Your browser doesn't support javascript.
Show: 20 | 50 | 100
Results 1 - 20 de 70
Filter
1.
Information Technology & People ; 35(4):1364-1382, 2022.
Article in English | ProQuest Central | ID: covidwho-1878903

ABSTRACT

Purpose>Most students are considered digital natives and are presumably equipped to handle extensive technology use. However, online learning turns students into involuntary telecommuters when it is the primary modality. The prevailing trends of online learning, digital socialization, telehealth and other online services, combined with remote work has increased students' reliance on information and communications technologies (ICTs) for all purposes, which may be overwhelming. We examine how technology overload strains the ability of online learning to meet students' basic psychological needs (BPNs), which can decrease positive outcomes such as academic enjoyment and personal performance.Design/methodology/approach>Data was collected via an online survey of 542 university students and the proposed model was tested using partial least squares (PLS) regression.Findings>We find that technology overload can diminish the positive relationship between online learning intensity and BPNs satisfaction, which is alarming because BPNs satisfaction is critical to students' positive experiences. Moreover, we find that technology overload and lack of technology experience can directly drive BPNs frustration, which decreases positive outcomes and increases academic anxiety.Originality/value>We extend a theoretical framework for telecommuting to examine online learning. Additionally, we consider the role of technology overload and experience both as drivers and as moderators of students' BPNs satisfaction and frustration in online learning. Our results provide valuable insights that can inform efforts to rebalance the deployment of ICTs to facilitate online educational experiences.

2.
Applied Sciences ; 12(10):4813, 2022.
Article in English | ProQuest Central | ID: covidwho-1871274

ABSTRACT

Senior management in tertiary institutions desires an efficient system that could help them assess and evaluate learning outcomes so that effective policies can be implemented to enhance teaching and learning. This gets intensified as broader issues arise and higher expectations are put on tertiary education—build a creative workforce and adapt to new technologies to analyze the large volume of teaching and learning data. Government and higher education policymakers have to rapidly adjust relevant policies to surmount the challenges from the pandemic and also to keep up with technological advancement. This demands a novel and efficient way for policymakers and senior management to see and gain insights from a large volume of data (e.g., student course and teacher evaluation). In this study, the researchers present such a system through various examples. The findings generated from this study contribute to the scholarship, and they provide a solution to senior management in tertiary institutions wanting to implement effective policies efficiently. The use of online analytical processing, virtual campus, online, and machine learning in education is growing. However, the use of these technology-enhanced approaches is rare in performing arts education. There has been no in-depth study, especially on technology-enhanced learning that leads to the improvement of teaching. This study utilizes a multi-dimensional analysis approach on the course student evaluation, a key aspect of the teaching and learning quality assurance for higher education. A novel analytical framework is developed and implemented at a leading performing arts university in Asia. It analyzes the course evaluation data of all courses (669 courses and 2664 responses) in the academic year 2018/2019 to make evidence-based recommendations. Such a framework provides an easy and effective visualization for senior management to identify courses that need closer scrutiny to ascertain whether and what areas of course enhancement measures are warranted.

3.
Mathematics ; 10(9):1583, 2022.
Article in English | ProQuest Central | ID: covidwho-1842594

ABSTRACT

As the novel coronavirus pandemic has spread globally since 2019, most countries in the world are conducting vaccination campaigns. First, based on the traditional SIR infectious disease model, we introduce a positive feedback mechanism associated with the vaccination rate, and consider the time delay from antibody production to antibody disappearance after vaccination. We establish an UVaV model for COVID-19 vaccination with a positive feedback mechanism and time-delay. Next, we verify the existence of the equilibrium of the formulated model and analyze its stability. Then, we analyze the existence of the Hopf bifurcation, and use the multiple time scales method to derive the normal form of the Hopf bifurcation, further determining the direction of the Hopf bifurcation and the stability of the periodic solution of the bifurcation. Finally, we collect the parameter data of some countries and regions to determine the reasonable ranges of multiple parameters to ensure the authenticity of simulation results. Numerical simulations are carried out to verify the correctness of the theoretical results. We also give the critical time for controllable widespread antibody failure to provide a reference for strengthening vaccination time. Taking two groups of parameters as examples, the time of COVID-19 vaccine booster injection should be best controlled before 38.5 weeks and 35.3 weeks, respectively. In addition, study the impact of different expiration times on epidemic prevention and control effectiveness. We further explore the impact of changes in vaccination strategies on trends in epidemic prevention and control effectiveness. It could be concluded that, under the same epidemic vaccination strategy, the existence level of antibody is roughly the same, which is consistent with the reality.

4.
Med Biol Eng Comput ; 60(6): 1763-1774, 2022 Jun.
Article in English | MEDLINE | ID: covidwho-1803060

ABSTRACT

Although some studies tried to identify risk factors for COVID-19, the evidence comparing COVID-19 and community-acquired pneumonia (CAP) is inconclusive, and CAP is the most common pneumonia with similar symptoms as COVID-19. We conducted a case-control study with 35 routine-collected clinical indicators and demographic factors to identify predictors for COVID-19 with CAP as controls. We randomly split the dataset into a training set (70%) and testing set (30%). We built Explainable Boosting Machine to select the important factors and built a decision tree on selected variables to interpret their relationships. The top five individual predictors of COVID-19 are albumin, total bilirubin, monocyte count, alanine aminotransferase, and percentage of monocyte with the importance scores ranging from 0.078 to 0.567. The top systematic predictors for COVID-19 are liver function, monocyte increasing, plasma protein, granulocyte, and renal function (importance scores ranging 0.009-0.096). We identified five combinations of important indicators to screen COVID-19 patients from CAP patients with differentiating abilities ranging 83.3-100%. An online predictive tool for our model was published. Certain clinical indicators collected routinely from most hospitals could help screen and distinguish COVID-19 from CAP. While further verification is needed, our findings and predictive tool could help screen suspected COVID-19 cases.


Subject(s)
COVID-19 , Pneumonia , COVID-19/diagnosis , Case-Control Studies , Humans , Machine Learning , Pneumonia/diagnosis , Risk Factors
5.
J Med Virol ; 94(5): 1935-1949, 2022 05.
Article in English | MEDLINE | ID: covidwho-1777575

ABSTRACT

The COVID-19 pandemic and related restrictions can impact mental health. To quantify the mental health burden of COVID-19 pandemic, we conducted a systematic review and meta-analysis, searching World Health Organization COVID-19/PsycInfo/PubMed databases (09/29/2020), including observational studies reporting on mental health outcomes in any population affected by COVID-19. Primary outcomes were the prevalence of anxiety, depression, stress, sleep problems, posttraumatic symptoms. Sensitivity analyses were conducted on severe mental health problems, in high-quality studies, and in representative samples. Subgroup analyses were conducted stratified by age, sex, country income level, and COVID-19 infection status. One-hundred-seventy-three studies from February to July 2020 were included (n = 502,261, median sample = 948, age = 34.4 years, females = 63%). Ninety-one percent were cross-sectional studies, and 18.5%/57.2% were of high/moderate quality. The highest prevalence emerged for posttraumatic symptoms in COVID-19 infected people (94%), followed by behavioral problems in those with prior mental disorders (77%), fear in healthcare workers (71%), anxiety in caregivers/family members of people with COVID-19 (42%), general health/social contact/passive coping style in the general population (38%), depression in those with prior somatic disorders (37%), and fear in other-than-healthcare workers (29%). Females and people with COVID-19 infection had higher rates of almost all outcomes; college students/young adults of anxiety, depression, sleep problems, suicidal ideation; adults of fear and posttraumatic symptoms. Anxiety, depression, and posttraumatic symptoms were more prevalent in low-/middle-income countries, sleep problems in high-income countries. The COVID-19 pandemic adversely impacts mental health in a unique manner across population subgroups. Our results inform tailored preventive strategies and interventions to mitigate current, future, and transgenerational adverse mental health of the COVID-19 pandemic.


Subject(s)
COVID-19 , Pandemics , Adult , COVID-19/epidemiology , Depression/epidemiology , Female , Humans , Mental Health , Prevalence , SARS-CoV-2 , Young Adult
6.
Front Mol Biosci ; 9: 836862, 2022.
Article in English | MEDLINE | ID: covidwho-1775720

ABSTRACT

Purpose: Computer-aided diagnostic methods were used to compare the characteristics of the Original COVID-19 and its Delta Variant. Methods: This was a retrospective study. A deep learning segmentation model was applied to segment lungs and infections in CT. Three-dimensional (3D) reconstruction was used to create 3D models of the patient's lungs and infections. A stereoscopic segmentation method was proposed, which can subdivide the 3D lung into five lobes and 18 segments. An expert-based CT scoring system was improved and artificial intelligence was used to automatically score instead of visual score. Non-linear regression and quantitative analysis were used to analyze the dynamic changes in the percentages of infection (POI). Results: The POI in the five lung lobes of all patients were calculated and converted into CT scores. The CT scores of Original COVID-19 patients and Delta Variant patients since the onset of initial symptoms were fitted over time, respectively. The peak was found to occur on day 11 in Original COVID-19 patients and on day 15 in Delta Variant patients. The time course of lung changes in CT of Delta Variant patients was redetermined as early stage (0-3 days), progressive and peak stage (4-16 days), and absorption stage (17-42 days). The first RT-PCR negative time in Original COVID-19 patients appeared earlier than in Delta Variant patients (22 [17-30] vs. 39 [31-44], p < 0.001). Delta Variant patients had more re-detectable positive RT-PCR test results than Original COVID-19 patients after the first negative RT-PCR time (30.5% vs. 17.1%). In the early stage, CT scores in the right lower lobe were significantly different (Delta Variant vs. Original COVID-19, 0.8 ± 0.6 vs. 1.3 ± 0.6, p = 0.039). In the absorption stage, CT scores of the right middle lobes were significantly different (Delta Variant vs. Original COVID-19, 0.6 ± 0.7 vs. 0.3 ± 0.4, p = 0.012). The left and the right lower lobes contributed most to lung involvement at any given time. Conclusion: Compared with the Original COVID-19, the Delta Variant has a longer lung change duration, more re-detectable positive RT-PCR test results, different locations of pneumonia, and more lesions in the early stage, and the peak of infection occurred later.

7.
J Med Virol ; 94(6): 2402-2413, 2022 06.
Article in English | MEDLINE | ID: covidwho-1718416

ABSTRACT

The aim of this study is to provide a more accurate representation of COVID-19's case fatality rate (CFR) by performing meta-analyses by continents and income, and by comparing the result with pooled estimates. We used multiple worldwide data sources on COVID-19 for every country reporting COVID-19 cases. On the basis of data, we performed random and fixed meta-analyses for CFR of COVID-19 by continents and income according to each individual calendar date. CFR was estimated based on the different geographical regions and levels of income using three models: pooled estimates, fixed- and random-model. In Asia, all three types of CFR initially remained approximately between 2.0% and 3.0%. In the case of pooled estimates and the fixed model results, CFR increased to 4.0%, by then gradually decreasing, while in the case of random-model, CFR remained under 2.0%. Similarly, in Europe, initially, the two types of CFR peaked at 9.0% and 10.0%, respectively. The random-model results showed an increase near 5.0%. In high-income countries, pooled estimates and fixed-model showed gradually increasing trends with a final pooled estimates and random-model reached about 8.0% and 4.0%, respectively. In middle-income, the pooled estimates and fixed-model have gradually increased reaching up to 4.5%. in low-income countries, CFRs remained similar between 1.5% and 3.0%. Our study emphasizes that COVID-19 CFR is not a fixed or static value. Rather, it is a dynamic estimate that changes with time, population, socioeconomic factors, and the mitigatory efforts of individual countries.


Subject(s)
COVID-19 , Asia , COVID-19/epidemiology , Europe/epidemiology , Humans , SARS-CoV-2 , Socioeconomic Factors
8.
Rev Med Virol ; : e2336, 2022 Feb 26.
Article in English | MEDLINE | ID: covidwho-1712178

ABSTRACT

The aim of this systematic review and network meta-analysis is to evaluate the comparative effectiveness of N95, surgical/medical and non-medical facemasks as personal protective equipment against respiratory virus infection. The study incorporated 35 published and unpublished randomized controlled trials and observational studies investigating specific mask effectiveness against influenza virus, SARS-CoV, MERS-CoV and SARS-CoV-2. We searched PubMed, Google Scholar and medRxiv databases for studies published up to 5 February 2021 (PROSPERO registration: CRD42020214729). The primary outcome of interest was the rate of respiratory viral infection. The quality of evidence was estimated using the GRADE approach. High compliance to mask-wearing conferred a significantly better protection (odds ratio [OR], 0.43; 95% confidence interval [CI], 0.23-0.82) than low compliance. N95 or equivalent masks were the most effective in providing protection against coronavirus infections (OR, 0.30; CI, 0.20-0.44) consistently across subgroup analyses of causative viruses and clinical settings. Evidence supporting the use of medical or surgical masks against influenza or coronavirus infections (SARS, MERS and COVID-19) was weak. Our study confirmed that the use of facemasks provides protection against respiratory viral infections in general; however, the effectiveness may vary according to the type of facemask used. Our findings encourage the use of N95 respirators or their equivalents (e.g., P2) for best personal protection in healthcare settings until more evidence on surgical and medical masks is accrued. This study highlights a substantial lack of evidence on the comparative effectiveness of mask types in community settings.

9.
J Med Internet Res ; 24(2): e33819, 2022 02 22.
Article in English | MEDLINE | ID: covidwho-1700130

ABSTRACT

The COVID-19 pandemic accelerated the uptake of digital health worldwide and highlighted many benefits of these innovations. However, it also stressed the magnitude of inequalities regarding accessing digital health. Using a scoping review, this article explores the potential benefits of digital technologies for the global population, with particular reference to people living with disabilities, using the autism community as a case study. We ultimately explore policies in Sweden, Australia, Canada, Estonia, the United Kingdom, and the United States to learn how policies can lay an inclusive foundation for digital health systems. We conclude that digital health ecosystems should be designed with health equity at the forefront to avoid deepening existing health inequalities. We call for a more sophisticated understanding of digital health literacy to better assess the readiness to adopt digital health innovations. Finally, people living with disabilities should be positioned at the center of digital health policy and innovations to ensure they are not left behind.


Subject(s)
COVID-19 , Disabled Persons , Ecosystem , Health Status Disparities , Humans , Pandemics , Policy , SARS-CoV-2 , United States
10.
Lancet Reg Health Eur ; 14: 100316, 2022 Mar.
Article in English | MEDLINE | ID: covidwho-1693140

ABSTRACT

The COVID-19 pandemic has highlighted the importance of digital health technologies and the role of effective surveillance systems. While recent events have accelerated progress towards the expansion of digital public health (DPH), there remains significant untapped potential in harnessing, leveraging, and repurposing digital technologies for public health. There is a particularly growing need for comprehensive action to prepare citizens for DPH, to regulate and effectively evaluate DPH, and adopt DPH strategies as part of health policy and services to optimise health systems improvement. As representatives of the European Public Health Association's (EUPHA) Digital Health Section, we reflect on the current state of DPH, share our understanding at the European level, and determine how the application of DPH has developed during the COVID-19 pandemic. We also discuss the opportunities, challenges, and implications of the increasing digitalisation of public health in Europe.

11.
EuropePMC; 2020.
Preprint in English | EuropePMC | ID: ppcovidwho-325026

ABSTRACT

Background: We aimed to screen clinical independent predictive factors for negative conversion of nucleic acid testing and established a predictive nomogram, so as to relieve patients' anxiety and reduce unnecessary repeated nucleic acid testing. Methods: All 70 consecutive patients with nonsevere COVID-19 pneumonia were admitted to the Fangcang shelter hospital in Wuhan from February 12 th to March 8 th , 2020. We used univariate Kaplan-Meier analysis and univariate and multivariate Cox regression to identify independent predictive factors and refit the predictive model. Area under ROC (AUR), Brier scores and calibration plots were used to assess the performance. Results: diabetes mellitus, gender and lymphocyte were deemed independent predictive factors and were incorporated into a Cox proportional hazards model. The AUR and Brier scores of the predictive model at 14 days were 0.694 [0.472;0.890] and 0.163 [0.109;0.219] in the internal validation set, respectively. Similarly, the AUR and Brier scores at 21 days were 0.779 [0.505;0.957] and 0.105 [0.042;0.175] in the internal validation set. Conclusions: By using the predictive nomogram, the clinicians could inform patients with nonsevere COVID-19 regarding a certain time to possible negative conversion, which would relieve the patients’ anxiety and reduce repeated testing.

12.
EuropePMC; 2020.
Preprint in English | EuropePMC | ID: ppcovidwho-321367

ABSTRACT

Objectives: A pneumonia associated with 2019 novel coronavirus (2019-nCoV, subsequently named SARS-CoV2) emerged worldwide since December, 2019. We aimed to describe the epidemiological characteristics of 2019 coronavirus disease (COVID-19) in Shaanxi province of China. Results: : 1. Among the 245 patients, 132 (53.9%) were males and 113 (46.1%) were females. The average age was 46.15±16.43 years, ranging from 3 to 89 years. 2. For the clinical type, 1.63% (4/245) patients were mild type , 84.90% (208/245) were moderate type, 7.76% (19/245) were severe type, 5.31% (13/245) were critical type and only 0.41% (1/245) was asymptomatic. 3. Of the 245 patients, 116 (47.35%) were input case, 114 (46.53%) were non-input case , and 15 (6.12%) were unknown exposure. 4. 48.57% (119/245) cases were family cluster , involving 42 families. The most common pattern of COVID-19 family cluster was between husband and wife or between parents and children.

13.
Build Environ ; 212: 108831, 2022 Mar 15.
Article in English | MEDLINE | ID: covidwho-1654126

ABSTRACT

In the era of Corona Virus Disease 2019 (COVID-19), inappropriate indoor ventilation may turn out to be the culprit of microbial contamination in enclosed spaces and deteriorate the environment. To collaboratively improve the thermal comfort, air quality and virus spread control effect, it was essential to have an overall understanding of different ventilation modes. Hence, this study reviewed the latest scientific literature on indoor ventilation modes and manuals of various countries, identified characteristics of different ventilation modes and evaluated effects in different application occasions, wherefore to further propose their main limitations and solutions in the epidemic era. For thermal comfort, various non-uniform ventilation modes could decrease the floor-to-ceiling temperature difference, draft rate or PPD by 60%, 80% or 33% respectively, or increase the PMV by 45%. Unsteady ventilation modes (including intermittent ventilation and pulsating ventilation) could lower PPD values by 12%-37.8%. While for air quality and virus spread control, non-uniform ventilation modes could lower the mean age of air or contaminants concentration by 28.3%-47% or 15%-47% respectively, increase the air change efficiency, contaminant removal effectiveness or protection efficiency by 6.6%-10.4%, 22.6% or 14%-50% respectively. Unsteady ventilation mode (pulsating ventilation) could reduce the peak pollutant concentration and exposure time to undesirable concentrations by 31% and 48% respectively. Non-uniform modes and unsteady modes presented better performance in thermal comfort, air quality and virus spread control, whereas relevant performance evaluation indexes were still imperfect and the application scenarios were also limited.

14.
Front Pharmacol ; 12: 779135, 2021.
Article in English | MEDLINE | ID: covidwho-1649683

ABSTRACT

Remdesivir has displayed pharmacological activity against SARS-CoV-2. However, no pharmacometabolomics (PM) or correlation analysis with pharmacokinetics (PK) was revealed. Rats were intravenously administered remdesivir, and a series of blood samples were collected before and after treatment. Comprehensive metabolomics profile and PK were investigated and quantitated simultaneously using our previous reliable HPLC-MS/MS method. Both longitudinal and transversal metabolic analyses were conducted, and the correlation between PM and PK parameters was evaluated using Pearson's correlation analysis and the PLS model. Multivariate statistical analysis was employed for discovering candidate biomarkers which predicted drug exposure or toxicity of remdesivir. The prominent metabolic profile variation was observed between pre- and posttreatment, and significant changes were found in 65 metabolites. A total of 15 metabolites-12 carnitines, one N-acetyl-D-glucosamine, one allantoin, and one corticosterone-were significantly correlated with the concentration of Nuc (active metabolite of remdesivir). Adenosine, spermine, guanosine, sn-glycero-3-phosphocholine, and l-homoserine may be considered potential biomarkers for predicting drug exposure or toxicity. This study is the first attempt to apply PM and PK to study remdesivir response/toxicity, and the identified candidate biomarkers might be used to predict the AUC and Cmax, indicating capability of discriminating good or poor responders. Currently, this study originally offers considerable evidence to metabolite reprogramming of remdesivir and sheds light on precision therapy development in fighting COVID-19.

15.
J Gen Intern Med ; 37(5): 1218-1225, 2022 Apr.
Article in English | MEDLINE | ID: covidwho-1649390

ABSTRACT

BACKGROUND: The long-term prevalence and risk factors for post-acute COVID-19 sequelae (PASC) are not well described and may have important implications for unvaccinated populations and policy makers. OBJECTIVE: To assess health status, persistent symptoms, and effort tolerance approximately 1 year after COVID-19 infection DESIGN: Retrospective observational cohort study using surveys and clinical data PARTICIPANTS: Survey respondents who were survivors of acute COVID-19 infection requiring Emergency Department presentation or hospitalization between March 3 and May 15, 2020. MAIN MEASURE(S): Self-reported health status, persistent symptoms, and effort tolerance KEY RESULTS: The 530 respondents (median time between hospital presentation and survey 332 days [IQR 325-344]) had mean age 59.2±16.3 years, 44.5% were female and 70.8% were non-White. Of these, 41.5% reported worse health compared to a year prior, 44.2% reported persistent symptoms, 36.2% reported limitations in lifting/carrying groceries, 35.5% reported limitations climbing one flight of stairs, 38.1% reported limitations bending/kneeling/stooping, and 22.1% reported limitations walking one block. Even those without high-risk comorbid conditions and those seen only in the Emergency Department (but not hospitalized) experienced significant deterioration in health, persistent symptoms, and limitations in effort tolerance. Women (adjusted relative risk ratio [aRRR] 1.26, 95% CI 1.01-1.56), those requiring mechanical ventilation (aRRR 1.48, 1.02-2.14), and people with HIV (aRRR 1.75, 1.14-2.69) were significantly more likely to report persistent symptoms. Age and other risk factors for more severe COVID-19 illness were not associated with increased risk of PASC. CONCLUSIONS: PASC may be extraordinarily common 1 year after COVID-19, and these symptoms are sufficiently severe to impact the daily exercise tolerance of patients. PASC symptoms are broadly distributed, are not limited to one specific patient group, and appear to be unrelated to age. These data have implications for vaccine hesitant individuals, policy makers, and physicians managing the emerging longer-term yet unknown impact of the COVID-19 pandemic.


Subject(s)
COVID-19 , Adult , Aged , COVID-19/epidemiology , Female , Health Status , Humans , Middle Aged , Pandemics , Retrospective Studies , SARS-CoV-2
16.
Int J Health Plann Manage ; 37(3): 1205-1220, 2022 May.
Article in English | MEDLINE | ID: covidwho-1640712

ABSTRACT

Eight versions of the Protocol on Prevention and Control of Coronavirus Disease 2019 (COVID-19) (the Protocol) were issued successively by the Chinese authority to guide the local responses since the first COVID-19 case appeared in Wuhan, China. This study aimed to investigate the evolution of the overall strategy and specific measures in these Protocols, and several recommendations were provided after analysing China's response to the epidemic resurgence. As a result, we found a gradual expanding trend in case surveillance, early screening, and epidemiological investigation, as well as a progressively rigorous tendency in isolation measures and close contact management. With the Protocol's guidance, China had achieved success in several recent fights against domestic COVID-19 resurgences. The city lockdown and multiple city-wide nucleic acid tests adopted were deemed necessary in COVID-19 resurgence's battle. Besides, the large-scale distance centralised quarantine, which is, quarantine in a purpose-built isolation station away from communities where people under quarantine lived, was promoted in rural areas. China's anti-epidemic achievements provide ideas for the global battle against COVID-19.


Subject(s)
COVID-19 , Epidemics , COVID-19/prevention & control , China/epidemiology , Communicable Disease Control , Epidemics/prevention & control , Humans , Quarantine
17.
Land ; 10(11):1206, 2021.
Article in English | ProQuest Central | ID: covidwho-1534139

ABSTRACT

Cropland abandonment occurs frequently in many countries and regions around the world, particularly in those with poor environmental conditions, such as mountainous regions. In Chongqing county, China, over 76% of the total area is mountainous. Due to the lack of reliable remote sensing monitoring and identification methods, the spatial and temporal distribution of abandoned cropland areas and its underlying causes are poorly understood. Thus, the extent of cropland abandonment in Chongqing, since 2001, was estimated using land use trajectories. The following results were obtained: (1) the cropland abandonment rate was 12.2–15.4% from 2001 to 2020, with an average of 13.3%;(2) hotspots of abandoned cropland were concentrated in the north and southeast. Cropland abandonment was clustered in the northern, southeastern, and southwestern areas;(3) socio-economic factors (including gross domestic product density, population density, and road density) had a greater impact on the spatial distribution of abandoned cropland than environmental factors. Based on the results, the government should strive to reduce production costs associated with poor agricultural infrastructure, sporadic cropland, and higher labor costs by providing grain subsidies, undertaking cropland consolidation, encouraging land transfer, and improving agricultural infrastructure.

18.
Image Vis Comput ; 117: 104341, 2022 Jan.
Article in English | MEDLINE | ID: covidwho-1531481

ABSTRACT

Coronavirus disease 2019 (COVID-19) is a world-wide epidemic and efficient prevention and control of this disease has become the focus of global scientific communities. In this paper, a novel face mask detection framework FMD-Yolo is proposed to monitor whether people wear masks in a right way in public, which is an effective way to block the virus transmission. In particular, the feature extractor employs Im-Res2Net-101 which combines Res2Net module and deep residual network, where utilization of hierarchical convolutional structure, deformable convolution and non-local mechanisms enables thorough information extraction from the input. Afterwards, an enhanced path aggregation network En-PAN is applied for feature fusion, where high-level semantic information and low-level details are sufficiently merged so that the model robustness and generalization ability can be enhanced. Moreover, localization loss is designed and adopted in model training phase, and Matrix NMS method is used in the inference stage to improve the detection efficiency and accuracy. Benchmark evaluation is performed on two public databases with the results compared with other eight state-of-the-art detection algorithms. At IoU = 0.5 level, proposed FMD-Yolo has achieved the best precision AP50 of 92.0% and 88.4% on the two datasets, and AP75 at IoU = 0.75 has improved 5.5% and 3.9% respectively compared with the second one, which demonstrates the superiority of FMD-Yolo in face mask detection with both theoretical values and practical significance.

20.
Clin Infect Dis ; 73(10): 1831-1839, 2021 11 16.
Article in English | MEDLINE | ID: covidwho-1522142

ABSTRACT

BACKGROUND: Monitoring of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) antibody prevalence can complement case reporting to inform more accurate estimates of SARS-CoV-2 infection burden, but few studies have undertaken repeated sampling over time on a broad geographic scale. METHODS: We performed serologic testing on a convenience sample of residual serum obtained from persons of all ages, at 10 sites in the United States from 23 March through 14 August 2020, from routine clinical testing at commercial laboratories. We standardized our seroprevalence rates by age and sex, using census population projections and adjusted for laboratory assay performance. Confidence intervals were generated with a 2-stage bootstrap. We used bayesian modeling to test whether seroprevalence changes over time were statistically significant. RESULTS: Seroprevalence remained below 10% at all sites except New York and Florida, where it reached 23.2% and 13.3%, respectively. Statistically significant increases in seroprevalence followed peaks in reported cases in New York, South Florida, Utah, Missouri, and Louisiana. In the absence of such peaks, some significant decreases were observed over time in New York, Missouri, Utah, and Western Washington. The estimated cumulative number of infections with detectable antibody response continued to exceed reported cases in all sites. CONCLUSIONS: Estimated seroprevalence was low in most sites, indicating that most people in the United States had not been infected with SARS-CoV-2 as of July 2020. The majority of infections are likely not reported. Decreases in seroprevalence may be related to changes in healthcare-seeking behavior, or evidence of waning of detectable anti-SARS-CoV-2 antibody levels at the population level. Thus, seroprevalence estimates may underestimate the cumulative incidence of infection.


Subject(s)
COVID-19 , SARS-CoV-2 , Antibodies, Viral , Bayes Theorem , Child , Humans , Seroepidemiologic Studies , United States/epidemiology , Utah
SELECTION OF CITATIONS
SEARCH DETAIL