ABSTRACT
In 2019, a pandemic of the so-called new coronavirus (SARS-COV-II) began, which causes the disease COVID-19. In a short time after the first case appeared, hundreds of countries began to register new cases every day. Mapping and analyzing the flow of people, regardless of the mode of transport, can help us to understand and prevent several phenomena that can affect our society in different ways. Graphs are complex networks made up of points and edges. The (geo)graphs are graphs with known spatial location and, in the case of our study, the edges represent the flow between them. The (geo)graphs proved to be a promising tool for such analyses. In the study region, municipalities that first registered their COVID-19 cases are also municipalities that have the highest mobility indices analyzed: degree, betweenness and weight of edges. © 2022 National Institute for Space Research, INPE. All rights reserved.
ABSTRACT
The COVID-19 outbreak began in December 2019 and was declared a global health emergency by the World Health Organization. The four most dominating variants are Beta, Gamma, Delta, and Omicron. After the administration of vaccine doses, an eminent decline in new cases has been observed. The COVID-19 vaccine induces neutralizing antibodies and T-cells in our bodies. However, strong variants like Delta and Omicron tend to escape these neutralizing antibodies elicited by COVID-19 vaccination. Therefore, it is indispensable to study, analyze and most importantly, predict the response of SARS-CoV-2-derived t-cell epitopes against Covid variants in vaccinated and unvaccinated persons. In this regard, machine learning can be effectively utilized for predicting the response of COVID-derived t-cell epitopes. In this study, prediction of T-cells Epitopes' response was conducted for vaccinated and unvaccinated people for Beta, Gamma, Delta, and Omicron variants. The dataset was divided into two classes, i.e., vaccinated and unvaccinated, and the predicted response of T-cell Epitopes was divided into three categories, i.e., Strong, Impaired, and Over-activated. For the aforementioned prediction purposes, a self-proposed Bayesian neural network has been designed by combining variational inference and flow normalization optimizers. Furthermore, the Hidden Markov Model has also been trained on the same dataset to compare the results of the self-proposed Bayesian neural network with this state-of-the-art statistical approach. Extensive experimentation and results demonstrate the efficacy of the proposed network in terms of accurate prediction and reduced error. © 2023 Tech Science Press. All rights reserved.
ABSTRACT
Real-time access to information during a pandemic is crucial for mobilizing a response. A sentiment analysis of Twitter posts from the first 90 days of the COVID-19 pandemic was conducted. In particular, 2 million English tweets were collected from users in the United States that contained the word 'covid' between January 1, 2020 and March 31, 2020. Sentiments were used to model the new case and death counts using data from this time. The results of linear regression and k-nearest neighbors indicate that sentiments expressed on social media accurately predict both same-day and near future counts of both COVID-19 cases and deaths. Public health officials can use this knowledge to assist in responding to adverse public health events. Additionally, implications for future research and theorizing of social media's impact on health behaviors are discussed. © 2022 IEEE Computer Society. All rights reserved.
ABSTRACT
The aim of this work is to study the impact of the COVID-19 pandemic new cases on the Moroccan financial market using the Autoregressive Distributed Lag (ARDL) approach. The analysis focuses on the relationship between the natural logarithm of the Moroccan All Shares Index (MASI) price and the natural logarithm of new daily cases of COVID-19 in the short term as well as in the long term. A cointegration test is performed on the daily time series for the period from March 3, 2020 to February 11, 2022. A causality test of Toda-Yamamoto is also applied on the variables. The implementation of the forecast with the ARDL method improves the forecast accuracy by 8% to achieve 26.7%. The implementation of the forecast with the ARDL method shows that the addition of the lag of COVID19, the trend and the seasonality makes it possible to achieve a MAPE of 26.7% by improving it by 8% compared to the forecast with the lag of the price only. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
The World Health Organization (WHO) declared the status of coronavirus disease 2019 (COVID-19) to a global pandemic on March 11, 2020. Since then, numerous statistical, epidemiological and mathematical models have been used and investigated by researchers across the world to predict the spread of this pandemic in different geographical locations. The data for COVID-19 outbreak in India has been collated on daily new confirmed cases from March 12, 2020 to April 10, 2021. A time series analysis using Auto Regressive Integrated Moving Average (ARIMA) model was used to investigate the dataset and then forecast for the next 30-day time-period from April 11, 2021, to May 10, 2021. The selected model predicts a surge in the number of daily new cases and number of deaths. An investigation into the daily infection rate for India has also been done. © 2023 The authors and IOS Press.
ABSTRACT
This study attempts to study the impact of social and economic constraints, identification of new diseases, wind and solar energy consumption during the 2019 crisis on daily electricity demand by constructing multivariate correlation regression. The aim of the study is to determine the impact of the COVID-19 pandemic on the structure of electricity consumption by building regression models to analyse how various variables (detection of new diseases, wind and solar energy consumption) and social behaviour affect electricity demand. Tasks: to identify the main dates from the chronologies of COVID-19 in Russia, compare the electricity indicators by years, compare the data with the pre-pandemic period, study the share of generated electricity in the balance, conduct a correlation-regression analysis in order to identify the relationship between the detection of new cases of COVID-19 disease in the period from 03/30/2020 to 10/27/2021 and energy consumption, to study the impact of social activity on the level of consumption of renewable energy sources. This study identified links between new cases of coronavirus disease and energy consumption;wind energy consumption and general indicator;consumption of wind energy and solar with an indicator of morbidity. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
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.
ABSTRACT
In 2020, the world was attacked by a virus known as the COVID-19 virus. Restrictions on people's activities were conducted in various countries to prevent the spread of the virus. However, since people were vaccinated, restriction levels have been reduced or eliminated, although the new cases of COVID-19 worldwide have not ended. People's responses to restriction policies vary, including sentiment and human mobility. The possibility of sentiment is either support or resistance, while mobility is staying at home or not. This study analyzes the proportion between the two responses through two types of data: Text for sentiment and time series for mobility. Sentiment text data is taken from Twitter and mobility time series data is taken from Google Mobility for February 2020 to April 2022. Twitter and Google Mobility data are collected from several countries using English and implementing restrictions: Australia, Canada, Singapore, the United Kingdom (UK), and the United States (US). The unsupervised Autoencoder model is leveraged to find clusters. Two Autoencoder architectures are proposed for each data type. Before being used in Multilayer Autoencoder, text data is converted to vector data by Word2Vec. On the other hand, LSTM-Autoencoder is used for time series data. Finally, hypothesis tests are performed to determine the mean between the clusters formed is the same or different, out of five countries, only Canada has a null hypothesis is accepted, that means people in Canada tend to be neutral in response to COVID-19 while mobilities are dynamics, it reveals that people in Canada obey the government's decision on restrictions during the rise of COVID-19 cases. © 2022 ACM.
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The spread of COVID-19 in Indonesia is still classified as a pandemic until October 31, 2022. Even though the endemic has been enforced in several nations worldwide. However, the fact that people's mobility is increasing means that this condition can increase the number of new cases of COVID-19. The Indonesian government remains vigilant about any decisions that will be taken to maintain the stability of the country's health sector, economy, and population mobility. First, The purpose of this our research is to forecast of daily positive confirmed and daily mortality for the next 13 days using COVID-19 epidemiological data in Indonesia, i.e. DKI Jakarta and West Java. Second, the forecasting model uses a deep learning approach, i.e. LSTM and ARIMA. furthermore, The LSTM method and ARIMA modeling results are compared based on their respective to regions. Finally, The LSTM method has good model performance and the ability to forecast COVID-19 cases based on RMSE and MAPE. © 2022 IEEE.
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Several instances of pneumonia with no clear etiology were recorded in Wuhan, China, on December 31, 2019. The world health organization (WHO) called it COVID-19 that stands for "Coronavirus Disease 2019," which is the second version of the previously known severe acute respiratory syndrome (SARS) Coronavirus and identified in short as (SARSCoV-2). There have been regular restrictions to avoid the infection spread in all countries, including Saudi Arabia. The prediction of new cases of infections is crucial for authorities to get ready for early handling of the virus spread. Methodology: Analysis and forecasting of epidemic patterns in new SARSCoV-2 positive patients are presented in this research using metaheuristic optimization and long short-term memory (LSTM). The optimization method employed for optimizing the parameters of LSTM is Al-Biruni Earth Radius (BER) algorithm. Results: To evaluate the effectiveness of the proposed methodology, a dataset is collected based on the recorded cases in Saudi Arabia between March 7th, 2020 and July 13th, 2022. In addition, six regression models were included in the conducted experiments to show the effectiveness and superiority of the proposed approach. The achieved results show that the proposed approach could reduce the mean square error (MSE), mean absolute error (MAE), and R2 by 5.92%, 3.66%, and 39.44%, respectively, when compared with the six base models. On the other hand, a statistical analysis is performed to measure the significance of the proposed approach. Conclusions: The achieved results confirm the effectiveness, superiority, and significance of the proposed approach in predicting the infection cases of COVID-19. © 2023 CRL Publishing. All rights reserved.
ABSTRACT
Understanding how people's interest and emotional state change in response to news coverage of a particular topic and elucidating the characteristics of these changes can reveal the shifting nature of attention and emotion. We analyzed people's interest and emotional responses expressed via Twitter in response to news coverage of announcements of new cases of coronavirus disease 2019 (COVID-19) as a case study. As a measure of interest, we examined replies to tweets of news items posted by media outlets on Twitter, and classified the emotional content of each reply tweet using Plutchik's wheel of emotion. The analysis suggested that people were most interested in COVID-19 case numbers in April 2020, when the first wave of cases occurred and the first emergency declaration was issued, and in July 2020, when the second wave of cases emerged. The results revealed that fear was the most commonly expressed emotion. The ratio of fear-related tweets was highest in February and March 2020, a time at which new COVID-19 cases were confirmed in various locations and there was substantial public discussion regarding whether Japan would declare a state of emergency for the first time. © 2022 IEEE.
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The world is witnessing COVID - 19 Pandemic for quite some time now. India has seen three waves of COVID-19 in the last 700 days. The curiosity still lies in the occurrence and timing of the fourth wave. The current study tries to solve this and predicts the COVID-19 daily incidence in India in the future. State-of-the-art methodologies both from Machine learning (LSTM, KNN, SVR, Random Forest, and Multi Linear Regressor) and Mathematical models (SEIR) have been tried out to train and predict the Daily New Cases of COVID19 in India. Further prediction for the next 200 days has been tried out using the trained models. As per the forecast from most of the models, it is evident that no fourth wave is going to be witnessed in India in the next 200 days. © 2022 IEEE.
ABSTRACT
The COVID-19 pandemic has affected hundreds of millions of people in countries around the world. The number of new cases has reached 100,000 per day since the last wave of COVID-19 in Vietnam. It has become very apparent that the front-line employees are overworked. There are not enough PCR tests to keep up with the rate of the virus spreading in our community. In addition, the PCR test is expensive for the government, highly invasive, and time-consuming for patients, which discourages individuals from visiting the clinic for testing. Therefore, it is very necessary to have a quicker and simpler way of prescreening patients. This is the reason why the paper will introduce a new artificial intelligence application, named COVCOUGH, to early detect COVID-19 patients using cough sounds recorded by smartphones. During the recent peak of the epidemic in Vietnam, the COVCOUGH has been deployed and has more than 10,000 users. © 2022 IEEE.
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The COVID-19 pandemic has had an impact on many aspects of society in recent years. The ever-increasing number of daily cases and deaths makes people apprehensive about leaving their homes without a mask or going to crowded places for fear of becoming infected, especially when vaccination was not available. People were expected to respect confinement rules and have their public events cancelled as more restrictions were imposed. As a result of the pandemic's insecurity and instability, people became more at ease at home, increasing their desire to stay at home. The present research focuses on studying the impact of the COVID-19 pandemic on the desire to stay at home and which metrics have a greater influence on this topic, using Big Data tools. It was possible to understand how the number of new cases and deaths influenced the desire to stay at home, as well as how the increase in vaccinations influenced it. Moreover, investigated how gatherings and confinement restrictions affected people's desire to stay at home. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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We estimate the case fatality rate from COVID-19 with our method by age groups for three waves - September 2020 to January 2021 (wild type), February 2021 to May 2021 (alpha), and July 2021 to January 2022 (delta). We use linear regression with optimal lag with 21 days moving averaging to correct for reporting delays. We take the coefficient from the regression as the case fatality ratio. We unite the lower age groups into one to achieve a good correlation. We have new cases by age group and deaths by age group and sex. Our results indicate that the delta variant is more severe than alpha, and this is enough to outweigh any improvements in treatment since the first major wave, 14.08.2020–01.01.2021. © 2022, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.
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The paper examines the relation between the spread of the Covid-19 epidemic and the respective measures adopted by various countries collectively labeled as 'social distancing'. The progress of the disease is publicly available from the data published by the Johns Hopkins University Center for Systems Science and Engineering (JHU CSSE) that contain the new cases of Covid-19 and deaths from Covid-19 per country on a daily basis. Moreover, Google reports the situation regarding social distancing in a number of social activity categories in several countries based on data gathered by users of mobile phones using Google mobile applications. The paper analyzes these two sets of data for 22 countries (20 in Europe plus the USA and Canada) and shows a significant correlation between the decrease in the levels of social distancing and the daily rate of increase of new Covid-19 cases. Consequently, the discussion is concerned with the effect of social distancing measures per category of social activity, the level of conformance displayed by citizens in each country as well as the number of days required between the imposition of the measures and their effect on the spread of the pandemic. This discussion provides additional insight that can assist policy makers in imposing the most effective set of measures as well as the proper sequence of measures withdrawing in in similar situations. © 2022 IEEE.
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The outbreak and spread of COVID-19 poses a tremendous threat to the health of people all over the world. We collected the new imported COVID-19 cases daily in Shanghai, China from September 1, 2021 to January 17, 2022 from the National Commission on Health of the People's Republic of China website. The SVR and ARIMA models were constructed and compared. On this base, it is provided for the early warning of the outbreak of COVID-19 and the targeted preventive measures proposed for this infectious disease. © 2022 IEEE.
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Regarding the pandemic taking place in the world from the spread of the Coronavirus pandemic and viral mutations, the need has arisen to analyze the epidemic data in terms of numbers of infected and deaths, different geographical regions, and the dynamics of the spread of the virus. In China, the total number of reported infections is 224,659 on June 11, 2022. In this paper, the Gaussian Mixture Model and the decision tree method were used to classify and predict new cases of the virus. Although we focus mainly on the Chinese case, the model is general and adapted to any context without loss of validity of the qualitative results. The Chi-Squared (χ2) Automatic Interaction Detection (CHAID) was applied in creating the decision tree structure, the data has been classified into five classes, according to the BIC criterion. The best mixture model is the E (Equal variance) with five components. The considered data sets of the world health organization (WHO) were used from January 5, 2020, to 12, November 2021. We provide numerical results based on the Chinese case. © 2022 THE AUTHORS
ABSTRACT
In the context of the COVID-19 epidemic, the development and popularization of vaccines have effectively alleviated people's panic. Twitter, as one of the world's largest social platforms, promptly reflects the trend of emotional changes in screen names. Currently, vaccines such as Pfizer, Sputnik, and Moderna have successfully made a large number of people gain high immunity against the COVID-19 virus. However, a few cases of death due to vaccines have caused some people to question and worry about the safety of vaccines. A comprehensive understanding of progress of vaccine popularization is conducive making wiser decisions and calming people's panic. Since the large number of Tweets updated daily on Twitter can represent attitudes of netizens on the progress of vaccination, we used Bert model to predict and classify emotion categories to which different Tweets belong, with an accuracy rate of 80%. It is found that with the promotion of vaccination, fluctuation of netizen sentiment for vaccine progress has gradually decreased. Tweets with neutral sentiment still account for a majority of proportion, and the proportion of tweets with positive sentiment has gradually increased. In addition, we used LSTM model to predict the growth of cases with MSE less than 0.001. The growth of new cases in most countries gradually decreased to less than 10, 000 people per day after June. Therefore, most vaccines have made significant progress in both winning public support and preventing COVID-19 infection. © VDE VERLAG GMBH · Berlin · Offenbach.
ABSTRACT
The COVID-19 epidemic is still very serious, because the United States and other countries have relaxed prevention and control, and the vaccine is ineffective against the mutant virus, resulting in a large number of new cases. The existing epidemic detection methods are still insufficient, and some detection methods are relatively expensive and complicated, resulting in the supply not keeping up with the demand for detection. The purpose of this study is to use relatively convenient, fast and low-cost computer vision technology for epidemic detection. We tried the VGG, ResNet and DenseNet models on an open Kaggle dataset, and found that DenseNet achieved the best results, achieving 95% accuracy, and there is hope for further applications in the future. © 2022 IEEE.