU5-Groupon and DMU7-Costco showed the worst performance during the research periods. The current study assists e-commerce practitioners and investors in determining key performance indicators, aiming for effective strategies, and expediting the industry toward sustainability development. © 2021 Author(s).
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Travel fears and restrictions, imposed capacity limitations, and the inability to hold events and large group gatherings have stifled hotel demand and caused devastating revenue losses for the hotel industry during the COVID-19 pandemic. The financial losses will undoubtedly affect hotel firms’ information technology (IT) investments in the long run. This paper aims to develop a framework to assist hotel executives in capturing more insights regarding the relationship between input resources and desired outputs throughout the production process. Accordingly, hotel executives will be able to evaluate and make appropriate IT investment decisions to strategically and effectively allocate scare financial resources in order to improve firm performance. © 2021, Springer Nature Switzerland AG.