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2.
Brief Bioinform ; 22(2): 631-641, 2021 03 22.
Article in English | MEDLINE | ID: covidwho-1352116

ABSTRACT

In early January 2020, the novel coronavirus (SARS-CoV-2) responsible for a pneumonia outbreak in Wuhan, China, was identified using next-generation sequencing (NGS) and readily available bioinformatics pipelines. In addition to virus discovery, these NGS technologies and bioinformatics resources are currently being employed for ongoing genomic surveillance of SARS-CoV-2 worldwide, tracking its spread, evolution and patterns of variation on a global scale. In this review, we summarize the bioinformatics resources used for the discovery and surveillance of SARS-CoV-2. We also discuss the advantages and disadvantages of these bioinformatics resources and highlight areas where additional technical developments are urgently needed. Solutions to these problems will be beneficial not only to the prevention and control of the current COVID-19 pandemic but also to infectious disease outbreaks of the future.


Subject(s)
COVID-19/virology , Computational Biology , SARS-CoV-2/isolation & purification , COVID-19/epidemiology , Disease Outbreaks/prevention & control , High-Throughput Nucleotide Sequencing/methods , Humans , Pandemics/prevention & control
3.
PLoS One ; 16(8): e0255259, 2021.
Article in English | MEDLINE | ID: covidwho-1344152

ABSTRACT

In response to the soaring needs of human mobility data, especially during disaster events such as the COVID-19 pandemic, and the associated big data challenges, we develop a scalable online platform for extracting, analyzing, and sharing multi-source multi-scale human mobility flows. Within the platform, an origin-destination-time (ODT) data model is proposed to work with scalable query engines to handle heterogenous mobility data in large volumes with extensive spatial coverage, which allows for efficient extraction, query, and aggregation of billion-level origin-destination (OD) flows in parallel at the server-side. An interactive spatial web portal, ODT Flow Explorer, is developed to allow users to explore multi-source mobility datasets with user-defined spatiotemporal scales. To promote reproducibility and replicability, we further develop ODT Flow REST APIs that provide researchers with the flexibility to access the data programmatically via workflows, codes, and programs. Demonstrations are provided to illustrate the potential of the APIs integrating with scientific workflows and with the Jupyter Notebook environment. We believe the platform coupled with the derived multi-scale mobility data can assist human mobility monitoring and analysis during disaster events such as the ongoing COVID-19 pandemic and benefit both scientific communities and the general public in understanding human mobility dynamics.


Subject(s)
Information Dissemination/methods , Population Dynamics/trends , Big Data , COVID-19/epidemiology , Humans , Models, Statistical , Numerical Analysis, Computer-Assisted , Pandemics/prevention & control , Pandemics/statistics & numerical data , Population Dynamics/statistics & numerical data , Reproducibility of Results , SARS-CoV-2/pathogenicity , Workflow
4.
J Med Internet Res ; 23(9): e30854, 2021 09 10.
Article in English | MEDLINE | ID: covidwho-1341588

ABSTRACT

BACKGROUND: The COVID-19 pandemic has imposed a large, initially uncontrollable, public health crisis both in the United States and across the world, with experts looking to vaccines as the ultimate mechanism of defense. The development and deployment of COVID-19 vaccines have been rapidly advancing via global efforts. Hence, it is crucial for governments, public health officials, and policy makers to understand public attitudes and opinions towards vaccines, such that effective interventions and educational campaigns can be designed to promote vaccine acceptance. OBJECTIVE: The aim of this study was to investigate public opinion and perception on COVID-19 vaccines in the United States. We investigated the spatiotemporal trends of public sentiment and emotion towards COVID-19 vaccines and analyzed how such trends relate to popular topics found on Twitter. METHODS: We collected over 300,000 geotagged tweets in the United States from March 1, 2020 to February 28, 2021. We examined the spatiotemporal patterns of public sentiment and emotion over time at both national and state scales and identified 3 phases along the pandemic timeline with sharp changes in public sentiment and emotion. Using sentiment analysis, emotion analysis (with cloud mapping of keywords), and topic modeling, we further identified 11 key events and major topics as the potential drivers to such changes. RESULTS: An increasing trend in positive sentiment in conjunction with a decrease in negative sentiment were generally observed in most states, reflecting the rising confidence and anticipation of the public towards vaccines. The overall tendency of the 8 types of emotion implies that the public trusts and anticipates the vaccine. This is accompanied by a mixture of fear, sadness, and anger. Critical social or international events or announcements by political leaders and authorities may have potential impacts on public opinion towards vaccines. These factors help identify underlying themes and validate insights from the analysis. CONCLUSIONS: The analyses of near real-time social media big data benefit public health authorities by enabling them to monitor public attitudes and opinions towards vaccine-related information in a geo-aware manner, address the concerns of vaccine skeptics, and promote the confidence that individuals within a certain region or community have towards vaccines.


Subject(s)
COVID-19 , Social Media , COVID-19 Vaccines , Humans , Pandemics , Public Opinion , SARS-CoV-2 , United States
6.
Int J Environ Res Public Health ; 18(13)2021 06 25.
Article in English | MEDLINE | ID: covidwho-1285386

ABSTRACT

Investigating the spatial distribution patterns of disease and suspected determinants could help one to understand health risks. This study investigated the potential risk factors associated with COVID-19 mortality in the continental United States. We collected death cases of COVID-19 from 3108 counties from 23 January 2020 to 31 May 2020. Twelve variables, including demographic (the population density, percentage of 65 years and over, percentage of non-Hispanic White, percentage of Hispanic, percentage of non-Hispanic Black, and percentage of Asian individuals), air toxins (PM2.5), climate (precipitation, humidity, temperature), behavior and comorbidity (smoking rate, cardiovascular death rate) were gathered and considered as potential risk factors. Based on four geographical detectors (risk detector, factor detector, ecological detector, and interaction detector) provided by the novel Geographical Detector technique, we assessed the spatial risk patterns of COVID-19 mortality and identified the effects of these factors. This study found that population density and percentage of non-Hispanic Black individuals were the two most important factors responsible for the COVID-19 mortality rate. Additionally, the interactive effects between any pairs of factors were even more significant than their individual effects. Most existing research examined the roles of risk factors independently, as traditional models are usually unable to account for the interaction effects between different factors. Based on the Geographical Detector technique, this study's findings showed that causes of COVID-19 mortality were complex. The joint influence of two factors was more substantial than the effects of two separate factors. As the COVID-19 epidemic status is still severe, the results of this study are supposed to be beneficial for providing instructions and recommendations for the government on epidemic risk responses to COVID-19.


Subject(s)
COVID-19 , African Americans , European Continental Ancestry Group , Health Status Disparities , Humans , SARS-CoV-2 , United States/epidemiology
7.
Cell ; 184(17): 4380-4391.e14, 2021 08 19.
Article in English | MEDLINE | ID: covidwho-1275186

ABSTRACT

Despite the discovery of animal coronaviruses related to SARS-CoV-2, the evolutionary origins of this virus are elusive. We describe a meta-transcriptomic study of 411 bat samples collected from a small geographical region in Yunnan province, China, between May 2019 and November 2020. We identified 24 full-length coronavirus genomes, including four novel SARS-CoV-2-related and three SARS-CoV-related viruses. Rhinolophus pusillus virus RpYN06 was the closest relative of SARS-CoV-2 in most of the genome, although it possessed a more divergent spike gene. The other three SARS-CoV-2-related coronaviruses carried a genetically distinct spike gene that could weakly bind to the hACE2 receptor in vitro. Ecological modeling predicted the co-existence of up to 23 Rhinolophus bat species, with the largest contiguous hotspots extending from South Laos and Vietnam to southern China. Our study highlights the remarkable diversity of bat coronaviruses at the local scale, including close relatives of both SARS-CoV-2 and SARS-CoV.


Subject(s)
COVID-19/virology , Chiroptera/virology , Coronavirus/genetics , Evolution, Molecular , SARS-CoV-2/genetics , Amino Acid Sequence , Angiotensin-Converting Enzyme 2/chemistry , Angiotensin-Converting Enzyme 2/metabolism , Animals , Asia, Southeastern , China , Coronavirus/classification , Coronavirus/isolation & purification , Ecological and Environmental Phenomena , Genome, Viral , Humans , Models, Molecular , Phylogeny , SARS-CoV-2/physiology , Sequence Alignment , Sequence Analysis, RNA , Spike Glycoprotein, Coronavirus/chemistry , Spike Glycoprotein, Coronavirus/genetics , Spike Glycoprotein, Coronavirus/metabolism , Viral Zoonoses
8.
ISPRS International Journal of Geo-Information ; 10(6):387, 2021.
Article in English | MDPI | ID: covidwho-1259505

ABSTRACT

(1) Background: Human mobility between geographic units is an important way in which COVID-19 is spread across regions. Due to the pressure of epidemic control and economic recovery, states in the United States have adopted different policies for mobility limitations. Assessing the impact of these policies on the spatiotemporal interaction of COVID-19 transmission among counties in each state is critical to formulating epidemic policies. (2) Methods: We utilized Moran’s I index and K-means clustering to investigate the time-varying spatial autocorrelation effect of 49 states (excluding the District of Colombia) with daily new cases at the county level from 22 January 2020 to 20 August 2020. Based on the dynamic spatial lag model (SLM) and the SIR model with unreported infection rate (SIRu), the integrated SLM-SIRu model was constructed to estimate the inter-county spatiotemporal interaction coefficient of daily new cases in each state, which was further explored by Pearson correlation test and stepwise OLS regression with socioeconomic factors. (3) Results: The K-means clustering divided the time-varying spatial autocorrelation curves of the 49 states into four types: continuous increasing, fluctuating increasing, weak positive, and weak negative. The Pearson correlation analysis showed that the spatiotemporal interaction coefficients in each state estimated by SLM-SIRu were significantly positively correlated with the variables of median age, population density, and proportions of international immigrants and highly educated population, but negatively correlated with the birth rate. Further stepwise OLS regression retained only three positive correlated variables: poverty rate, population density, and highly educated population proportion. (4) Conclusions: This result suggests that various state policies in the U.S. have imposed different impacts on COVID-19 transmission among counties. All states should provide more protection and support for the low-income population;high-density populated states need to strengthen regional mobility restrictions;and the highly educated population should reduce unnecessary regional movement and strengthen self-protection.

9.
Eng Life Sci ; 21(6): 453-460, 2021 Jun.
Article in English | MEDLINE | ID: covidwho-1230200

ABSTRACT

SARS-CoV-2 is responsible for a disruptive worldwide viral pandemic, and renders a severe respiratory disease known as COVID-19. Spike protein of SARS-CoV-2 mediates viral entry into host cells by binding ACE2 through the receptor-binding domain (RBD). RBD is an important target for development of virus inhibitors, neutralizing antibodies, and vaccines. RBD expressed in mammalian cells suffers from low expression yield and high cost. E. coli is a popular host for protein expression, which has the advantage of easy scalability with low cost. However, RBD expressed by E. coli (RBD-1) lacks the glycosylation, and its antigenic epitopes may not be sufficiently exposed. In the present study, RBD-1 was expressed by E. coli and purified by a Ni Sepharose Fast Flow column. RBD-1 was structurally characterized and compared with RBD expressed by the HEK293 cells (RBD-2). The secondary structure and tertiary structure of RBD-1 were largely maintained without glycosylation. In particular, the major ß-sheet content of RBD-1 was almost unaltered. RBD-1 could strongly bind ACE2 with a dissociation constant (KD) of 2.98 × 10-8 M. Thus, RBD-1 was expected to apply in the vaccine development, screening drugs and virus test kit.

10.
Child Youth Serv Rev ; 126: 106012, 2021 Jul.
Article in English | MEDLINE | ID: covidwho-1174141

ABSTRACT

Background: COVID-19 was first recognized in late 2019 in China, at which time school closures forced most students to isolate at home or maintain social distance, both of which increased smartphone use, daytime sleepiness and post traumatic disorder (PTSD) risks. However, to date, no research has fully explored these behavioral risks or the consequences. Methods: Two thousand and ninety home-confined students from two Chinese high schools participated in an online-based questionnaire battery that assessed their sociodemographic characteristics, COVID-19 related exposures, daytime sleepiness, problematic smartphone use, and PTSD. The subsequent data were subjected to mediation analysis, and structural equation models (SEM) were employed to explore the variable relationships. Results: The problematic smartphone use, daytime sleepiness and PTSD prevalence were respectively 16.4%, 20.2% and 6.9%. The number of COVID-19 related exposure was directly associated with problematic smartphone use and PTSD symptoms. Problematic smartphone use was found to be a mediator between COVID-19 related exposure and PTSD symptoms, and daytime sleepiness was found to partially mediate the associations between problematic smartphone use and PTSD. Conclusions: The more exposure associated with the pandemic, the more psychological and behavioral problems the adolescents had. The relatively high rate of problematic smartphone use in home isolated adolescents possibly increased the risk of daytime sleepiness and psychological problems. Therefore, targeted improvements are needed to reduce the risk of psychological problems and daytime sleepiness in adolescents.

11.
IEEE Access ; 9: 28646-28657, 2021.
Article in English | MEDLINE | ID: covidwho-1101969

ABSTRACT

Studying the spatiotemporal differences in coronavirus disease (COVID-19) between social groups such as healthcare workers (HCWs) and patients can aid in formulating epidemic containment policies. Most previous studies of the spatiotemporal characteristics of COVID-19 were conducted in a single group and did not explore the differences between groups. To fill this research gap, this study assessed the spatiotemporal characteristics and differences among patients and HCWs infection in Wuhan, Hubei (excluding Wuhan), and China (excluding Hubei). The temporal difference was greater in Wuhan than in the rest of Hubei, and was greater in Hubei (excluding Wuhan) than in the rest of China. The incidence was high in healthcare workers in the early stages of the epidemic. Therefore, it is important to strengthen the protective measures for healthcare workers in the early stage of the epidemic. The spatial difference was less in Wuhan than in the rest of Hubei, and less in Hubei (excluding Wuhan) than in the rest of China. The spatial distribution of healthcare worker infections can be used to infer the spatial distribution of the epidemic in the early stage and to formulate control measures accordingly.

12.
Cell Transplant ; 30: 963689720985453, 2021.
Article in English | MEDLINE | ID: covidwho-1069506

ABSTRACT

In this work, we discovered a new phenomenon-asymptomatic COVID-19 infection, or covert case, during the pandemic. All the 3 patients had a history of exposure, with no symptoms, and no abnormalities were found in computed tomography scan or lab tests. Except for case 2, the other patients' severe acute respiratory syndrome coronavirus 2 (SARS-COV-2) nucleic acid tests were negative. But their anti-SARS-COV-2 nucleocapsid antibody showed a dynamic trend, consistent with the process of virus infection and clearance. A growing number of asymptomatic or covert cases need more attention. Lack of surveillance may lead to another outbreak. We hope to demonstrate our cases to attract the attention of governments or health authorities that covert cases should be the focus as well.


Subject(s)
COVID-19 Testing/methods , COVID-19/diagnosis , COVID-19/virology , SARS-CoV-2/isolation & purification , Adult , Child , Female , Humans , Male , SARS-CoV-2/genetics
13.
Int J Environ Res Public Health ; 18(3)2021 01 26.
Article in English | MEDLINE | ID: covidwho-1050609

ABSTRACT

BACKGROUND: Potential unreported infection might impair and mislead policymaking for COVID-19, and the contemporary spread of COVID-19 varies in different counties of the United States. It is necessary to estimate the cases that might be underestimated based on county-level data, to take better countermeasures against COVID-19. We suggested taking time-varying Susceptible-Infected-Recovered (SIR) models with unreported infection rates (UIR) to estimate factual COVID-19 cases in the United States. METHODS: Both the SIR model integrated with unreported infection rates (SIRu) of fixed-time effect and SIRu with time-varying parameters (tvSIRu) were applied to estimate and compare the values of transmission rate (TR), UIR, and infection fatality rate (IFR) based on US county-level COVID-19 data. RESULTS: Based on the US county-level COVID-19 data from 22 January (T1) to 20 August (T212) in 2020, SIRu was first tested and verified by Ordinary Least Squares (OLS) regression. Further regression of SIRu at the county-level showed that the average values of TR, UIR, and IFR were 0.034%, 19.5%, and 0.51% respectively. The ranges of TR, UIR, and IFR for all states ranged from 0.007-0.157 (mean = 0.048), 7.31-185.6 (mean = 38.89), and 0.04-2.22% (mean = 0.22%). Among the time-varying TR equations, the power function showed better fitness, which indicated a decline in TR decreasing from 227.58 (T1) to 0.022 (T212). The general equation of tvSIRu showed that both the UIR and IFR were gradually increasing, wherein, the estimated value of UIR was 9.1 (95%CI 5.7-14.0) and IFR was 0.70% (95%CI 0.52-0.95%) at T212. INTERPRETATION: Despite the declining trend in TR and IFR, the UIR of COVID-19 in the United States is still on the rise, which, it was assumed would decrease with sufficient tests or improved countersues. The US medical system might be largely affected by severe cases amidst a rapid spread of COVID-19.


Subject(s)
COVID-19 , Disease Notification , COVID-19/epidemiology , Disease Notification/statistics & numerical data , Humans , Models, Statistical , Regression Analysis , United States/epidemiology
16.
Int J Environ Res Public Health ; 17(24)2020 12 19.
Article in English | MEDLINE | ID: covidwho-1011501

ABSTRACT

The U.S. has merely 4% of the world population, but contains 25% of the world's COVID-19 cases. Since the COVID-19 outbreak in the U.S., Massachusetts has been leading other states in the total number of COVID-19 cases. Racial residential segregation is a fundamental cause of racial disparities in health. Moreover, disparities of access to health care have a large impact on COVID-19 cases. Thus, this study estimates racial segregation and disparities in testing site access and employs economic, demographic, and transportation variables at the city/town level in Massachusetts. Spatial regression models are applied to evaluate the relationships between COVID-19 incidence rate and related variables. This is the first study to apply spatial analysis methods across neighborhoods in the U.S. to examine the COVID-19 incidence rate. The findings are: (1) Residential segregations of Hispanic and Non-Hispanic Black/African Americans have a significantly positive association with COVID-19 incidence rate, indicating the higher susceptibility of COVID-19 infections among minority groups. (2) Non-Hispanic Black/African Americans have the shortest drive time to testing sites, followed by Hispanic, Non-Hispanic Asians, and Non-Hispanic Whites. The drive time to testing sites is significantly negatively associated with the COVID-19 incidence rate, implying the importance of the accessibility of testing sites by all populations. (3) Poverty rate and road density are significant explanatory variables. Importantly, overcrowding represented by more than one person per room is a significant variable found to be positively associated with COVID-19 incidence rate, suggesting the effectiveness of social distancing for reducing infection. (4) Different from the findings of previous studies, the elderly population rate is not statistically significantly correlated with the incidence rate because the elderly population in Massachusetts is less distributed in the hotspot regions of COVID-19 infections. The findings in this study provide useful insights for policymakers to propose new strategies to contain the COVID-19 transmissions in Massachusetts.


Subject(s)
COVID-19/ethnology , Health Services Accessibility , Social Segregation , African Americans , Health Status Disparities , Hispanic Americans , Humans , Incidence , Massachusetts/epidemiology
17.
International Journal of Environmental Research and Public Health ; 17(24):9528, 2020.
Article in English | ScienceDirect | ID: covidwho-984386

ABSTRACT

The U.S. has merely 4% of the world population, but contains 25% of the world’s COVID-19 cases. Since the COVID-19 outbreak in the U.S., Massachusetts has been leading other states in the total number of COVID-19 cases. Racial residential segregation is a fundamental cause of racial disparities in health. Moreover, disparities of access to health care have a large impact on COVID-19 cases. Thus, this study estimates racial segregation and disparities in testing site access and employs economic, demographic, and transportation variables at the city/town level in Massachusetts. Spatial regression models are applied to evaluate the relationships between COVID-19 incidence rate and related variables. This is the first study to apply spatial analysis methods across neighborhoods in the U.S. to examine the COVID-19 incidence rate. The findings are: (1) Residential segregations of Hispanic and Non-Hispanic Black/African Americans have a significantly positive association with COVID-19 incidence rate, indicating the higher susceptibility of COVID-19 infections among minority groups. (2) Non-Hispanic Black/African Americans have the shortest drive time to testing sites, followed by Hispanic, Non-Hispanic Asians, and Non-Hispanic Whites. The drive time to testing sites is significantly negatively associated with the COVID-19 incidence rate, implying the importance of the accessibility of testing sites by all populations. (3) Poverty rate and road density are significant explanatory variables. Importantly, overcrowding represented by more than one person per room is a significant variable found to be positively associated with COVID-19 incidence rate, suggesting the effectiveness of social distancing for reducing infection. (4) Different from the findings of previous studies, the elderly population rate is not statistically significantly correlated with the incidence rate because the elderly population in Massachusetts is less distributed in the hotspot regions of COVID-19 infections. The findings in this study provide useful insights for policymakers to propose new strategies to contain the COVID-19 transmissions in Massachusetts.

19.
Engineering (Beijing) ; 2020 Nov 28.
Article in English | MEDLINE | ID: covidwho-947208

ABSTRACT

Coronavirus disease 2019 (COVID-19) has become a worldwide pandemic. Hospitalized patients of COVID-19 suffer from a high mortality rate, motivating the development of convenient and practical methods that allow clinicians to promptly identify high-risk patients. Here, we have developed a risk score using clinical data from 1479 inpatients admitted to Tongji Hospital, Wuhan, China (development cohort) and externally validated with data from two other centers: 141 inpatients from Jinyintan Hospital, Wuhan, China (validation cohort 1) and 432 inpatients from The Third People's Hospital of Shenzhen, Shenzhen, China (validation cohort 2). The risk score is based on three biomarkers that are readily available in routine blood samples and can easily be translated into a probability of death. The risk score can predict the mortality of individual patients more than 12 d in advance with more than 90% accuracy across all cohorts. Moreover, the Kaplan-Meier score shows that patients can be clearly differentiated upon admission as low, intermediate, or high risk, with an area under the curve (AUC) score of 0.9551. In summary, a simple risk score has been validated to predict death in patients infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); it has also been validated in independent cohorts.

20.
Biotechniques ; 70(1): 7-20, 2021 01.
Article in English | MEDLINE | ID: covidwho-940124

ABSTRACT

A real-time dPCR system was developed to improve the sensitivity, specificity and quantification accuracy of end point dPCR. We compared three technologies - real-time qPCR, end point dPCR and real-time dPCR - in the context of SARS-CoV-2. Some improvement in limit of detection was obtained with end point dPCR compared with real-time qPCR, and the limit of detection was further improved with the newly developed real-time dPCR technology through removal of false-positive signals. Real-time dPCR showed increased linear dynamic range compared with end point dPCR based on quantitation from amplification curves. Real-time dPCR can improve the performance of TaqMan assays beyond real-time qPCR and end point dPCR with better sensitivity and specificity, absolute quantification and a wider linear range of detection.


Subject(s)
COVID-19 Nucleic Acid Testing/methods , Real-Time Polymerase Chain Reaction , SARS-CoV-2/isolation & purification , COVID-19/diagnosis , COVID-19 Nucleic Acid Testing/statistics & numerical data , Endpoint Determination , Humans
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