COVID-19 Data Prediction and Visualization in Mainland China

Contemporarily, COVID-19 shows a sign of recurrence in Mainland China. To better understand the situation, this paper investigates the growth pattern of COVID-19 based on the research of past data through regression models. The proposed work collects the data on COVID-19 in Mainland China from January 21st, 2020, to April 30th, 2020, including confirmed, recovered, and death cases. Based on polynomial regression and support vector machine regressor, it predicts the further trend of COVID-19. The paper uses root mean squared error to evaluate the performance of both models and concludes that there is no best model due to the high frequency of daily changes. According to the analysis, support vector machine regressors fit the growth of COVID-19 confirmed case better than polynomial regression does. The best solution is to utilize different types of models to generate a range of prediction result. These results shed light on guiding further exploration of the growth of COVID-19. © 2023 SPIE.

Covid-19 descriptive analytics visualization of pandemic and hospitalization data

This chapter will introduce readers to the kinds of data visualizations that can be made from a data set. From these data visualizations, readers can form some important insights about the nature of the data sets even before analysis begins. Based on these insights, analytical models can be designed to generate other insights valuable for understanding various phenomena in the data set. © 2023 Elsevier Inc. All rights reserved.

Research on machine learning-based epidemic impact on Social Electricity Consumption model

This paper uses social electricity consumption data from 2015-2021 in a city in Hubei province, and uses some methods of artificial intelligence, for example, python function fitting and machine learning to construct an impact analysis and prediction model of the COVID-19 epidemic on Electricity Consumption. Through comparison with the effects of general linear regression and polynomial regression, a better model is developed which comprises four independent variables and uses polynomial regression. The model developed in this paper helps to quantify and measure the impact of the epidemic on society's electricity consumption, and ultimately enables users in the electricity industry to make convenient and rapid forecasts, helping them to make reasonable power supply plans, trading plans and dispatch plans, and to ensure safe and economic operation of the Electricity System. © 2022 ACM.

Simulation of Impact of Migration Caused by the War in Ukraine on COVID-19 Epidemic Process in Moldova

The novel coronavirus pandemic has become a global challenge and has shown that health systems worldwide are unprepared for pandemics of this magnitude. The war in Ukraine, escalated by Russia on February 24, 2022, brought deaths and a humanitarian catastrophe and stimulated the spread of COVID-19. Most refugees who evacuated from the war crossed the border with other countries. At the end of July, almost 550 thousand people crossed the border with Moldova. This study is devoted to modeling the impact of migration processes on the dynamics of COVID-19 in Moldova. For this, a machine learning model was built based on the polynomial regression method. The forecast accuracy a month before the escalation of the war was from 98.77% to 96.37% for new cases and from 99.8% to 99.75% for fatal cases. The forecast accuracy for the first month after the escalation of the war was from 99.96% to 99.34% for new cases and from 99.91% to 99.88% for fatal cases. The high accuracy of the model, both before the war and with the start of its escalation, suggests that the migration flows of refugees from Ukraine to Moldova did not affect the dynamics of COVID-19. ©2022 Copyright for this paper by its authors.

Forecasting the Growth in Covid-19 Infection Rates

The recent times have seen the global rise in infection rates from the virus Covid-19, leading to a pandemic. The exponential rise in infections and deaths lead to panic and nation-wide lockdowns across the globe. Advancements in biotechnical and medical research have paved the way for the development and mass distribution of vaccines. To build an understanding of the current situation we did a comparative analysis of the rise in infection rates among citizens across the countries and also the growth in vaccinations in the pre-vaccination phase and the post-vaccination phase of the on-going pandemic to determine whether the rate of vaccination is more than the rate of infection or otherwise. Then, a comparison is done among two prediction models we built, one using polynomial regression and other using SVM to determine which model provides better prediction results of infection rates in a country. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

COVID-19 Future Forecasting Using Machine Learning Models

Covid-19 is a very infectious virus. According to World Health Organization (WHO), millions of individuals have been diagnosed with Covid-19 since then, and at least a million have died as the virus has expanded dramatically. While most of the news on this front is scary, technology is helping to pave the path through this crisis. Manual forecasting is a difficult challenge for humans due to its large scale and complexity. Machine Learning (ML) techniques can effectively predict Covid-19 infected patients. There are a lot of study that have been developed to predict and forecast the future number of cases affected by Covid-19. In this area, our forecasting can be tackled as a problem of supervised learning. Supervised ML is very popular regression methods due to its simplicity to be interpreted by Humans. In this paper, we use two datasets to predict the symptoms through two different types of regression algorithms (single and multiple regression), the ML algorithms are LR, SVM, LASSO, ES and Polynomial regression, for the multiple regression we used LR, SVM and LASSO. The obtained results validate that for the single regression the Exponential Smoothing (ES) outperforms other machine learning approaches like Linear Regression (LR) and LASSO in terms of R-Square, Adjusted R-Square, Mean Absolute Error (MAE), Mean Square Error (MSE), and Root Mean Square Error (RMSE). The same accuracy is observed for the models used in the multiple regression. © 2022 IEEE.

Impact of Migration on COVID-19 Epidemic Process in Romania: A Simulation Study

The Russian war in Ukraine, which escalated on February 24, 2022, caused massive destruction and the death of thousands of people. In addition, the Russian invasion has affected the public health system and the spread of infectious diseases. Millions of Ukrainians fled from the war, which caused a pan-European migration crisis. This study is devoted to testing the hypothesis of the impact of population migration caused by the Russian war in Ukraine on the dynamics of the spread of COVID-19 in Romania. For this, a machine learning model was developed based on the polynomial regression method. The model showed high accuracy. However, the formulated hypothesis was not confirmed fully. The results of the experimental study showed that population migration have not impacted the fatality caused by COVID-19, but has the impact on COVID-19 new cases. The further investigation is needed to find out the exact factors which influenced the epidemic process. © 2022 IEEE.

Impact of Russian War on COVID-19 Dynamics in Germany: the Simulation Study by Statistical Machine Learning

The new coronavirus COVID-19 has been spreading worldwide for almost three years. The global community has developed effective measures to contain and control the pandemic. However, new factors are emerging that are driving the dynamics of COVID-19. One of these factors was the escalation of Russia's war in Ukraine. This study aims to test the hypothesis of the influence of migration flows caused by the Russian war in Ukraine on the dynamics of the epidemic process in Germany. For this, a model of the COVID-19 epidemic process was built based on the polynomial regression method. The model's adequacy was tested 30 days before the start of the escalation of the Russian war in Ukraine. To assess the impact of the war on the dynamics of COVID-19, the model was used to calculate the forecast of cumulative new and fatal cases of COVID-19 in Germany in the first 30 days after the start of the escalation of the Russian war in Ukraine. Modeling showed that migration flows from Ukraine are not a critical factor in the growth of the dynamics of the incidence of COVID-19 in Germany, but they influenced the number of cases. The next stage of the study is the development of more complex models for a detailed analysis of population dynamics, identifying factors influencing the epidemic process in the context of the Russian war in Ukraine, and assessing their information content. © 2022 Copyright for this paper by its authors.

How to recover from difficult condition? The relationship between leader-employee congruence in creativity goal and innovative performance

Purpose>Drawing upon the literature on person-leader supplementary fit literature, this study aims to positions dissatisfaction with organizational performance as a difficult condition that moderates the relationship between leader-employee congruence/incongruence in creativity goal and employee innovative performance.Design/methodology/approach>In this paper data were collected from 226 leader-employee dyads from several information technology companies in China. Polynomial regression combined with the response surface methodology was used to test the hypotheses.Findings>Three conclusions were drawn. First, employee innovative performance was maximized when leaders and employees were congruence in creativity goal. Second, in the case of congruence, employee had higher innovative performance when a leader's and an employee's creativity goal matched at high levels. Third, dissatisfaction with organizational performance moderated the effect of leader – employee congruence in creativity goal on employee innovative performance.Originality/value>This study enhanced theoretical developments by considering the importance of leaders' congruence with employees in creativity goal for the first time. Additionally, the research results provided better practical guidance for how to help employees recover from difficult condition and continue to participate in innovation.

Estimating COVID-19 Cases Using Machine Learning Regression Algorithms

Background: Coronavirus refers to a large group of RNA viruses that infects the respira-tory tract in humans and also causes diseases in birds and mammals. SARS-CoV-2 gives rise to the infectious disease “COVID-19”. In March 2020, coronavirus was declared a pandemic by the WHO. The transmission rate of COVID-19 has been increasing rapidly;thus, it becomes indispensable to estimate the number of confirmed infected cases in the future. Objective: The study aims to forecast coronavirus cases using three ML algorithms, viz., support vector regression (SVR), polynomial regression (PR), and Bayesian ridge regression (BRR). Methods: There are several ML algorithms like decision tree, K-nearest neighbor algorithm, Ran-dom forest, neural networks, and Naïve Bayes, but we have chosen PR, SVR, and BRR as they have many advantages in comparison to other algorithms. SVM is a widely used supervised ML algorithm developed by Vapnik and Cortes in 1990. It is used for both classification and regression. PR is known as a particular case of Multiple Linear Regression in Machine Learning. It models the rela-tionship between an independent and dependent variable as nth degree polynomial. Results: In this study, we have predicted the number of infected confirmed cases using three ML algorithms, viz. SVR, PR, and BRR. We have assumed that there are no precautionary measures in place. Conclusion: In this paper, predictions are made for the upcoming number of infected confirmed cases by analyzing datasets containing information about the day-wise past confirmed cases using ML models (SVR, PR and BRR). According to this paper, as compared to SVR and PR, BRR performed far better in the future forecasting of the infected confirmed cases owing to coronavirus. © 2022 Bentham Science Publishers.

Using Regression to Predict Number of Tourism in Indonesia based of Global COVID-19 Cases

COVID-19 has majorly impacted the world and has spread to every corner of the world. As a result, the tourism industry suffered greatly with many tourist sites having to close. Previous research has used regression models to predict the impact of COVID-19, though few has linked it to the number of tourists. This paper uses five different regression models to predict tourism rates based on multiple country's COVID-19 data. Regression models include linear regression, polynomial regression, K-Nearest Neighbors regression, random forest regression, and support vector regression. The datasets that we use are COVID-19 data that contains the number of cases and Indonesia's tourism data that contains the monthly number of incoming tourists to Indonesia from different countries. The dataset will be processed by selecting the countries with the most amount of tourist. The preprocessed dataset is divided into two for training and testing the models with an 8:2 ratio. The result from the evaluation showed that random forest regression has the highest accuracy with a R2 score of 0.9. Our research is limited to the number of datasets that are used as there might be other variables that are not considered. © 2022 IEEE.

Monitoring Novel Corona Virus using Machine Learning

In this article, we are working on a new Pandemic Corona (COVID-19) virus. COVID-19 is an infectious disease that causes serious lung damage. COVID-19 causes a disease in humans and has killed many people around the world. However, the virus has been declared pandemic by the World Health Organization (WHO) and all countries are trying to control and block all locations. In particular, four standard forecasting models have been used: linear regression (LR), logistics regression (LOR) and polynomial regression. Many areas of application that require the identification and hierarchy of threats have long used automatic learning models. [1] Machine-based (ML) analysis methods have been shown to be useful in predicting preoperative outcomes and improving decision-making about future actions. Different forecasting methods are widely used to solve forecasting problems. The purpose of this study was to determine the function of COVID-19 research and machine learning applications and algorithms for various purposes [2]. © 2022 IEEE.

IMPACT OF WAR ON COVID-19 PANDEMIC IN UKRAINE: THE SIMULATION STUDY

The COVID-19 pandemic has posed a challenge to public health systems worldwide. As of March 2022, almost 500 million cases have been reported worldwide. More than 6.2 million people died. The war that Russia launched for no reason on the territory of Ukraine is not only the cause of the death of thousands of people and the destruction of dozens of cities but also a large-scale humanitarian crisis. The military invasion also affected the public health sector. The impossibility of providing medical care, non-compliance with sanitary conditions in areas where active hostilities are occurring, high population density during the evacuation, and other factors contribute to a new stage in the spread of COVID-19 in Ukraine. Building an adequate model of the epidemic process will make it possible to assess the actual statistics of the incidence of COVID-19 and assess the risks and effectiveness of measures to curb the curse of the disease epidemic process. The article aims to develop a simulation model of the COVID-19 epidemic process in Ukraine and to study the results of an experimental study in war conditions. The research is targeted at the epidemic process of COVID-19 under military conditions. The subjects of the study are models and methods for modeling the epidemic process based on statistical machine learning methods. To achieve the study's aim, we used forecasting methods and built a model of the COVID-19 epidemic process based on the polynomial regression method. Because of the experiments, the accuracy of pre-dicting new cases of COVID-19 in Ukraine for 30 days was 97,98%, and deaths of COVID-19 in Ukraine – was 99,87%. The model was applied to data on the incidence of COVID-19 in Ukraine for the first month of the war (02/24/22 - 03/25/22). The calculated predictive values showed a significant deviation from the registered sta-tistics. Conclusions. This article describes experimental studies of implementing the COVID-19 epidemic pro-cess model in Ukraine based on the polynomial regression method. The constructed model was sufficiently ac-curate in deciding on anti-epidemic measures to combat the COVID-19 pandemic in the selected area. The study of the model in data on the incidence of COVID-19 in Ukraine during the war made it possible to assess the completeness of the recorded statistics, identify the risks of the spread of COVID-19 in wartime, and determine the necessary measures to curb the epidemic curse of the incidence of COVID-19 in Ukraine. The investigation of the experimental study results shows a significant decrease in the registration of the COVID-19 incidence in Ukraine. An analysis of the situation showed difficulty in accessing medical care, a reduction in diagnosis and registration of new cases, and the war led to the intensification of the COVID-19 epidemic process © Dmytro Chumachenko, Pavlo Pyrohov, Ievgen Meniailov, Tetyana Chumachenko, 2022

The Model of Regularization Coefficient in Polynomial Regression for Modelling the Spread of COVID-19 in Romania

Research on the pandemic situation of COVID-19 is very important for delivering detailed risk analyzes based on estimating the peak of the pandemic. The machine learning approach has a major role to play in predicting the number of COVID-19 cases. Most research on COVID-19 uses polynomial regression for analysis. When a regression model is build, often, the model fails to generalize on unseen data. For instance, the model might end up becoming too complex, having significantly high variance due to over-fitting, thereby impacting the model performance on new data sets. To avoid over-fitting of the polynomial regression, a regularization method can be used to suppress the coefficients of the higher order polynomial, a principle that allows the smoothness of the regression function. The aim of this paper is to formulate a mathematical model for regularization coefficient in polynomial regression and evaluate this approach to enable obtaining meaningful results on a COVID-19 data set. Therefore we believe that our results will contribute to a better understanding of the over-fitting process in polynomial regression. Our methodology consists of following major steps: i) optimizing the model using k-fold cross-validation for finding an optimal regularization coefficient and ii) comparing the performance of ridge regression and lasso regression using accuracy metrics. Moreover, our approach could also have a potential impact in machine learning education, regarding the understanding of the underlying mathematical machinery behind polynomial regression algorithms. The obtained results show that the polynomial model built using lasso regression, outperforms the ridge regression. © 2022 IEEE.

Toward Data Visualization and Data Forecasting with COVID-19 Vaccination Statistics

The world runs on data. Various organizations, businesses, and institutions utilize and generate data. This information is a valuable commodity if availed of in the right way. Big data can be large and incomprehensible on its own, but when analyzed computationally, it can be a powerful tool for revealing patterns and trends, forecasting future values of certain data parameters as well as providing clarity about the metrics in the data. Data visualization and forecasting using such data are fields that have applications in every sector—from information technology, to education, to healthcare. Since the world was hit by the debilitating COVID-19 pandemic in 2019, life has become a blur of statistics—daily new case counts, daily deaths and recoveries, number of people vaccinated, etc. Such data are of paramount importance to everyone affected by the pandemic, and presenting it in a way that is easily understandable to a layperson and using it to glean insights into the spread and curb of the disease as well as the efficacy of the vaccines is necessary. This paper takes COVID vaccination statistics as a use case for the fields of data visualization and data forecasting. It elucidates the methodology and benefits of both interactive visualizations of vaccination data and forecasting future trends in vaccine and case metrics based on data over time. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Requested to do right things excessively: how citizenship pressure/future focus influence health-related work outcomes in health organizations during the pandemic.

PURPOSE: Public health practitioners face citizenship pressure when requested to engage in more extra-roles behaviors during the pandemic. The purpose of the study is to reveal the potential influence mechanism of citizenship pressure on the health and work outcomes of practitioners. DESIGN/METHODOLOGY/APPROACH: The authors completed a three-wave survey from a public healthcare organization during the coronavirus disease 2019 (COVID-19) delta-variant epidemic. FINDINGS: Results of polynomial regression and response surface showed that increased (versus decreased) and consistently high (versus low) level of citizenship pressure induced citizenship fatigue, which in turn increases negative affect/turnover intention. These negative effects of citizenship pressure are weaker among practitioners with a higher level of future focus. PRACTICAL IMPLICATIONS: Providing counseling service to health care practitioners in adopting a future time perspective of citizenship behaviors is important for public health organizations. ORIGINALITY/VALUE: This study is among the earliest attempts to reveal the potential dark side of excessive request of conducting organization citizenship behavior which is more commonly seen within public health organizations in the context of pandemic.

Machine Learning for Trend Analysis and Forecasting COVID-19

System-based prediction based on machine learning has demonstrated its utility in predicting the appropriate outcomes for the longer-term course of activities. There are numerous applications in which machine learning models are used to identify and prioritize unfavorable factors for threats. There have been a lot of deaths in various parts of the world caused by the Novel Coronavirus or COVID-19. There is no doubt that the best pandemics in world history are grouped together in this warning to public health. Increasing the accuracy of our predictions requires identifying the characteristics that could show correlation with COVID-19's spread. The purpose of this article is to deliver an import an ten hanced consideration of how machine learning models are frequently used in real-life settings. To predict COVID-19 and to forecast the threatening variables of COVID-19, we compared four machine learning models: Linear Regression model, Polynomial Regression model, Support Vector Machine (SVM) model, and Bayesian Ridge Polynomial Regression model. Based on the models, a forecast of confirmed cases over the next ten days can be made. In addition to analyzing the present pattern or trend of COVID-19 spread, this paper also defines the challenges for future research. © 2022 IEEE.

Comparative study on performance of different artificial neural network methods for prediction of the Covid19

Purpose>This study aims to compare many artificial neural network (ANN) methods to find out which method is better for the prediction of Covid19 number of cases in N steps ahead of the current time. Therefore, the authors can be more ready for similar issues in the future.Design/methodology/approach>The authors are going to use many ANNs in this study including, five different long short-term memory (LSTM) methods, polynomial regression (from degree 2 to 5) and online dynamic unsupervised feedforward neural network (ODUFFNN). The authors are going to use these networks over a data set of Covid19 number of cases gathered by World Health Organization. After 1,000 epochs for each network, the authors are going to calculate the accuracy of each network, to be able to compare these networks by their performance and choose the best method for the prediction of Covid19.Findings>The authors concluded that for most of the cases LSTM could predict Covid19 cases with an accuracy of more than 85% after LSTM networks ODUFFNN had medium accuracy of 45% but this network is highly flexible and fast computing. The authors concluded that polynomial regression cant is a good method for the specific purpose.Originality/value>Considering the fact that Covid19 is a new global issue, less studies have been conducted with a comparative approach toward the prediction of Covid19 using ANN methods to introduce the best model of the prediction of this virus.

Evaluation of using Parametric and Non-parametric Machine Learning Algorithms for Covid-19 Forecasting

Machine learning prediction algorithms are considered powerful tools that could provide accurate insights about the spread and mortality of the novel Covid-19 disease. In this paper, a comparative study is introduced to evaluate the use of several parametric and non-parametric machine learning methods to model the total number of Covid-19 cases (TC) and total deaths (TD). A number of input features from the available Covid-19 time sequence are investigated to select the most significant model predictors. The impact of using the number of PCR tests as a model predictor is uniquely investigated in this study. The parametric regression including the Linear, Log, Polynomial, Generative Additive Regression, and Spline Regression and the non-parametric K-Nearest Neighborhood (KNN), Support Vector machine (SVM) and the Decision Tree (DT) have been utilized for building the models. The findings show that, for the used dataset, the linear regression is more accurate than the non-parametric models in predicting TC & TD. It is also found that including the total number of tests in the mortality model significantly increases its prediction accuracy.

Modelling New Cases of Covid-19 in the Philippines using Polynomial and MLP Regression

Covid-19 has been a serious issue in the Philippines for the past two years. Its spread has taken a toll on the country's economy and society. Furthermore, the populous has been suffering throughout the pandemic as new cases and deaths are increasing. These massive problems warrant research on modelling and predicting this pandemic. Although there are numerous research with regards to using statistical modelling, Machine learning, deep learning, and artificial intelligence to model and understand the pandemic throughout the world, few pieces of researches focus on the Philippines alone. In addition to that, simple models are seen to fit the Covid-19 data more than complex ones. With these in mind, the authors fit and modelled Philippine new cases of Covid-19 using Sklearn Polynomial and MLP regressors. It was found out that Polynomial models fit the entire dataset from January 2020 to September 2021, but MLP model fits the recent September 2021 data better. Further research using different countries as case studies or different models is recommended. © 2021 IEEE.