Prediction and Analysis of COVID-19 Epidemic Based on Improved GEP Algorithm to Optimize SEIR Mode

In 2019, COVID-19 (CoronaVirus Disease 2019) broke out all over the world. COVID-19 is an infectious disease, which has a huge impact on the global economy. It is very difficult to prevent and control the epidemic situation of this infectious disease. At present, many SEIR(Susceptible Exposed Infected Recovered)models are used to predict the number of infectious diseases, which has the shortcomings of low prediction accuracy and inaccurate inflection point prediction. Therefore, this paper proposes that the prediction and analysis of COVID-19 based on improved GEP algorithm and optimized SEIR model can improve the prediction accuracy and inflection point prediction accuracy, and provide a theoretical basis for epidemic prevention of large-scale infectious diseases in the future. The algorithm. First, establish SEIR (Susceptible Exposed Infected Recovered) model to analyze the epidemic trend, and then use improved GEP (Gene Expression Programming) algorithm to analyze the infection coefficient of SEIR model beta And coefficient of restitution y, perform parameter estimation to optimize the initial value I and recovery coefficient of the infected population y and so on to improve the accuracy of model prediction. The experimental data take the number of COVID-19 infected people in the United States, China, the United Kingdom and Italy as examples. The results show that the SEIR model optimized based on the improved GEP algorithm conforms to the inflection point of the actual data, and the average error value is 1.32%. The algorithm provides a theoretical basis for the future epidemic prevention. © 2022 IEEE.

Prediction and Analysis of Infectious Disease Visit Data Based on DPC

Since the outbreak of the COVID-19 in early 2020, the prevention and control of infectious diseases has been raised to a higher level. However, tuberculosis still ranks in the forefront of the incidence rate of various infectious diseases in China. The tuberculosis epidemic has also brought great economic pressure and negative social impact to the society every year. Therefore, we have always been very concerned about how to effectively prevent and control the spread of tuberculosis. However, the diagnostic data of tuberculosis are often high-dimensional, huge, messy and difficult to be used effectively. How to extract knowledge from the data to help medical staff find the incidence trend of tuberculosis to assist decision-making has become a practical topic. In this paper, after clarifying and standardizing the original data, the density peak clustering (DPC) algorithm is used for deep mining. The knowledge is extracted through clustering analysis and visualization. Finally, analysis results can intuitively illustrate the effectiveness and practical research significance of this work. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

SA-FEM: Combined Feature Selection and Feature Fusion for Students' Performance Prediction.

Around the world, the COVID-19 pandemic has created significant obstacles for education, driving people to discover workarounds to maintain education. Because of the excellent benefit of cheap-cost information distribution brought about by the advent of the Internet, some offline instructional activity started to go online in an effort to stop the spread of the disease. How to guarantee the quality of teaching and promote the steady progress of education has become more and more important. Currently, one of the ways to guarantee the quality of online learning is to use independent online learning behavior data to build learning performance predictors, which can provide real-time monitoring and feedback during the learning process. This method, however, ignores the internal correlation between e-learning behaviors. In contrast, the e-learning behavior classification model (EBC model) can reflect the internal correlation between learning behaviors. Therefore, this study proposes an online learning performance prediction model, SA-FEM, based on adaptive feature fusion and feature selection. The proposed method utilizes the relationship among features and fuses features according to the category that achieved better performance. Through the analysis of experimental results, the feature space mined by the fine-grained differential evolution algorithm and the adaptive fusion of features combined with the differential evolution algorithm can better support online learning performance prediction, and it is also verified that the adaptive feature fusion strategy based on the EBC model proposed in this paper outperforms the benchmark method.

Prediction of COVID-19 Disease by ARIMA Model and Tuning Hyperparameter Through GridSearchCV

The COVID-19 pandemic has significantly impacted the mental, physiological, and financial well-being of people around the globe. It has threatened lives and livelihoods and triggered supply chain disruptions and economic crises. In every country, there are risks and long-term implications. Planners and decision-makers could benefit from a forecasting model that anticipates the spread of this virus, thereby providing insight for a more targeted approach, advanced preparation, and drive better proactive collaboration. The signs and symptoms of a disease like COVID-19 are hard to define and predict, particularly during times of pandemic. Several epidemiological studies have been successful in identifying predictors, using artificial intelligence (AI). This paper explores various methodologies for tuning the hyperparameters of the auto-regressive integrated moving average (ARIMA) model, using GridSearchCV, to predict and analyze the occurrence of COVID-19 in populations. In time series analysis, hyperparameters are crucial and the GridSearchCV methodology results in greater predictive accuracy. The parameters proposed for the analysis of daily confirmed cases, recovered cases, and deceased cases in India were ARIMA (4, 1, 5), ARIMA (5, 1, 1), and ARIMA (5, 1, 1), respectively. The performance of the model with different configurations was evaluated using three measurements: root mean square error (RMSE), R2 score, and mean absolute error (MAE). These results were compared with a state-of-the-art method to assess model selection, fitting, and forecasting accuracy. The results indicated accuracy and continuous growth in the number of confirmed and deceased cases, while a decreasing trend was graphed for recovered cases. In addition, the proposed ARIMA using a GridSearchCV model predicted more accurately than existing approaches. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Enhancing Online Epidemic Supervising System by Compartmental and GRU Fusion Model

The global pandemic, COVID-19, is an acute respiratory infectious disease caused by the 2019 novel coronavirus. Building the online epidemic supervising system to provide COVID-19 dynamic prediction and analysis has attracted the attention of the industry and applications community. In previous studies, the compartmental models and deep neural networks (DNNs) played important roles in predicting and analyzing the dynamics of the pandemic. Nevertheless, the compartmental model has limited ability to fit historical data and thus leads to unsatisfactory prediction accuracy due to the difficulty in parameter estimation. For DNNs, the lack of interpretability makes it difficult to explain the prediction results;thus, it cannot provide an in-depth understanding of the transmission mechanism of the pandemic. We propose a fusion model to leverage the merits of both models and resolve their shortcomings. The fusion model extracts epidemic-related knowledge from the state-of-the-art SEIDR compartmental model to guide the training of the GRU model, which can preserve the interpretability and achieve a good performance in predicting epidemic dynamics. This model can help to enhance the online epidemic supervising system by providing more accurate prediction results and deeper analysis. Our extensive experiments across multiple epidemic datasets from six European countries demonstrate that our model outperforms existing state-of-the-art baselines in predicting the active confirmed cases. More importantly, by analyzing the effective reproductive number, our method can reveal the risk of the second wave of the epidemic in Europe and justify the importance of social distancing to control the outbreak of the epidemic. © 2022 Junyi Ma et al.

Prediction and analysis of natural gas consumption in chongqing with a grey prediction model group in the context of COVID-19.

In this paper, a grey prediction model group is employed to quantitatively study the impact of COVID-19 on natural gas consumption in Chongqing, China. First, a grey prediction model group suitable for the prediction of Chongqing's natural gas consumption is introduced, which consists of GM(1,1), TWGM(1,1), and the newly-developed ODGM(1,1). Then, the model group is constructed to predict Chongqing's natural gas consumption in 2020. Finally, compare the predicted results of the model group with the actual consumption and quantitatively analyze the impact of the epidemic on natural gas in Chongqing. It is found that the impact of the epidemic on the consumption of natural gas in the first quarter of the year is very small, but relatively bigger in the second and third quarters. The study is of positive significance to maintain the supply and demand balance of natural gas consumption in Chongqing in the background of COVID-19; and it enriches and develops the theoretical system of grey prediction models.

An Integration of AI, Blockchain and IoT Technologies for Combating Covid-19

An entire world is facing epidemic from past one year due to Covid-19. With effect this, lot of countries and lot of states are collapsed their economic and also mainly common man suffers during lockdown. One of main reasons to get vaccination and drug delayed is variable characteristics of Covid-19 from individual to individual hence, it difficult to finalize characteristics and symptoms. Recently, there are huge research going on Covid-19 and presenting various directions to prepare vaccine and drug. This paper presents novel platform such as integrated IoT based AI with Blockchain technology to combat Covid-19. AI discusses with required ML and DL algorithms for prediction and analyzes data of Covid-19. This paper also directs new direction to prepare vaccine and drug based on AI hence updating and storing data of infected individuals automatically. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

COVID-19 Prediction and analysis using Neural Network and Pearson Correlation

In the present study, a neural network-based predictive model has been used to predict the trend of the second wave of COVID-19 in a few countries, namely the US, UK, Brazil, South Africa, and India. The Neural Network model was trained for the rise of first-wave and refined predicting and comparing the predictions with the observed trends of the second waves in these countries. As the US has seen a clear-cut arrival of third-wave, the methodused was a neural network tool on Matlab software to predict the covid-19 wave pattern and later used to have a prediction of third-wave in India. Pearson correlation coefficient between the covid first wave and second wave for five countries was also computed and results were rationalized in terms of the extent of population vaccinated in these countries. © 2021 IEEE.

COVID-19 PREDICTION THROUGH CHEST X-RAY IMAGE DATASETS USING DEEP LEARNING

The work aims at the prediction and analysis of COVID-19 from Chest X-Ray scan images using Pre-trained Deep Convolutional Neural Network models. Analysis is carried out using two open-source datasets, to identify and differentiate between the Chest X-Ray scans of non COVID person and COVID-19 affected person. A baseline model using LeNet-5 is implemented using the initial dataset collected, which gave 98.57 % accuracy. Further, pre-trained models such as AlexNet, ResNet 50, Inception V3, VGG16, VGG19 and Xception are used for COVID prediction and carryout comparative analysis. Using the performance measures viz. Accuracy, Confusion Matrix and ROC Curves, the result of study shows that for the first dataset used for analysis, Xception and for the second dataset Inception architectures respectively are most suitable for the prediction of COVID-19. © 2021 IEEE.

Covid-19 Impact and Implications on Traffic: Smart Predictive Analytics for Mobility Navigation

Traffic prediction and analysis is an essential task towards intelligent mobility, particularly for path planning and navigation. When the traffic flow starts after the COVID-19 pandemic is subsided, the mobility patterns changes and may become unpredictable or challenging. This problem may be crucial, particularly if many people hurry to single occupancy transport mode. Notably, the rapid development in machine learning with new methods and the emergence of new data sources make it possible to evaluate and predict traffic conditions in smart cities more quickly and precisely. The proposed work is modeled in two-fold manner to investigate the impact of COVID shift in regular urban traffic movements given the particular period of the pre, during, and post lockdown phases. Firstly, the investigation is carried out for time series analysis considering the three phases of lockdown. Secondly, the real-time spatial information is analyzed for different time zones in a day. Notably, this requires a detailed analysis of the heterogeneous and complex input traffic data. Machine learning and advanced deep learning methodologies such as regression models, RNN, variants of LSTM, and GRU is used for analysis in this proposed traffic modeling. Significantly, the least error scores with Root Mean Square Error (RMSE) loss of 1.82 is observed for the RNN and GRU models, and 0.058 with the Gradient Boosting regression analysis, respectively. © 2022 IEEE.

Prediction and Analysis of COVID-19 Epidemic Situation via Modified SEIR Model with Asymptomatic Infection

This paper deals with the prediction and analysis of COVID-19 epidemic situation based on a modified SEIR model with asymptomatic infection. First, by considering the self-isolation and asymptomatic infection, a modified SEIR model is proposed to predict and evaluate the epidemic situation of COVID-19 in Hubei Province, China. Then, based on the daily data reported by the Health Commission of Hubei Province, the modified SEIR model is solved numerically, and the parameters of the modified model are inverted by the least square method. Third, based on the modified model, the epidemic situation of COVID-19 in Hubei Province is predicted and verified. The simulation results show that the modified SEIR model is significant and reliable to describe the spread property of the COVID-19, thereby providing a potential theoretical support for the decision-making of epidemic prevention and control in the future. © 2021 IEEE.

Research of Epidemic Dynamics Model Considering Individual Movements and Urban Areas

With the rapid spread of COVID-19, hundreds of millions of people worldwide have been infected. In order to cope with the epidemic, experts from various countries have carried out a lot of research works. Most of these works chose to use the traditional SEIR model, but the traditional model doesn't consider the individual's movement in the city. Based on the transmission characteristics of COVID-19, this paper optimized the traditional SEIR model by combining the in-depth mining and processed multiple data, such as the real epidemic data published by some official organizations, as well as data with certain credibility obtained from reference papers, journals or newspapers. Compared with the traditional SEIR model, the proposed model takes into account the impact of individuals' movement and the division of urban functional areas. The outcomes can play a certain role in the prediction and analysis of the spread of the epidemic in cities with regular individuals' movements and functions of urban areas. © 2021 IEEE.

Prediction and Analysis of Covid-19 Positive Cases Using Deep Learning Model

At the end of December 2019, the COVID-19 virus was the initial report case in China Wuhan City. On March 11, 2020. The Department of Health (WHO) announced COVID-19, a global pandemic. The COVID-19 spread rapidly out all over the world within a few weeks. We will propose to develop a forecasting model of COV-19 positive case predict outbreak in Pakistan using Deep Learning (DL) models. We assessed the main features to forecast patterns and indicated The new COVID-19 disease pattern in Pakistan and other countries of the world. This research will use the deep learning model to measure several COVID-19 positive case reports in Pakistan. LSTM cell to process time-series data forecasts is very efficient. Recurrent neural network processes to handle time-dependent and involve hidden layers are confirmed and predict positive cases and weekly cases reported in the future. Bidirectional LSTM (Bi-LSTM) processes data and information in one direction to predict and analyze the weekly 6-9 days readily forecast the number of positive cases of COVID-19 © 2021 IEEE.