The paper deals with two exact mathematical models of study: A borrowed model with constant intensity of study and amodel with variable intensity of study derived by the authors are presented using data acquired in real pedagogical research. Thebounding of study performance is proved and precisely determined in dependence on personal (group) parameters of study andintensity of forgetting. The parameter of forgetting has decisive influence on total performance.

Mathematical Modelling of Study

Patients with rheumatic diseases are often more susceptible to different bacteria and viruses because of immune impairment, but it is not clear whether there is a higher risk of infection and a more serious course of disease for novel coronavirus (SARS-CoV-2). We performed this systematic review and meta analysis to assess the risk and clinical outcomes of COVID-19 in patients with rheumatic diseases compared with the general population. We searched PubMed, EMBASE, Scopus and Web of Science databases from January 1, 2020 to October 20, 2020 to determine epidemiological information related to patients with rheumatic diseases and COVID-19, including clear risk estimate or data that could be converted and extracted. We included 26 observational studies, totaling about 2000 patients with rheumatic diseases of whom were infected with COVID-19. Meta-analysis showed that the risk of COVID-19 infection in rheumatic patients was significantly higher than that in the general population (OR = 1.53, 95% CI 1.24-1.88, P = 0.000). In terms of hospitalization and severe clinical outcomes associated with COVID-19, we found that rheumatic patients showed similar results to the reference population (hospitalization OR = 1.36, 95% CI 0.81-2.29, P = 0.247; admitted to ICU OR = 1.94, 95% CI 0.88-4.27, P = 0.098; death OR = 1.29, 95% CI 0.84-1.97, P = 0.248). The presence of comorbidities, hypertension, lung diseases were significantly associated with the increased risk of COVID-19-related hospitalization in rheumatic patients and anti-TNF drugs were associated with lower hospitalization risk. Older age was related to severe COVID-19. Our meta-analysis indicated that rheumatic patients were at a higher risk of COVID-19 infection but might not lead to a more serious disease process.

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Risk and clinical outcomes of COVID-19 in patients with rheumatic diseases compared with the general population: a systematic review and meta-analysis.

COVID-19 has sparked a worldwide pandemic, with the number of infected cases and deaths rising on a regular basis. Along with recent advances in soft computing technology, researchers are now actively developing and enhancing different mathematical and machine-learning algorithms to forecast the future trend of this pandemic. Thus, if we can accurately forecast the trend of cases globally, the spread of the pandemic can be controlled. In this study, a hybrid CNN-LSTM model was developed on a time-series dataset to forecast the number of confirmed cases of COVID-19. The proposed model was evaluated and compared with 17 baseline models on test and forecast data. The primary finding of this research is that the proposed CNN-LSTM model outperformed them all, with the lowest average MAPE, RMSE, and RRMSE values on both test and forecast data. Conclusively, our experimental results show that, while standalone CNN and LSTM models provide acceptable and efficient forecasting performance for the confirmed COVID-19 cases time series, combining both models in the proposed CNN-LSTM encoder-decoder structure provides a significant boost in forecasting performance. Furthermore, we demonstrated that the suggested model produced satisfactory predicting results even with a small amount of data.

/pdf/covid-19-pandemic-forecasting-using-cnn-lstm-a-hybrid-4ru5fqlpq7.pdf

COVID-19 Pandemic Forecasting Using CNN-LSTM: A Hybrid Approach

Prediction of complex epidemiological systems such as COVID-19 is challenging on many grounds. Commonly used compartmental models struggle to handle an epidemiological process that evolves rapidly and is spatially heterogeneous. On the other hand, machine learning methods are limited at the beginning of the pandemics due to small data size for training. We propose a deep learning approach to predict future COVID-19 infection cases and deaths 1 to 4 weeks ahead at the fine granularity of US counties. The multi-variate Long Short-term Memory (LSTM) recurrent neural network is trained on multiple time series samples at the same time, including a mobility series. Results show that adding mobility as a variable and using multiple samples to train the network improve predictive performance both in terms of bias and of variance of the forecasts. We also show that the predicted results have similar accuracy and spatial patterns with a standard ensemble model used as benchmark. The model is attractive in many respects, including the fine geographic granularity of predictions and great predictive performance several weeks ahead. Furthermore, data requirement and computational intensity are reduced by substituting a single model to multiple models folded in an ensemble model.

Spatio-temporal prediction of the COVID-19 pandemic in US counties: modeling with a deep LSTM neural network.

http://www.ejradiology.com/article/S0720048X22000146/pdf

Long-COVID diagnosis: From diagnostic to advanced AI-driven models

The current outbreak of coronavirus disease 2019 (COVID-19) has recently been declared as a pandemic and spread over 200 countries and territories. Forecasting the long-term trend of the COVID-19 epidemic can help health authorities determine the transmission characteristics of the virus and take appropriate prevention and control strategies beforehand. Previous studies that solely applied traditional epidemic models or machine learning models were subject to underfitting or overfitting problems. We propose a new model named Dynamic-Susceptible-Exposed-Infective-Quarantined (D-SEIQ), by making appropriate modifications of the Susceptible-Exposed-Infective-Recovered (SEIR) model and integrating machine learning based parameter optimization under epidemiological rational constraints. We used the model to predict the long-term reported cumulative numbers of COVID-19 cases in China from January 27, 2020. We evaluated our model on officially reported confirmed cases from three different regions in China, and the results proved the effectiveness of our model in terms of simulating and predicting the trend of the COVID-19 outbreak. In China-Excluding-Hubei area within 7 days after the first public report, our model successfully and accurately predicted the long trend up to 40 days and the exact date of the outbreak peak. The predicted cumulative number (12,506) by March 10, 2020, was only 3·8% different from the actual number (13,005). The parameters obtained by our model proved the effectiveness of prevention and intervention strategies on epidemic control in China. The prediction results for five other countries suggested the external validity of our model. The integrated approach of epidemic and machine learning models could accurately forecast the long-term trend of the COVID-19 outbreak. The model parameters also provided insights into the analysis of COVID-19 transmission and the effectiveness of interventions in China.

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Forecasting the long-term trend of COVID-19 epidemic using a dynamic model.

Graph-level representations are critical in various real-world applications, such as predicting the properties of molecules. But in practice, precise graph annotations are generally very expensive and time-consuming. To address this issue, graph contrastive learning constructs instance discrimination task which pulls together positive pairs (augmentation pairs of the same graph) and pushes away negative pairs (augmentation pairs of different graphs) for unsupervised representation learning. However, since for a query, its negatives are uniformly sampled from all graphs, existing methods suffer from the critical sampling bias issue, i.e., the negatives likely having the same semantic structure with the query, leading to performance degradation. To mitigate this sampling bias issue, in this paper, we propose a Prototypical Graph Contrastive Learning (PGCL) approach. Specifically, PGCL models the underlying semantic structure of the graph data via clustering semantically similar graphs into the same group, and simultaneously encourages the clustering consistency for different augmentations of the same graph. Then given a query, it performs negative sampling via drawing the graphs from those clusters that differ from the cluster of query, which ensures the semantic difference between query and its negative samples. Moreover, for a query, PGCL further reweights its negative samples based on the distance between their prototypes (cluster centroids) and the query prototype such that those negatives having moderate prototype distance enjoy relatively large weights. This reweighting strategy is proved to be more effective than uniform sampling. Experimental results on various graph benchmarks testify the advantages of our PGCL over state-of-the-art methods.

Prototypical Graph Contrastive Learning.

Objectives: This study firstly aimed to explore predicting cognitive impairment at an early stage using a large population-based longitudinal survey of elderly Chinese people. The second aim was to identify reversible factors which may help slow the rate of decline in cognitive function over 3 years in the community. Methods: We included 12,280 elderly people from four waves of the Chinese Longitudinal Healthy Longevity Survey (CLHLS), followed from 2002 to 2014. The Chinese version of the Mini-Mental State Examination (MMSE) was used to examine cognitive function. Six machine learning algorithms (including a neural network model) and an ensemble method were trained on data split 2/3 for training and 1/3 testing. Parameters were explored in training data using 3-fold cross-validation and models were evaluated in test data. The model performance was measured by area-under-curve (AUC), sensitivity, and specificity. In addition, due to its better interpretability, logistic regression (LR) was used to assess the association of life behavior and its change with cognitive impairment after 3 years. Results: Support vector machine and multi-layer perceptron were found to be the best performing algorithms with AUC of 0.8267 and 0.8256, respectively. Fusing the results of all six single models further improves the AUC to 0.8269. Playing more Mahjong or cards (OR = 0.49,95% CI: 0.38–0.64), doing more garden works (OR = 0.54,95% CI: 0.43–0.68), watching TV or listening to the radio more (OR = 0.67,95% CI: 0.59–0.77) were associated with decreased risk of cognitive impairment after 3 years. Conclusions: Machine learning algorithms especially the SVM, and the ensemble model can be leveraged to identify the elderly at risk of cognitive impairment. Doing more leisure activities, doing more gardening work, and engaging in more activities combined were associated with decreased risk of cognitive impairment.

/pdf/using-machine-learning-algorithms-for-predicting-cognitive-28xda6vf.pdf

Shuojia Wang

Papers

Forecasting the long-term trend of COVID-19 epidemic using a dynamic model.

Prototypical Graph Contrastive Learning.

Using machine learning algorithms for predicting cognitive impairment and identifying modifiable factors among Chinese elderly people