ABSTRACT
This paper proposes a new epidemiological mathematical model based on the dynamics of urban public epidemic prevention and control model. Then, the nonlinear differential equation of epidemic propagation dynamics is deduced. Secondly, this paper uses the exponential equation to fit the curve, takes three days as the optimal window time, and estimates the turning point of the urban public epidemic. Again, this paper establishes a dynamic model of dynamic experience transfer. Finally, this paper uses the COVID19 example to verify the public epidemic prevention and control problems described in the text. Experimental simulations show that the algorithm can better grasp important epidemiological dynamics.
ABSTRACT
Introduction: The underlying pathophysiology of Post-COVID-19 syndrome remains unknown, but increased cardiometabolic demand and state of mitochondrial dysfunction have emerged as candidate mechanisms. Cardiovascular magnetic resonance (CMR) provides insight into pathophysiological mechanisms underlying cardiovascular disease and 31-phosphorus magnetic resonance spectroscopy (31P-MRS) allows noninvasive assessment of the myocardial energetic state. Purpose(s): We sought to assess whether Post-COVID-19 syndrome is associated with abnormalities of myocardial structure, function, perfusion and tissue characteristics or energetic derangement. Method(s): Prospective case-control study. A total of 20 patients with a clinical diagnosis of Post-COVID-19 syndrome (seropositive) and no prior underlying cardiovascular disease (CVD) and ten matching controls underwent 31P-MRS and CMR at 3T at a single time point. (Figure 1) All patients had been symptomatic with acute COVID-19, but none required hospital admission. Result(s): Between the Post-COVID-19 syndrome patients and matched contemporary controls there were no differences in myocardial energetics (phosphocreatine to ATP ratio), in cardiac structure (biventricular volumes, left ventricular mass), function (biventricular ejection fractions, global longitudinal strain), tissue characterization (T1 and extracellular volume [ECV] fraction mapping, late gadolinium enhancement) or perfusion (myocardial rest and stress blood flow, myocardial perfusion reserve). One patient with Post-COVID-19 syndrome showed subepicardial hyperenhancement on the late gadolinium enhancement imaging compatible with prior myocarditis, but no accompanying abnormality in cardiac size, function, perfusion, ECV, T1, T2 mapping or energetics. This patient was excluded from statistical analyses. (Table 1) Conclusion(s): In this study, the overwhelming majority of patients with a clinical Post-COVID-19 syndrome with no prior CVD did not exhibit any abnormalities in myocardial energetics, structure, function, blood flow or tissue characteristics.
ABSTRACT
In response to the COVID-19 epidemic, many education systems have relied on distance learning and educational technologies to an unprecedented degree. However, rigorous empirical research on the impacts on learning under these conditions is still scarce. We present the first large-scale, quantitative evidence detailing how school closures affected education in China. The data set includes households and teachers of 4,360 rural and urban primary school students. We find that although the majority of students engaged in distance education, many households encountered difficulties including barriers to learning (such as access to appropriate digital devices and study spaces), curricular delays, and costs to parents equivalent to between 3.5 and 6 months of income. We also find significant disparities across rural and urban households.
ABSTRACT
Patients with advanced chronic kidney disease (CKD) stage 4-5 face unknown progression rates to End Stage Renal Disease (ESRD) with elevated baseline mortality. Hemodialysis preparation requires surgical planning months in advance, and many patients may pass away before reaching ESRD. Improved understanding of survival probability in the near future could help physicians and patients in the shared decision making on the risks and benefits of dialysis vs conservative care. Patients from Kaiser Southern California Electronic Health Record (EHR) with CKD Stage 4-5 between 1/1/2010 – 12/31/2018 were selected for our initial training population. We picked an XGBoost model as it offered the best combination of accuracy and interpretability. Our features included aggregations of demographics, comorbidities calculated based on the Elixhauser comorbidity index, common labs, vitals, and past utilization data. On March 10, 2020, 16,267 current Stage 4-5 CKD patients at Kaiser Southern California were scored with the model . From March 11, 2020 to March 10, 2021, a 1-year prospective study was performed to assess the accuracy of the predictive model. At the conclusion of the 1-year observation, we assessed the model’s predictions against the actual survival data. The machine learning survival model achieved an AUC of .73 in the prospective study. We computed an optimal cut-point based on the probability prediction threshold that maximized the sum of sensitivity and specificity. At this level, the model achieved an accuracy of 70%, sensitivity of 63%, specificity of 72%, and precision of 25%, in predicting 12 month survival for individuals with advanced CKD stage 4-5. Despite unforeseen COVID-19 pandemic, our model achieved predictive accuracy for 1-year survival in CKD stage 4-5 patients prospectively. Machine learning based probabilistic forecasting can be used to better inform decisions regarding CKD management.
ABSTRACT
Ensifentrine (RPL554) is an investigational, first-in-class, inhaled dual inhibitor of PDE 3 and 4 with bronchodilator and anti-inflammatory actions in a single compound administered via nebulizer. This reports on the results from a phase 1, randomized, double-blind, placebo- and positivecontrolled, 4-way crossover thorough QT (TQT) study in healthy individuals. Thirty-two male and female subjects were randomized to receive a single dose of blinded ensifentrine 3mg (therapeutic dose), 9 mg (supra-therapeutic dose), and placebo via standard jet nebulizer and a single dose of oral moxifloxacin (400 mcg, open-label) in 4 separate treatment periods. 12-lead electrocardiograms (ECGs) were extracted from continuous Holter recordings pre-dose and at 0.5, 1, 1.5, 2, 2.5, 3, 4, 6, 8, 10, 12, and 24 hours post-dose. ECG parameters were measured by a blinded central ECG laboratory. The primary statistical method was concentration-QTc analysis with the placebo-corrected change-from-baseline QTcF (ΔΔQTcF) as the primary endpoint. No clinically relevant effect on ΔΔQTcF was observed with either the 3mg or 9mg dose of ensifentrine. Using concentration-QTc analysis, the effect on ΔΔQTcF at the geometric mean estimated Cmax of 3 mg (546 pg/mL) and 9 mg (2342 pg/mL) ensifentrine indicated 1.4 ms (90% CI: 0.4 to 2.4) and 3.7 ms (90% CI: 0.7 to 6.7) increases vs placebo, respectively. The by-timepoint analysis showed that all upper bounds of the 90% CIs of ΔΔQTcF were below 10 ms with both doses across all timepoints. The lower bound of the 90% CI of the predicted QTc effect at the moxifloxacin mean Cmax was above 5 ms, thereby demonstrating assay sensitivity. Ensifentrine did not have a clinically meaningful effect on heart rate, PR or QRS intervals with either dose. In healthy individuals, both doses of ensifentrine were well tolerated. There were 14 (14.3%) reported AEs, including 13 treatment-emergent AEs (TEAE) of which 1 TEAE was serious (syncope) approximately 2 days post-dose in a subject who was hospitalized following the syncope event and physical injuries from a subsequent fall. The subject was positive for COVID-19 (moderate AE) at the time of hospitalization for the SAE event, and this was the only TEAE that led to study discontinuation. All AEs were mild except for syncope (severe) and COVID-19 (moderate). There were no deaths. AEs (23.3%) were more frequently reported in the ensifentrine-9mg arm. In summary, ensifentrine at the studied doses had no clinically relevant effects on studied ECG parameters.
ABSTRACT
Heterogeneity and irregularity of multi-source data sets present a significant challenge to time-series analysis. In the literature, the fusion of multi-source time-series has been achieved either by using ensemble learning models which ignore temporal patterns and correlation within features or by defining a fixed-size window to select specific parts of the data sets. On the other hand, many studies have shown major improvement to handle the irregularity of time-series, yet none of these studies has been applied to multi-source data. In this work, we design a novel architecture, PIETS, to model heterogeneous time-series. PIETS has the following characteristics: (1) irregularity encoders for multi-source samples that can leverage all available information and accelerate the convergence of the model;(2) parallelised neural networks to enable flexibility and avoid information over-whelming;and (3) attention mechanism that highlights different information and gives high importance to the most related data. Through extensive experiments on real-world data sets related to COVID-19, we show that the proposed architecture is able to effectively model heterogeneous temporal data and outperforms other state-of-the-art approaches in the prediction task.
ABSTRACT
Because there are many types of accommodation facilities in Jiuzhen Mountain Tourist Resort in Wuhan, the evaluation indexes that affect the accommodation output benefit in Jiuzhen Mountain Tourist Resort, including quality grade, brand effect, government policy, market supply and demand relationship, business subject and consumer habits, should be comprehensively considered. In this paper, the fuzzy comprehensive evaluation method is used to evaluate the outgoing benefits of accommodation products in Jiuzhen Mountain Tourist Resort. The results show that the hotel products within the geographical area have high output efficiency, but there is still the problem of uneven development, so the hotel resources should be optimally allocated, and the service and management should be improved to boost the overall efficiency of the tourism resort accommodation industry. © 2021 ACM.
ABSTRACT
This study documents the COVID-19 disease-control measures enacted in rural China and examines the economic and social impacts of these measures. We conducted two rounds of surveys with 726 randomly selected village informants across seven provinces. Strict disease-control measures have been universally enforced and appear to have been successful in limiting disease transmission in rural communities. The infection rate in our sample was 0.001 per cent, a rate that is near the national average outside of Hubei province. None of the villages reported any COVID-19-related deaths. For a full month during the quarantine, the rate of employment of rural workers was essentially zero. Even after the quarantine measures were lifted, nearly 70 per cent of the villagers still were unable to work owing to workplace closures. Although action has been taken to mitigate the potential negative effects, these disease-control measures might have accelerated the inequality between rural and urban households in China.
ABSTRACT
The usage of smartphone-collected respiratory sound, trained with deep learning models, for detecting and classifying COVID-19 becomes popular recently. It removes the need for in-person testing procedures especially for rural regions where related medical supplies, experienced workers, and equipment are limited. However, existing sound-based diagnostic approaches are trained in a fully-supervised manner, which requires large scale well-labelled data. It is critical to discover new methods to leverage unlabelled respiratory data, which can be obtained more easily. In this paper, we propose a novel self-supervised learning enabled framework for COVID-19 cough classification. A contrastive pre-training phase is introduced to train a Transformer-based feature encoder with unlabelled data. Specifically, we design a random masking mechanism to learn robust representations of respiratory sounds. The pre-trained feature encoder is then fine-tuned in the downstream phase to perform cough classification. In addition, different ensembles with varied random masking rates are also explored in the downstream phase. Through extensive evaluations, we demonstrate that the proposed contrastive pre-training, the random masking mechanism, and the ensemble architecture contribute to improving cough classification performance.
ABSTRACT
The outbreak of the COVID-19 event has been a major international concern since the first case was discovered in December 2019. After mid- to- late February 2020, the daily number of newly diagnosed cases abroad has increased rapidly, showing the characteristics of a pandemic disease. Under the deep impact of the COVID-19 event, the international relations are intricate and ever-changing. The instability and uncertainty of international relations have increased dramatically and have brought profound changes to the economy, security, and diplomacy. A comprehensive and timely analysis of "Global-China" international relations and its changing characteristics has important reference value for China's diplomatic development planning. Complex international relations can be split up into a series of event units. News data contains key information such as time, location, people, things, etc. It is the most direct and comprehensive source of information for constructing events. The GDELT ( Global Database of Events, Language, and Tone ) is a free and open news database which monitors news from print, broadcast, and online media in the world then analyzes the texts and extracts the key information such as people, location, organization, and event. From the perspective of "Global-China", this paper takes GDELT as the data source and uses global news data about the COVID-19 event from January to May 2020 to analyze the changes in international relations. First of all, the characteristics of international relations, such as intensity, similarity and polarity, are consistent with emotions. According to Plutchik's wheel of emotions, this paper provides a representation and calculation model of international relations to solve the problem of ambiguity in representation and the difficulty in calculation, using key variables including the number of events, the intensity of events, and the number of mentioned events. Then, the features of the changes in international relations are obviously displayed from the perspective of spatio-temporal visualization. Finally, this paper analyzes the causes of changes in international relations by important international events during the COVID-19 event. The results show that the analysis method can accurately reveal the development degree of the "Global-China" international relations during the COVID-19 event and find out the rules and causes of changes and has important application value. This paper can provide a new perspective for the exploration of international relations and a reference for the analysis of news data in the era of big data. And it shows the great potential and broad prospect of the research on international relations of big data. 2021, Science Press. All right reserved.
ABSTRACT
In 2020, China was also severely affected by the global spread of the new coronavirus (2019-nCoV). In the midst of the epidemic, colleges and universities are turning to online teaching. With the phased success of the epidemic, the spirit of "epidemic power"is an important part of the curriculum construction in colleges and universities. Computer-related majors have the identity of social service and are consistent in social practice. In higher vocational colleges, the curriculum construction of computer-related majors is also an important content, to improve students'study enthusiasm, to strengthen the construction of teachers'team, to improve teachers'professional ability, to give full play to their professional advantages, to put curriculum construction in the first task of teaching. © 2021 ACM.
ABSTRACT
At the end of 2019, a new type of coronavirus (COVID-19) rapidly spread globally, even if the penetration of vaccination is getting higher and higher, the emergence of viral variants has increased the number of new coronal pneumonia infections. The deep learning model can help doctors quickly and accurately divide the lesion zone. However, there are many problems in the segmentation of the slice from the CT slice, including the problem of uncertainty of the disease area, low accuracy. At the same time, the semantic segmentation model of the traditional CNN architecture has natural defects, and the sensing field restrictions result in constructing the relationship between pixels and pixels, and the context information is insufficient. In order to solve the above problems, we introduced a Transformer module. Visual Transformer has been proved to effectively improve the accuracy of the model. We have designed a plug-and-play spatial attention module, on the basis of attention, increased positional offset, effective aggregate advanced features, and improve the accuracy of existing models. © 2021 Institute of Physics Publishing. All rights reserved.
ABSTRACT
Objective: To explore the mechanism of Kangguan decoction in the treatment of coronavirus disease 2019 (COVID-19) and then perform preliminary verification. Methods: The effective compounds and target genes of Kangguan decoction were obtained from Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform. COVID-19 related target genes were searched in the GeneCards database. The active target genes of Kangguan decoction acting on COVID-19 were identified to perform GO function enrichment and KEGG pathway enrichment analysis. The compound-target network and protein-protein interaction were constructed;Molecular docking simulations of macromolecular protein target receptors and their corresponding compounds were performed. The clinical data of COVID-19 patients were retrieved from their electronic medical records of Nantong Third People's Hospital. Results: We screened out 137 effective compounds and 274 effective target genes of Kangguan decoction from TCMSP. The active target genes of Kangguan decoction were compared with the COVID-19 related target genes, and 63 active target genes for Kangguan decoction acting on COVID-19 were identified. GO function enrichment and KEGG pathway enrichment analysis were performed. The compound-target network and PPI network were constructed and the key compounds and key targets were selected to construct a key compound-target network. Finally, the binding of the target and its corresponding components was verified by molecular docking and two clinical cases with obvious clinical efficacy after Kangguan decoction application were demonstrated. Conclusion: The pharmacological mechanism of Kangguan decoction acting on COVID-19 has been explored, and the active compounds and targets of Kangguan decoction acting on COVID-19 and clinical efficacy for Kangguan decoction treating COVID-19 patients have been preliminarily verified.
ABSTRACT
AIM: To retrospectively evaluate the interobserver variability of intensive care unit (ICU) practitioners and radiologists who used the M-BLUE (modified bedside lung ultrasound in emergency) protocol to assess coronavirus disease-19 (COVID-19) patients, and to determine the correlation between total M-BLUE protocol score and three different scoring systems reflecting disease severity. MATERIALS AND METHODS: Institutional review board approval was obtained and informed consent was not required. Ninety-six lung ultrasonography (LUS) examinations were performed using the M-BLUE protocol in 79 consecutive COVID-19 patients. Two ICU practitioners and three radiologists reviewed video clips of the LUS of eight different regions in each lung retrospectively. Each observer, who was blind to the patient information, described each clip with M-BLUE terminology and assigned a corresponding score. Interobserver variability was assessed using intraclass correlation coefficient. Spearman's correlation coefficient analysis (R-value) was used to assess the correlation between the total score of the eight video clips and disease severity. RESULTS: For different LUS signs, fair to good agreement was obtained (ICC = 0.601, 0.339, 0.334, and 0.557 for 0-3 points respectively). The overall interobserver variability was good for both the five different readers and consensus opinions (ICC = 0.618 and 0.607, respectively). There were good correlations between total LUS score and scores from three systems reflecting disease severity (R=0.394-0.660, p<0.01). CONCLUSION: In conclusion, interobserver agreement for different signs and total scores in LUS is good and justifies its use in patients with COVID-19. The total scores of LUS are useful to indicate disease severity.
Subject(s)
COVID-19/diagnostic imaging , Clinical Protocols , Critical Care/methods , Lung/diagnostic imaging , Observer Variation , Point-of-Care Testing , Severity of Illness Index , Adult , Aged , Aged, 80 and over , Female , Humans , Male , Middle Aged , Retrospective Studies , SARS-CoV-2 , Ultrasonography , Young AdultABSTRACT
Objective: To investigate the death time of patients with coronavirus disease 2019 (COVID-19). Methods: The death time was calculated and analyzed using individual data and aggregated data through the daily notification of the epidemic situation and the death cases published on the website of the Heath Commission of China and provinces. Results: In the 153 patients who died of COVID-19, the shortest time from onset to death was 4 days and the longest time was 50 days with the mean±standard deviation of (16.7±9.2) days. The median was 14 days and the 95% confidence interval was 4.6-42.9. The shortest time from admission to death was 1 day and the longest time was 50 days with the mean ± standard deviation of (12.1±7.8) days. The median was 11 days and the 95% confidence interval was 2-32.8. The time curve from diagnosis to death was skewed. The death time from diagnosis to death was 0 to 48 days with the mean ± standard deviation of (11.1±8.9) days. The median was 9 days, the interquartile interval was 10.5 days, and the 95% confidence interval was 0-35.4. It took 3 days from onset to admission and 1 day from admission to diagnosis. Aggregated data showed that the time from diagnosis to death of COVID-19 patients in China, China (except Hubei Province), Hubei Province and Wuhan City was 8, 9, 6 and 6 days, respectively. Conclusion: The time from diagnosis to death of COVID-19 patients varied significantly, with the median time of 6-9 days in different regions.