Cellular and Molecular Atlas of Peripheral Blood Mononuclear Cells from a Pregnant Woman after Recovery from COVID-19

Objective This study aimed to investigate the immune response of a pregnant woman who recovered from the coronavirus disease 2019 (COVID_RS) by using single-cell transcriptomic profiling of peripheral blood mononuclear cells (PBMCs) and to analyze the properties of different immune cell subsets. Methods PBMCs were collected from the COVID_RS patient at 28 weeks of gestation, before a cesarean section. The PBMCs were then analyzed using single-cell RNA sequencing. The transcriptional profiles of myeloid, T, and natural killer (NK) cell subsets were systematically analyzed and compared with those of healthy pregnant controls from a published single-cell RNA sequencing data set. Results We identified major cell types such as T cells, B cells, NK cells, and myeloid cells in the PBMCs of our COVID_RS patient. The increase of myeloid and B cells and decrease of T cells and NK cells in the PBMCs in this patient were quite distinct compared with that in the control subjects. After reclustering and Augur analysis, we found that CD16 monocytes and mucosal-Associated invariant T (MAIT) cells were mostly affected within different myeloid, T, and NK cell subtypes in our COVID_RS patient. The proportion of CD16 monocytes in the total myeloid population was increased, and the frequency of MAIT cells in the total T and NK cells was significantly decreased in the COVID-RS patient. We also observed significant enrichment of gene sets related to antigen processing and presentation, T-cell activation, T-cell differentiation, and tumor necrosis factor superfamily cytokine production in CD16 monocytes, and enrichment of gene sets related to antigen processing and presentation, response to type II interferon, and response to virus in MAIT cells. Conclusion Our study provides a single-cell resolution atlas of the immune gene expression patterns in PBMCs from a COVID_RS patient. Our findings suggest that CD16-positive monocytes and MAIT cells likely play crucial roles in the maternal immune response against severe acute respiratory syndrome coronavirus 2 infection. These results contribute to a better understanding of the maternal immune response to severe acute respiratory syndrome coronavirus 2 infection and may have implications for the development of effective treatments and preventive strategies for the coronavirus disease 2019 in pregnant women.Copyright © Wolters Kluwer Health, Inc. All rights reserved.

Mental health status of military healthcare workers in shelter hospitals in Shanghai during the epidemic caused by severe acute respiratory syndrome coronavirus 2 omicron variant and its influencing factors.

Objective To investigate the mental health status of military healthcare workers in shelter hospitals in Shanghai during the epidemic caused by severe acute respiratory syndrome coronavirus 2 omicron variant and its influencing factors. Methods A total of 540 military healthcare workers in shelter hospitals in Shanghai were investigated with patient health questionnaire-9 (PHQ-9), generalized anxiety disorder-7 (GAD-7) and Athens insomnia scale (AIS) to explore their mental health status, and logistic regression was used to analyze the influencing factors. Results A total of 536 valid questionnaires were collected, with an effective rate of 99.3% (536/540). The incidence of depression, anxiety and insomnia among military healthcare workers in shelter hospitals in Shanghai was 45.5% (244/536), 26.1% (140/536) and 59.5% (319/536), respectively. Logistic regression analysis showed that whether people resided in Shanghai, the proportion of negative information in daily browsing information and diet status in shelter hospitals were the influencing factors of depression, anxiety and insomnia (all P<0.05);age and confidence in the future of Shanghai were the influencing factors of depression and insomnia (all P<0.05);and the time spent daily on epidemic-related information was an influencing factor of insomnia (P=0.021). Conclusion The incidence of depressive, anxiety and insomnia among military healthcare workers in shelter hospitals in Shanghai is high during the epidemic caused by severe acute respiratory syndrome coronavirus 2 omicron variant. Psychological consequences of the epidemic should be monitored regularly and continuously to promote the mental health of military healthcare workers.Copyright © 2022, Second Military Medical University Press. All rights reserved.

Mental health status of military healthcare workers in shelter hospitals in Shanghai during the epidemic caused by severe acute respiratory syndrome coronavirus 2 omicron variant and its influencing factors

Objective To investigate the mental health status of military healthcare workers in shelter hospitals in Shanghai during the epidemic caused by severe acute respiratory syndrome coronavirus 2 omicron variant and its influencing factors. Methods A total of 540 military healthcare workers in shelter hospitals in Shanghai were investigated with patient health questionnaire-9 (PHQ-9), generalized anxiety disorder-7 (GAD-7) and Athens insomnia scale (AIS) to explore their mental health status, and logistic regression was used to analyze the influencing factors. Results A total of 536 valid questionnaires were collected, with an effective rate of 99.3% (536/540). The incidence of depression, anxiety and insomnia among military healthcare workers in shelter hospitals in Shanghai was 45.5% (244/536), 26.1% (140/536) and 59.5% (319/536), respectively. Logistic regression analysis showed that whether people resided in Shanghai, the proportion of negative information in daily browsing information and diet status in shelter hospitals were the influencing factors of depression, anxiety and insomnia (all P<0.05);age and confidence in the future of Shanghai were the influencing factors of depression and insomnia (all P<0.05);and the time spent daily on epidemic-related information was an influencing factor of insomnia (P=0.021). Conclusion The incidence of depressive, anxiety and insomnia among military healthcare workers in shelter hospitals in Shanghai is high during the epidemic caused by severe acute respiratory syndrome coronavirus 2 omicron variant. Psychological consequences of the epidemic should be monitored regularly and continuously to promote the mental health of military healthcare workers.Copyright © 2022, Second Military Medical University Press. All rights reserved.

A Medical Device That Can Be Used for Psychotherapy in the Metaverse

In 2020, a new coronavirus swept the world, and the advent of this disease has a huge impact on our social and economic development. Due to the limited medical resources and regional differences, this model of virtual medicine becomes more valuable. In this paper, we create a virtual medical space based on a metaverse in order to investigate whether the medical model can be freely transformed between virtual and reality. In this process, I first describe different scenarios of virtual medical care in mixed reality, and then we use one of them as an example to develop a medical device. Then we designed the software and hardware of the product and performed the user experience, it includes the interaction and usage scenarios that affect the user. Finally, this medical device will be demonstrated by user experience and feedback. © 2023 The authors and IOS Press.

Mapping the intersection of nanotechnology and SARS-CoV-2/COVID-19: A bibliometric analysis

Background: Coronavirus disease 2019 (COVID-19), caused by infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has imposed great medical and economic burdens on human society, and nanotechnology is a promising technique for managing the ongoing COVID-19 pandemic. To drive further studies on anti-COVID-19 nanotechnology, this paper provides an analysis, from a bibliometric perspective, of the intersection of nanotechnology and SARS-CoV-2/COVID-19. Methods: We analyzed the 2585 publications on nanotechnology and SARS-CoV-2/COVID-19 included in the Web of Science Core Collection from January 2019 to March 2022 to determine the bibliometric landscape. The basic bibliometric characteristics are summarized in this article. Results: Our bibliometric analysis revealed that the intersection between nanotechnology and SARS-CoV-2/COVID-19 is a cutting-edge field in the science community and that the related studies were multidisciplinary in nature. Studies on the structural basis of SARS-CoV-2, SARS-CoV-2 detection assays, and mRNA vaccines against COVID-19 provided the development foundation for this field. Conclusions: The current research focuses are the development of nanomaterial-based vaccines and SARS-CoV-2 detection methods, and the design of nanomedicines carrying SARS-CoV-2 inhibitors is a relatively burgeoning frontier. In summary, this bibliometric analysis of the intersection of nanotechnology and SARS-CoV-2/COVID-19 highlights the current research focuses of this field to inspire future studies on anti-COVID-19 nanotechnologies. © 2022

ACE2 and TMPRSS2 Expression in Hepatocytes of Chronic HBV Infection Patients

Background:Pre-existing liver disease is a risk factor for the worse prognosis of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. We aimed to evaluate whether chronic hepatitis B (CHB) and hepatocellular carcinoma (HCC) affect the expression of viral receptor angiotensin-converting enzyme 2 (ACE2) and transmembrane serine protease 2 (TMPRSS2) in the liver.Methods:Twelve pairs of matched liver tissues of HCC and para-carcinoma were collected from the First Affiliated Hospital of Zhejiang University School of Medicine. And 20 liver biopsies from CHB patients were collected from Peking University People's Hospital. The expression of ACE2 and TMRPSS2 were detected using immunofluorescence staining, western blot, and RT-qPCR. The effects of hepatitis B virus (HBV) replication or interferon on ACE2 and TMPRSS2 expression were tested in hepatic cell lines.Results:The mRNA expression of TMPRSS2 in HCC tissues was six-fold higher than that of para-carcinoma tissues (P = 0.002), whereas that of ACE2 was not statistically different between HCC and para-carcinoma tissues. Hepatocellular ACE2 expression was detected in 35% (7/20) of CHB patients and mostly distributed in the inflammatory areas. However, there was no difference in TMPRSS2 expression between areas with or without inflammation. IFN-α2b slightly induced ACE2 expression (2.4-fold, P = 0.033) in HepG2 cells but not in Huh-7, QSG-7701, and L-02 cells. IFN-α2b did not affect TMPRSS2 expression in these cell lines. In addition, HBV replication did not alter ACE2 expression in HepAD38 cells.Conclusions:Although HBV replication does not directly affect the expression of ACE2 and TMPRSS2, intrahepatic inflammation and carcinogenesis may increase their expression in some patients, which, in turn, may facilitate SARS-CoV-2 infection in hepatocytes. © 2021 The Chinese Medical Association, Published by Wolters Kluwer Health, Inc.

Strategic responses to the pandemic: a case study of the US department store industry

PurposeThe study focuses on (1) the success of three strategies employed during the pandemic - two "persevering" strategies, curbside pickup and return window extension and one innovative strategy, virtual try-on technology and (2) whether the strategies are likely to be successful in the post-pandemic world.Design/methodology/approachThe authors utilize a panel dataset containing 17 department store chains in the US The panel includes weekly sales by the retailers at the city level from 2018 to 2021, encompassing both a pre-COVID-19 period and a period during the pandemic. A two-way fixed effects model, including retailer-city fixed effects and year-week fixed effects, is used to estimate department store sales.FindingsThe authors find that the two persevering strategies offset the negative impact of government-imposed containment and health measures on sales performance. On the other hand, the innovative strategy is more effective with a low level of containment and health measures, leading to our observation that virtual try-on may be more sustainable than the other two strategies in a post-pandemic environment.Originality/valueThis paper makes the following contributions: First, the authors contribute to the literature on strategies that may be used to respond to crises. Second, the authors contribute to the retail management literature, assessing the impact of the three retail strategies on department store sales. Finally, the authors compare the impact on sales of the two persevering strategies to the innovative strategy and conclude that a mix of these types of strategies may be most effective at generating short-term sales during a crisis and longer-term sales post crisis.

Study on the Discovery of Personal Information on the Network of People Diagnosed with COVID-19

The arbitrary disclosure of information of people diagnosed with COVID-19on the network will adversely affect personal privacy and even violate the privacy rights of individuals. Through the method of literature analysis and case analysis, the information of the confirmed patients of COVID-19 is studied on the network disclosure. The study found that information disclosure can be divided into disclosable information and non-disclosure information, and make different ways of dealing with sensitive information, sensitive information must be handled with care, personal information processing must take into account the balance between personal interests and public interests. © 2022, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

A Hybrid Model for Spatio-Temporal Information Recognition in COVID-19 Trajectory Text

Since the outbreak of the COVID-19 epidemic at the end of 2019, the normalization of epidemic prevention and control has become one of the core tasks of the entire country. Health self-examination by checking the trajectory of diagnosed patients has gradually become everyone's basic necessity and essential to epidemic prevention. The COVID-19 patient's spatio-temporal information helps to facilitate the self-inspection of the masses of whether their trajectory overlaps with the confirmed cases, which promotes the epidemic prevention work. This paper, proposes a named entity recognition model to automatically identify the time and place information in the COVID-19 patient trajectory text. The model consists of an ALBERT layer, a Bi-GRU layer, and a GlobalPointer layer. The previous two layers jointly focus on extracting the context's characteristics and the semantic dependencies. And the GlobalPointer layer extracts the corresponding named entities from a global perspective, which improves the recognition ability for the long-nested place and time entities. Compared to the conventional name entity recognition models, our proposed model has high effectiveness because it has a smaller parameter scale and faster training speed. We evaluate the proposed model using a dataset crawled from the official COVID-19 trajectory text. The F1-score of the model has reached 92.86%, which outperforms four traditional named entity recognition models. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Four-dimensional variational assimilation for SO2emission and its application around the COVID-19 lockdown in the spring 2020 over China

Emission inventories are essential for modelling studies and pollution control, but traditional emission inventories are usually updated after a few years based on the statistics of "bottom-up"approach from the energy consumption in provinces, cities, and counties. The latest emission inventories of multi-resolution emission inventory in China (MEIC) was compiled from the statistics for the year 2016 (MEIC_2016). However, the real emissions have varied yearly, due to national pollution control policies and accidental special events, such as the coronavirus disease (COVID-19) pandemic. In this study, a four-dimensional variational assimilation (4DVAR) system based on the "top-down"approach was developed to optimise sulfur dioxide (SO2) emissions by assimilating the data of SO2 concentrations from surface observational stations. The 4DVAR system was then applied to obtain the SO2 emissions during the early period of COVID-19 pandemic (from 17 January to 7 February 2020), and the same period in 2019 over China. The results showed that the average MEIC_2016, 2019, and 2020 emissions were 42.2×106, 40.1×106, and 36.4×106 kg d-1. The emissions in 2020 decreased by 9.2 % in relation to the COVID-19 lockdown compared with those in 2019. For central China, where the lockdown measures were quite strict, the mean 2020 emission decreased by 21.0 % compared with 2019 emissions. Three forecast experiments were conducted using the emissions of MEIC_2016, 2019, and 2020 to demonstrate the effects of optimised emissions. The root mean square error (RMSE) in the experiments using 2019 and 2020 emissions decreased by 28.1 % and 50.7 %, and the correlation coefficient increased by 89.5 % and 205.9 % compared with the experiment using MEIC_2016. For central China, the average RMSE in the experiments with 2019 and 2020 emissions decreased by 48.8 % and 77.0 %, and the average correlation coefficient increased by 44.3 % and 238.7 %, compared with the experiment using MEIC_2016 emissions. The results demonstrated that the 4DVAR system effectively optimised emissions to describe the actual changes in SO2 emissions related to the COVID lockdown, and it can thus be used to improve the accuracy of forecasts. Copyright: © 2022 Yiwen Hu et al.

Unravelling psychiatric heterogeneity and predicting suicide attempts in women with trauma-related dissociation using artificial intelligence

Background: Suicide is a leading cause of death, and rates of attempted suicide have increased during the COVID-19 pandemic. The under-diagnosed psychiatric phenotype of dissociation is associated with elevated suicidal self-injury;however, it has largely been left out of attempts to predict and prevent suicide. Objective: We designed an artificial intelligence approach to identify dissociative patients and predict prior suicide attempts in an unbiased, data-driven manner. Method: Participants were 30 controls and 93 treatment-seeking female patients with posttraumatic stress disorder (PTSD) and various levels of dissociation, including some with the PTSD dissociative subtype and some with dissociative identity disorder (DID). Results: Unsupervised learning models identified patients along a spectrum of dissociation. Moreover, supervised learning models accurately predicted prior suicide attempts with an score up to 0.83. DID had the highest risk of prior suicide attempts, and distinct subtypes of dissociation predicted suicide attempts in PTSD and DID. Conclusions: These findings expand our understanding of the dissociative phenotype and underscore the urgent need to assess for dissociation to identify individuals at high-risk of suicidal self-injury.

A GARCH Framework Analysis of COVID-19 Impacts on SMEs Using Chinese GEM Index

Stock market return analysis and forecasting are an important topic in econometric finance research. Since the traditional ARIMA models do not consider the variation of volatility, their prediction accuracy is not satisfactory to represent highly volatile periods of any stock market. The GARCH model family resolves the heteroskedasticity of a time series, and hence, it performs better in periods of high volatility. This paper explores the impact of the COVID-19 epidemic on Chinese small- and medium-sized enterprises (SMEs) using a GARCH model for Business as usual (BAU) simulation. We use the Chinese Growth Enterprise Market (GEM) stock index to represent the economic situation of SMEs during the COVID-19 period. Then, we extract, analyze, and predict changes in GEM stock volatility, explore the impact on and recovery status of SMEs, and predict their future trends. For BAU simulation, we first preprocess the GEM stock index between 2018 and 2020 and determine the order of autocorrelation and lags of the data to build the mean model. An ARCH effect test on the residual term of the mean equation was found to be significant and help to decide the order of the GARCH framework. Using the model, a BAU simulation was created and compared statistically with the actual GEM index during 2020. The comparison successfully demonstrated that the GEM index has increased volatility during the pandemic, which is in line with our prior hypothesis.

The Research on Social Distance Detection on the Complex Environment of Multi-Pedestrians

To keep a safe social distance plays an important role in the prevention of high-risk diseases. Aiming at the outbreak of COVID-19, in order to regulate the social distance between pedestrians and reduce the risk of COVID-19 spreading among pedestrians, a multi-pedestrians distance measurement method based on monocular vision is reasonably proposed to realize the measurement of the distance between multiple pedestrians under the monitoring perspective. The pedestrian detection model is used by that method to capture the multi-pedestrians target under the monitoring perspective, and the monocular distance measurement principle is also used to achieve the distance measurement between the multi-pedestrians. Through analyzing these distances, the social distance between pedestrians can be regulated. The experimental results show that this method can efficiently and quickly detect people who do not meet the social distance norms. © 2021 IEEE.

Deep learning for drug repurposing: Methods, databases, and applications

Drug development is time-consuming and expensive. Repurposing existing drugs for new therapies is an attractive solution that accelerates drug development at reduced experimental costs, specifically for Coronavirus Disease 2019 (COVID-19), an infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). However, comprehensively obtaining and productively integrating available knowledge and big biomedical data to effectively advance deep learning models is still challenging for drug repurposing in other complex diseases. In this review, we introduce guidelines on how to utilize deep learning methodologies and tools for drug repurposing. We first summarized the commonly used bioinformatics and pharmacogenomics databases for drug repurposing. Next, we discuss recently developed sequence-based and graph-based representation approaches as well as state-of-the-art deep learning-based methods. Finally, we present applications of drug repurposing to fight the COVID-19 pandemic and outline its future challenges. This article is categorized under: Data Science > Artificial Intelligence/Machine Learning

Impact of COVID-19 on Extremely Polluted Air Quality and Trend Forecast in Seven Provinces and Three Cities of China

At the beginning of 2020, broke out. Because the virus is extremely contagious and the mortality rate after infection is extremely high, China and many countries in the world have imposed lockdowns. Air pollutants during the epidemic period have attracted the attention of many scholars. This research is to use predictive models to describe changes in extreme air pollutants. China is the first country in the world to enter the lockdown state. This study uses data from 2015-2020 to compare and predict the concentration of extreme pollutants before and after the lockdown. The results show that the lockdown of the epidemic will reduce the annual average concentration of PM2.5, and the annual average concentration of O-3 will increase first and then decrease. Through analysis, it is concluded that there is a synergistic decrease trend between PM2.5 and O-3. With the various blockade measures for epidemic prevention and control, the reduction of extreme air pollutant concentrations is sustainable. The assessment of China's air quality in conjunction with the can provide scientific guidance for the Chinese government and other relevant departments to formulate policies.</p>

Reliability and validity of revised scale -the Coping Strategies Scale after COVID-19 based on SPSS 18.0 and AMOS 22.0

A cross-section research was conducted on 1969 participants in February 2020 who were randomly selected from public in China. This study evaluated the reliability and validity of revised scale-the Coping Strategies Scale after COVID-19 (CSSC) by using the sample of 420 Chinese from the research. The purpose of the questionnaire revision was to measure people's coping strategies under the threat of COVID-19. Exploratory factor analyses were performed on 200 participants, and confirmatory factor analysis were on 220 participants. SPSS version 18.0 and AMOS 22.0 were used to analyze the data. Regarding the reliability of the scale, internal consistency revealed acceptable coefficient. The T -test was also used to examine the discrimination of the items. The exploratory factor analysis revealed that the scale was suitable for the application of factor analysis. Confirmatory factor analysis via Amos 22.0 was distinguished three dimensions for CSSC: distract, maintain, and escape (GFI=.96;AGFI=.93;RMR=.07;TLI=.95;NFI=.90;RMSEA=.05). The Cronbach's coefficient of the total scale was.79. The revised CSSC shows appropriate structural validity and relatively appropriate reliability, which can be used to measure coping strategies. © 2021 IEEE.

Analysis of the Current Status and Development Trend of Medical Protective Clothing Standards

With the spread of COVID-19 in the world, the medical protective clothing has becoming more and more important. However, there have been few reports on the analysis of the current protective clothing standards. This paper summarizes the current standards of medical protective clothing at home and abroad systematically. Furthermore, the main standards of medical protective clothing (GB19082-2009, ISO 16603-2004, ISO 16604-2004, NFPA1999-2018, EN14126-2003) are compared. Through the analysis, the protection requirements of various standards are compared in detail, the shortcomings of the standards of surgical clothing are pointed out, and lastly suggestions for improvement are put forward.

Research on Standard Analysis and Test Performance of Protective Masks

With the further spread of COVID-19, the market trend the protective mask industry presents new changes, which is both an opportunity and a challenge for the development of China's textile industry. This paper reviews the origin and development of masks and expounds the internal structure and protective mechanism of masks. Masks are an important line of defense against respiratory infections and can reduce the risk of novel coronavirus infection. Masks can not only prevent the patient from spraying droplets, reduce the amount and speed of droplets, but also block the virus containing droplets nucleus, preventing the wearer from inhaling. There are many types of face masks on the market, and different types of face masks have different application ranges. Different types of face masks follow different standards. The author investigated the application scope and relevant standards of masks in various countries, sorted out the classification, standards and evaluation indicators, etc., in order to provide help for medical personnel and the public. Through analysis, the new surgical mask with high antibacterial, high viral resistance and comfortable wearing is an important development direction in the future.

[Salvianolic acid B and its magnesium salt inhibit SARS-CoV-2 infection of Vero-E6 cells by blocking spike protein-mediated membrane fusion].

OBJECTIVE: The investigate the inhibitory effects of the traditional Chinese medicine (TCM) monomer salvianolic acid B (Sal-B) and its magnesium salt Salvia Miltiorrhiza Polyphenolate Injection (ZDDY) against SARS-CoV-2 infection in vitro and explore the molecular mechanism. OBJECTIVE: The anti-SARS-CoV-2 activity of Sal-B and ZDDY was assessed using the authentic and pseudotyped SARS-CoV-2 infection assay. The antiviral targets of Sal-B were identified by molecular docking and molecular dynamics simulation. Circular dichroism spectroscopy was used to examine the structural characteristics of HR1 and HR2 regions of SARS-CoV-2 S protein, and the S protein-mediated cell-cell fusion assay was used to evaluate the effect of Sal-B on virus-cell membrane fusion. Flow cytometry was carried out to analyze the effect of Sal-B on the binding of SARS-CoV-2 RBD to hACE2 receptor. OBJECTIVE: The median effective concentrations (EC50) of Sal-B and ZDDY against SARSCoV-2 infection in Vero-E6 cells were 55.47 µmol/L and 36.07 µg/mL, respectively. Both Sal-B and ZDDY successfully inhibited the entry of SARS-CoV-2 pseudovirus into the cells that stably expressed human ACE2 (ACE2/293T), with half maximal inhibitory concentrations (IC50) of 1.69 µmol/L and 24.81 µg/mL, respectively. Sal-B showed a binding affinity of -8.2 kcal/mol to the 6-helix bundle (6-HB) of SARS-CoV-2 S protein. Molecular dynamics simulation showed stable binding between Sal-B and the 6-HB of SARS-CoV-2 S protein at the predicted binding site. Sal-B disturbed the formation of the secondary structure of 6-HB in HR1P/HR2P mixture, resulting in a significantly lowered α-helicity (P < 0.05). Sal-B dose-dependently inhibited SARS-CoV-2 S protein-mediated cell-cell fusion, with an IC50 of 3.33 µmol/L. Sal-B showed no effect on RBD-Fc protein binding to the ACE2 receptor. OBJECTIVE: Sal-B and its magnesium salt ZDDY can inhibit the entry of SARS-CoV-2 in Vero-E6 cells in vitro by blocking SARS-CoV-2 spike protein-mediated virus-cell membrane fusion.

Quantitative analysis and prediction of the worldwide COVID-19 pandemic

At present, the novel coronavirus disease 2019 (COVID-19) has spread rapidly worldwide. As COVID-19 impacts societies, governments, communities, and individuals, we want to provide information for governments and people to understand the situation better. In this paper, we perform quantitative analysis and prediction of the global COVID-19 pandemic. First, we summarize worldwide confirmed, recovered, death, and active cases. Second, we select eight representative countries and analyze their confirmed and death cases with considering their populations. Third, we exploit the AR model in machine learning to predict the cases of these representative countries for the next 30 days. Our analysis and prediction show that some countries, including the United States, Spain, and Brazil, are in and will still be in the severe COVID-19 outbreak. This work can provide reference information for the prevention and control of COVID-19. © 2021 Institute of Physics Publishing. All rights reserved.