A review of some Covid-19 pandemic numbers in European Union, Canada, and Mexico

The rapidity and extent of Covid-19 infections have shown how a phenomenon that initially seemed geographically circumscribed quickly spread worldwide. In 2020, the spread of infection and the containment and management measures taken by local governments have been quite heterogeneous. Therefore, here we investigate the different ways of the spread of the infection in different areas, and specifically in Canada, Mexico, and the European Union states. For this purpose, for each area, official data on infection in 2020 are used to depict, analyze, and compare the monthly contagion's curves and the Rt index, both in absolute and relative terms. © Springer Nature Switzerland AG 2023. All rights reserved.

Measuring the Spread of COVID-19: Restrictions and Mobility in the Visegrad Countries (Czech Republic, Hungary, Poland, Slovakia)

The Visegrad Group, Visegrad Four, or V4, is a cultural and political alliance of four countries of Central Europe (Czech Republic, Hungary, Poland and Slovakia). The smallest country of them, Slovakia, recorded the first confirmed case of COVID-19 on March 6, 2020 in a 52-year-old man from the Bratislava region. However, it is a secondary transmission of the disease, as his son returned home from the risk area (Venice, Italy) (ÚVZSR, Retrieved from https://www.uvzsr.sk/index.php?option=com_contentandview=articleandid=4061:slovensko-zaznamenalo-prvy-potvrdeny-pripad-ochorenia-covid-19andcatid=250:koronavirus-2019-ncovandItemid=153, 2020). This record points to the gradual spread of the virus from the European outbreak in northern Italy and France (the first cases on January 24) to the neighbouring countries with the V4 countries emerging approximately 1 month apart. The first of the four countries was Czechia (3 cases) on March 1, followed by Hungary (2 cases) together with Poland (1 case) on March 4. In this study we will take a closer look at how quickly and in which regions of Czechia, Hungary, Poland and Slovakia the virus spread in contrast to the dates of implementation of individual measures and mobility changes during the COVID-19 period. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.

Identification of new cases of tuberculosis during the COVID-19 pandemic using model strategic management

Introduction: Tuberculosis (TB) is a world health problem that causes the third-largest death after cardiovascular and respiratory diseases. One of the causes of transmission of environmental factors is controlling the mobilization of individuals suffering from tuberculosis. This research aims to develop a strategic model of finding new TB cases based on region. Method: This descriptive research utilizes primary and secondary data. Variables consist of geographical and demographic characteristics, resources, tuberculosis prevalence, and indicators of tuberculosis response processes. The TB management planning model document is based on the experiences of health centers Perak Timur, Wonokromo, and Siwalankerto in maximizing TB case detection. Results: The management model outlines policies and procedures based on the variables. For example, the detection of new TB patients became a priority at the health center in Perak Timur. In contrast, the health center in Wonokromo focused on developing cadres and private practice physicians. Conclusions: This research provides an overview of the aspects that need attention and improvement by discovering different new cases in each region.

Trends and factors associated with daily number of new cases of COVID-19 in the early stage of the pandemic: A worldwide opendata study

Objectives: We analyzed global trends in the daily number of new cases during the first wave of COVID-19 and factors associated with these trends. Methods: Data from 151 countries were analyzed. The index date for each country was set with consideration for a 7-day moving average (MA7) of ≥100 people. Data were collected for 60 and 90 days from the index date. Time-series hierarchical clustering was used to analyze the trends in the number of new cases in each country on the basis of their MA7 values. Multinomial logistic regression was performed to identify factors associated with these trends. Results: The trends in the daily number of new cases in the early stage of COVID-19 were classified into growth, declines, and smooth declines. The number of cases in countries with ≥25.60% residents with obesity (odds ratio = 6.69;p = 0.004) was more likely to exhibit growth than were those with obesity of 9.60-20.79%. The number in countries with a GDP of ≥US$34,341 (odds ratio = 0.10;p = 0.001) was more likely to exhibit a decline than were those with a GDP of US$5,277–14,932. Conclusions: COVID-19 epidemic prevention policies should account for country-specific characteristics such as the proportion of residents with obesity and GDP. © 2022, Taiwan Public Health Association. All rights reserved.

Trends and factors associated with daily number of new cases of COVID-19 in the early stage of the pandemic: A worldwide opendata study

Objectives: We analyzed global trends in the daily number of new cases during the first wave of COVID-19 and factors associated with these trends. Methods: Data from 151 countries were analyzed. The index date for each country was set with consideration for a 7-day moving average (MA7) of ≥100 people. Data were collected for 60 and 90 days from the index date. Time-series hierarchical clustering was used to analyze the trends in the number of new cases in each country on the basis of their MA7 values. Multinomial logistic regression was performed to identify factors associated with these trends. Results: The trends in the daily number of new cases in the early stage of COVID-19 were classified into growth, declines, and smooth declines. The number of cases in countries with ≥25.60% residents with obesity (odds ratio = 6.69;p = 0.004) was more likely to exhibit growth than were those with obesity of 9.60-20.79%. The number in countries with a GDP of ≥US$34,341 (odds ratio = 0.10;p = 0.001) was more likely to exhibit a decline than were those with a GDP of US$5,277–14,932. Conclusions: COVID-19 epidemic prevention policies should account for country-specific characteristics such as the proportion of residents with obesity and GDP. © 2022, Taiwan Public Health Association. All rights reserved.

COVID-19 pandemic: a health challenge for commoners during the first unlock phase in India.

AIM: COVID-19, the disease caused by the novel coronavirus, is now a worldwide pandemic. This disease has become a reason for disturbance and concern. India, as a densely populated country, took initiative after the pandemic was declared. The objective of this study was to determine the mortality and recovery rates at 30 days from the first unlock phase after five phases of lockdown. The the number of infected people has continually increased, and currently, this pandemic continues to present challenges to public health. SUBJECT AND METHODS: Statistical analysis was used to calculate the mortality rate, ratio between active and death cases, active cases and recovered cases, recovered and death cases in India during the first 30 days of the unlock phase. RESULTS: The relationship between the new cases, deaths and recovered cases, shows that the new and recovered cases increased progressively. From the scatter plot of daily deaths and new cases, the R2 value is 0.0047. That means the death ratio is low against the new cases. Also, if we look at another scatter plot, the ratio between new cases and recovery rate shows the R2 value is 0.8015. That means the recovery rate was very high during the study period in India. The R2 value of daily recovery and death is 0.0072. India faced a huge number of new coronavirus cases and increased death rate every day during the first unlock phase. CONCLUSION: There was not the same condition as in the preliminary stage. The affected graphs progressively increased, and the government is fighting to control this deadly infection. Central and state governments are working together to combat this pandemic.

Association Rules Extraction From the Coronavirus Disease 2019: Attributes on Morbidity and Mortality

This research was aimed to extract association rules on the morbidity and mortality of corona virus disease 2019 (COVID-19). The dataset has four attributes that determine morbidity and mortality;including Confirmed Cases, New Cases, Deaths, and New Deaths. The dataset was obtained as of 2nd April, 2020 from the WHO website and converted to transaction format. The Apriori algorithm was then deployed to extract association rules on these attributes. Six rules were extracted: Rule 1. {Deaths, NewDeaths}=>{NewCases}, Rule 2. {ConfCases, NewDeaths}=>{NewCases}, Rule 3. {ConfCases, Deaths}=>{NewCases}, Rule 4. {Deaths, NewCases}=>{NewDeaths}, Rule 5. {ConfCases, Deaths}=>{NewDeaths}, Rule 6. {ConfCases, NewCases}=>{NewDeaths}, with confidence 0.96, 0.96, 0.86, 0.66, 0.59, 0.51 respectively. These rules provide useful information that is vital on how to curtail further spread and deaths from the virus, both in areas where the pandemic is already ravaging and in areas yet to experience the outbreak.

Effect of daily new cases of COVID-19 on public sentiment and concern: Deep learning-based sentiment classification and semantic network analysis.

Aim: This study explored the influence of daily new case videos posted by public health agencies (PHAs) on TikTok in the context of COVID-19 normalization, as well as public sentiment and concerns. Five different stages were used, based on the Crisis and Emergency Risk Communication model, amidst the 2022 Shanghai lockdown. Subject and Methods: After dividing the duration of the 2022 Shanghai lockdown into stages, we crawled all the user comments of videos posted by Healthy China on TikTok with the theme of daily new cases based on these five stages. Third, we constructed the pre-training model, ERNIE, to classify the sentiment of user comments. Finally, we performed semantic network analyses based on the sentiment classification results. Results: First, the high cost of fighting the epidemic during the 2022 Shanghai lockdown was why ordinary people were reluctant to cooperate with the anti-epidemic policy in the pre-crisis stage. Second, Shanghai unilaterally revised the definition of asymptomatic patients led to an escalation of risk levels and control conditions in other regions, ultimately affecting the lives and work of ordinary people in the area during the initial event stage. Third, the public reported specific details that affected their lives due to the long-term resistance to the epidemic in the maintenance stage. Fourth, the public became bored with videos regarding daily new cases in the resolution stage. Finally, the main reason for the negative public sentiment was that the local government did not follow the central government's anti-epidemic policy. Conclusion: Our results suggest that the methodology used in this study is feasible. Furthermore, our findings will help the Chinese government or PHAs improve the possible behaviors that displease the public in the anti-epidemic process.

The race Against Time to Save Human Lives During the COVID-19 With Vaccines: Global Evidence.

Voluminous vaccine campaigns have been used globally, since the COVID-19 pandemic has brought devastating mortality and destructively unprecedented consequences to different aspects of economies. This study aimed to identify how the numbers of new deaths and new cases per million changed after half of the population had been vaccinated. This paper used actual pandemic consequence variables (death and infected rates) together with vaccination uptake rates from 127 countries to shed new light on the efficacy of COVID-19 vaccines. The 50% uptake rate was chosen as the threshold to estimate the real benefits of vaccination campaigns for reducing COVID-19 infection and death cases using the difference-in-differences (DiD) imputation estimator. In addition, a number of control variables, such as government interventions and people's mobility patterns during the pandemic, were also included in the study. The number of new deaths per million significantly decreased after half of the population was vaccinated, but the number of new cases did not change significantly. We found that the effects were more pronounced in Europe and North America than in other continents. Our results remain robust after using other proxies and testing the sensitivity of the vaccinated proportion. We show the causal evidence of significantly lower death rates in countries where half of the population is vaccinated globally. This paper expresses the importance of vaccine campaigns in saving human lives during the COVID-19 pandemic, and its results can be used to communicate the benefits of vaccines and to fight vaccine hesitancy.

Prediction New Cases of COVID-19 in Indonesia Using Vector Autoregression (VAR) and Long-Short Term Memory (LSTM) Methods

The addition of Covid-19 cases is still uncontrolled, especially in Indonesia. Often the addition of Covid-19 cases in Indonesia always experiences a significant upward trend after a slightly loose government policy. This is because the government does not think there will be a spike in cases after cases go down. This is where the importance of predicting new cases of Covid-19 in Indonesia to be a reference for the government in taking policy. With deep learning, the prediction results will be more accurate. The implementation of vector autoregression (VAR) and long-short term memory (LSTM) methods can reach an accretion rate of up to 98%. With this method, the prediction results can be used for the government in anticipating if there is a surge in new cases per day because it has been predicted from the beginning. In fact, this method can predict new cases for up to a year. © 2021 IEEE.

Application of probabilistic models for extreme values to the COVID-2019 epidemic daily dataset.

Worldwide, COVID-19 coronavirus disease is spreading rapidly in a second and third wave of infections. In this context of increasing infections, it is critical to know the probability of a specific number of cases being reported. We collated data on new daily confirmed cases of COVID-19 breakouts in: Argentina, Brazil, China, Colombia, France, Germany, India, Indonesia, Iran, Italy, Mexico, Poland, Russia, Spain, U.K., and the United States, from the 20th of January, 2020 to 28th of August 2021. A selected sample of almost ten thousand data is used to validate the proposed models. Generalized Extreme-Value Distribution Type 1-Gumbel and Exponential (1, 2 parameters) models were introduced to analyze the probability of new daily confirmed cases. The data presented in this document for each country provide the daily probability of rate incidence. In addition, the frequencies of historical events expressed as a return period in days of the complete data set is provided.

Prediction of COVID-19 New Cases Using Multiple Linear Regression Model Based on May to June 2020 Data in Ethiopia

The aims of this study was to predict COVID-19 new cases using multiple linear regression model based on May to June 2020 data in Ethiopia. The COVID-19 cases data was collected from the Ethiopia Ministry of Health Organization Facebook page. Pearson's correlation analysis and linear regression model were used in the study. And, the COVID-19 new cases was positively correlated with the number of days, daily laboratory tests, new cases of males, new cases of females, new cases from Addis Ababa city, and new cases from foreign natives. In the multiple linear regression model, COVID-19 new cases was significantly predicted by the number of days at 5%, the number of daily laboratory tests at 10%, and the number of new cases from Addis Ababa city at 1% levels of significance. Then, the researchers recommended that Ethiopian Government, Ministry of Health, and Addis Ababa city administrative should give more awareness and protections for societies, and they should open again more COVID-19 laboratory testing centers. And, this study will help the government and doctors in preparing their plans for the next times.

Time series forecasting of new cases and new deaths rate for COVID-19 using deep learning methods.

The first known case of Coronavirus disease 2019 (COVID-19) was identified in December 2019. It has spread worldwide, leading to an ongoing pandemic, imposed restrictions and costs to many countries. Predicting the number of new cases and deaths during this period can be a useful step in predicting the costs and facilities required in the future. The purpose of this study is to predict new cases and deaths rate one, three and seven-day ahead during the next 100 days. The motivation for predicting every n days (instead of just every day) is the investigation of the possibility of computational cost reduction and still achieving reasonable performance. Such a scenario may be encountered in real-time forecasting of time series. Six different deep learning methods are examined on the data adopted from the WHO website. Three methods are LSTM, Convolutional LSTM, and GRU. The bidirectional extension is then considered for each method to forecast the rate of new cases and new deaths in Australia and Iran countries. This study is novel as it carries out a comprehensive evaluation of the aforementioned three deep learning methods and their bidirectional extensions to perform prediction on COVID-19 new cases and new death rate time series. To the best of our knowledge, this is the first time that Bi-GRU and Bi-Conv-LSTM models are used for prediction on COVID-19 new cases and new deaths time series. The evaluation of the methods is presented in the form of graphs and Friedman statistical test. The results show that the bidirectional models have lower errors than other models. A several error evaluation metrics are presented to compare all models, and finally, the superiority of bidirectional methods is determined. This research could be useful for organisations working against COVID-19 and determining their long-term plans.

Social distance capacity to control the COVID-19 pandemic: A systematic review on time series analysis.

BACKGROUND: Reducing interpersonal contact has been one of the least expensive and most widely used COVID-19 control strategies. OBJECTIVE: This systematic review has been conducted with the aim of identifying social distancing strategies and policies and their impact on the COVID-19 pandemic. METHODS: In order to compile this systematic review, Google Scholar, PubMed, Scopus, Web of Science, Science Direct, Magiran, SID, and Irandoc databases were searched from the COVID-19 outbreak until March 2021. Keywords included "social", "physical", "distance", "outbreak", "incidence", "prevalence", "spread", "new case", "death*", "mortality*", "morbidity*" , "covid-19", "coronavirus", "sars-cov-2" and "time series*". The articles were qualitatively evaluated by two researchers using the STROBE tool. Finally, the study data were divided into three conceptual categories by three researchers, who then agreed on one category. The practical suggestions were also categorized in the same way. RESULTS: The policies and strategies adopted to implement social distancing were included in five categories of restrictions, prohibitions, closures, incentives, and punishments. Transportation and travel restrictions, crowded places and schools closure, use of telecommunications and virtual communications, and financial and psychological support to society members were the main policies in this area. CONCLUSION: Rapid and complete vaccination of all people around the world is out of reach, therefore social distancing and the implementation of physical restraints, especially in crowded and densely populated environments, should be done extensively until COVID-19 is eradicated.

Positive Correlation Between General Public Knowledge and Attitudes Regarding COVID-19 Outbreak 1 Month After First Cases Reported in Indonesia.

The increasing number cases of coronavirus disease (COVID-19) infections in the general population in Indonesia raises questions concerning the public's knowledge and attitudes regarding this pandemic. To determine the correlation between the general public's knowledge and attitudes regarding the COVID-19 outbreak 1 month after the first cases were reported in Indonesia. This cross-sectional study was conducted between early March and the end of April 2020 in the general population of Indonesia, beginning with the North Sumatra region, where the spread of COVID-19 in Indonesia began. Questionnaires were randomly distributed online in the red zone in Indonesia. Data were collected by collecting people's responses to the questionnaire, which were distributed via WhatsApp (WA) application and were competed independently by the participants. A descriptive analysis was conducted to describe the demographic characteristics, knowledge, and attitudes of the general population. A total of 201 people had good knowledge (98%) and a positive attitude (96%) regarding the COVID-19 pandemic. The respondents had a negative attitude in relation to two aspects of the COVID-19 outbreak: having to always maintain a distance of 1.5 m when in crowds, and not being able to regularly exercise or eat nutritious food (78.6% and 79.1%, respectively). Most people in Indonesia have good knowledge and a positive attitude regarding the COVID-19 pandemic. However, negative attitudes were still found in this study, and as a result, transmission prevention measures cannot reach their maximum effectiveness by simply publicizing the increase in day-to-day cases to the general public.

Impact of Systematic Factors on the Outbreak Outcomes of the Novel COVID-19 Disease in China: Factor Analysis Study.

BACKGROUND: The novel COVID-19 disease has spread worldwide, resulting in a new pandemic. The Chinese government implemented strong intervention measures in the early stage of the epidemic, including strict travel bans and social distancing policies. Prioritizing the analysis of different contributing factors to outbreak outcomes is important for the precise prevention and control of infectious diseases. We proposed a novel framework for resolving this issue and applied it to data from China. OBJECTIVE: This study aimed to systematically identify national-level and city-level contributing factors to the control of COVID-19 in China. METHODS: Daily COVID-19 case data and related multidimensional data, including travel-related, medical, socioeconomic, environmental, and influenza-like illness factors, from 343 cities in China were collected. A correlation analysis and interpretable machine learning algorithm were used to evaluate the quantitative contribution of factors to new cases and COVID-19 growth rates during the epidemic period (ie, January 17 to February 29, 2020). RESULTS: Many factors correlated with the spread of COVID-19 in China. Travel-related population movement was the main contributing factor for new cases and COVID-19 growth rates in China, and its contributions were as high as 77% and 41%, respectively. There was a clear lag effect for travel-related factors (previous vs current week: new cases, 45% vs 32%; COVID-19 growth rates, 21% vs 20%). Travel from non-Wuhan regions was the single factor with the most significant impact on COVID-19 growth rates (contribution: new cases, 12%; COVID-19 growth rate, 26%), and its contribution could not be ignored. City flow, a measure of outbreak control strength, contributed 16% and 7% to new cases and COVID-19 growth rates, respectively. Socioeconomic factors also played important roles in COVID-19 growth rates in China (contribution, 28%). Other factors, including medical, environmental, and influenza-like illness factors, also contributed to new cases and COVID-19 growth rates in China. Based on our analysis of individual cities, compared to Beijing, population flow from Wuhan and internal flow within Wenzhou were driving factors for increasing the number of new cases in Wenzhou. For Chongqing, the main contributing factor for new cases was population flow from Hubei, beyond Wuhan. The high COVID-19 growth rates in Wenzhou were driven by population-related factors. CONCLUSIONS: Many factors contributed to the COVID-19 outbreak outcomes in China. The differential effects of various factors, including specific city-level factors, emphasize the importance of precise, targeted strategies for controlling the COVID-19 outbreak and future infectious disease outbreaks.

Analyzing the effect of duration on the daily new cases of COVID-19 infections and deaths using bivariate Poisson regression: a marginal conditional approach.

The whole world is devastated by the impact of the COVID-19 pandemic. The socioeconomic and other effects of COVID-19 on people are visible in all echelons of society. The main goal of countries is to stop the spreading of this pandemic by reducing the COVID-19 related new cases and deaths. In this paper, we analyzed the correlated count outcomes, daily new cases, and fatalities, and assessed the impact of some covariates by adopting a generalized bivariate Poisson model. There are different effects of duration on new cases and deaths in different countries. Also, the regional variation found to be different, and population density has a significant impact on outcomes.

COVID-19: update of the Italian situation.

On the March 11, 2020, the World Health Organization (WHO) declared the novel coronavirus disease 2019 (COVID-19) outbreak as a pandemic. The first cases in Italy were reported on January 30, 2020, and quickly the number of cases escalated. On March 20, 2020, according to the Italian National Institute of Health (ISS) and National Institute of Statistics (ISTAT), the peak of COVID-19 cases reported in Italy reached the highest number, surpassing those in China. The Italian government endorsed progressively restrictive measures initially at the local level, and finally, at the national level with a lockdown of the entire Italian territory up to 3 May 2020. The complete Italian territory closing slowed down the contagion. This review retraces the main numbers of the pandemic in Italy. Although in decline, the new reported cases remain high in the northern regions. Since drugs or vaccines are still not available, the described framework highlights the importance of the containment measures to be able to quickly identify all the potential transmission hotspots and keep control subsequent epidemic waves of COVID-19.

ARIMA modelling and forecasting of irregularly patterned COVID-19 outbreaks using Japanese and South Korean data.

The World Health Organization (WHO) upgraded the status of the coronavirus disease 2019 (COVID-19) outbreak from epidemic to global pandemic on March 11, 2020. Various mathematical and statistical models have been proposed to predict the spread of COVID-2019 [1]. We collated data on daily new confirmed cases of the COVID-19 outbreaks in Japan and South Korea from January 20, 2020 to April 26, 2020. Auto Regressive Integrated Moving Average (ARIMA) model were introduced to analyze two data sets and predict the daily new confirmed cases for the 7-day period from April 27, 2020 to May 3, 2020. Also, the forecasting results and both data sets are provided.

An updated analysis of turning point, duration and attack rate of COVID-19 outbreaks in major Western countries with data of daily new cases.

As coronavirus spreads around the world, the study of its effects is of great practical significance. We collated data on daily new cases of the COVID-19 outbreaks in the six Western countries of the Group of Seven and the dates of governments' interventions. We studied the periods before and after the dates of major governments' interventions integrally based on a segmented Poisson model. The relevant results are published in the paper of "Predicting turning point, duration and attack rate of COVID - 19 outbreaks in major Western countries" [1]. Our method can be used to update prediction daily as COVID-19 outbreaks evolve. In this article, we illustrate an updated analysis with our method to facilitate reproducibility. Both datasets used and updated are provided.