scholarly journals Forecasting COVID-19 Confirmed Cases Using Empirical Data Analysis in Korea

Healthcare ◽  
2021 ◽  
Vol 9 (3) ◽  
pp. 254
Author(s):  
Da Hye Lee ◽  
Youn Su Kim ◽  
Young Youp Koh ◽  
Kwang Yoon Song ◽  
In Hong Chang
Keyword(s):  
Data Analysis ◽  
Empirical Data ◽  
Moving Average ◽  
Arima Model ◽  
Third Wave ◽  
Cumulative Number ◽  
Control And Prevention ◽  
Empirical Data Analysis ◽  
The Government ◽  
Increasing Trends

From November to December 2020, the third wave of COVID-19 cases in Korea is ongoing. The government increased Seoul’s social distancing to the 2.5 level, and the number of confirmed cases is increasing daily. Due to a shortage of hospital beds, treatment is difficult. Furthermore, gatherings at the end of the year and the beginning of next year are expected to worsen the effects. The purpose of this paper is to emphasize the importance of prediction timing rather than prediction of the number of confirmed cases. Thus, in this study, five groups were set according to minimum, maximum, and high variability. Through empirical data analysis, the groups were subdivided into a total of 19 cases. The cumulative number of COVID-19 confirmed cases is predicted using the auto regressive integrated moving average (ARIMA) model and compared with the actual number of confirmed cases. Through group and case-by-case prediction, forecasts can accurately determine decreasing and increasing trends. To prevent further spread of COVID-19, urgent and strong government restrictions are needed. This study will help the government and the Korea Disease Control and Prevention Agency (KDCA) to respond systematically to a future surge in confirmed cases.

scholarly journals Predicting the number of cases of dengue infection in Ribeirão Preto, São Paulo State, Brazil, using a SARIMA model

2011 ◽  
Vol 27 (9) ◽  
pp. 1809-1818 ◽  
Author(s):  
Edson Zangiacomi Martinez ◽  
Elisângela Aparecida Soares da Silva
Keyword(s):  
Moving Average ◽  
Dengue Infection ◽  
Arima Model ◽  
Sao Paulo ◽  
São Paulo ◽  
Incidence Data ◽  
Paulo State ◽  
Control And Prevention ◽  
São Paulo State ◽  
Predicted Values

This study aimed to develop a forecasting model for the incidence of dengue in Ribeirão Preto, São Paulo State, Brazil, using time series analysis. The model was performed using the Seasonal Autoregressive Integrated Moving Average (SARIMA). Firstly, we fitted a model considering monthly notifications of cases of dengue recorded from 2000 to 2008 in Ribeirão Preto. We then extracted predicted values for 2009 from the adjusted model and compared them with the number of cases observed for that year. The SARIMA (2,1,3)(1,1,1)12 model offered best fit for the dengue incidence data. The results showed that the seasonal ARIMA model predicts the number of dengue cases very effectively and reliably, and is a useful tool for disease control and prevention.

Introduction to Empirical Data Analysis

2021 ◽  
pp. 1-54
Author(s):  
Klaus Backhaus ◽  
Bernd Erichson ◽  
Sonja Gensler ◽  
Rolf Weiber ◽  
Thomas Weiber
Keyword(s):  
Data Analysis ◽  
Empirical Data ◽  
Empirical Data Analysis

scholarly journals Modelling the Impact of Government Policies on Import on Domestic Price of Indian Gold Using ARIMA Intervention Method

2016 ◽  
Vol 2016 ◽  
pp. 1-6 ◽  
Author(s):  
Jyothi Unnikrishnan ◽  
Kodakanallur Krishnaswamy Suresh
Keyword(s):  
Current Account Deficit ◽  
Moving Average ◽  
Arima Model ◽  
Trade Deficit ◽  
Government Policies ◽  
Suitable Model ◽  
Domestic Price ◽  
Gold Market ◽  
The Government ◽  
The Impact

The study attempts to determine the impact of government policies of import of gold in India on the domestic price of gold during 2013 using Autoregressive Integrated Moving Average (ARIMA) intervention model. 2013 was an amazing year for Indian gold market where the price had reached its zenith. In April 2013, to curb a record trade deficit, India imposed an import duty of 10 percent on gold and tied imports for domestic consumption to exports, creating scarce supply of the yellow metal and boosting premiums to curtail the Current Account Deficit (CAD). The objective of the paper is to model the impact of this intervention by the government on the domestic price of Indian gold. Suitable ARIMA model is fit on the preintervention period and thereafter the effects of the interventions are analysed. The results indicate that ARIMA(1,1,1)is the most suitable model during preintervention period. Intervention analysis reveals that there is significant decrease in domestic price of gold by 56% from 2013. The model may be used by policymakers to analyse the future of gold before framing regulations and policies.

scholarly journals Stance Taking in Social Media: the Analysis of the Comments About Us Presidential Candidates on Facebook and Twitter

Verbum ◽  
2018 ◽  
Vol 9 ◽  
pp. 21-30
Author(s):  
Roma Kriaučiūnienė ◽  
Jefferey La Roux ◽  
Miglė Lauciūtė
Keyword(s):  
Social Networks ◽  
Data Analysis ◽  
Empirical Data ◽  
Presidential Election ◽  
The United States ◽  
Online Environment ◽  
Presidential Candidates ◽  
Du Bois ◽  
Empirical Data Analysis ◽  
The Many

[full article and abstract in English] The subject of the paper is the analysis of the expression of stance taking in an online environment, mainly in the comments of users of social networks such as Facebook and Twitter about the presidential candidates of the American Presidential Election in 2016. The empirical data analysis was carried out following the ideas of J. W. Du Bois (2007), D. Barton & C. Lee (2013) and R. Englebretson (2007) on stance taking and J. W. Du Bois’ (2007) model of stance triangle, i.e. grouping instances of stance-taking into one of these groups: evaluation, affect or epistemicity, which served as the main framework of this study. The work of linguists D. Barton & C. Lee (2013) on the expression of stance-taking in an online environment were also taken into consideration. Having in mind the fact that stance identification is a challenging task , i.e. it could be implicitly as well as explicitly expressed and that it should be inferred from different modes of its expression and interpreted with reference to many contextual and intertextual factors, in the current analysis the authors focused on interpretation of linguistic as well as other multimodal means of the expression of stance that were used by users of social networks in their writing spaces on the topic of the Presidential Election in the United States in 2016. It should also be mentioned that the analysis presented in this article offers only one of the many possible interpretations of the data. Moreover, the current paper concentrates mainly on the presentation of the empirical data of the expression of affective stance. However, it should be indicated that in some cases stance types overlap, i.e. one instance could be treated as both taking an affective and an evaluative stance, as judgements and evaluation (i.e. evaluative stance) are often based on feelings (i.e. affective stance). The main source of the empirical data were the instances of stance taking taken from comments found on Donald Trump’s and Hillary Clinton’s verified Facebook and Twitter pages during their presidential campaigns in 2016. All in all, 147 examples of posts and comments from the social networks Facebook and Twitter were collected: 72 comments incorporating stance taking on Donald Trump‘s posts, and 75 comments including stance taking on Hillary Clinton‘s posts. The results of the empirical data analysis showed that the affective stance was expressed by linguistic as well as multimodal means.

scholarly journals Ethnic and Regional Identity in Polish Part of Spiš Region (On the Example of Jurgów Village)

2014 ◽  
Vol 14 (1) ◽  
pp. 78-94
Author(s):  
Vojtech Bagín
Keyword(s):  
Data Analysis ◽  
Social Interaction ◽  
Empirical Data ◽  
Local Population ◽  
Regional Identity ◽  
Empirical Data Analysis ◽  
Village Population

ABSTRACT In my study I am focusing on the issue of national and regional identity of Spiš region inhabitants. The researched Jurgów village belongs to territory situated on the frontier between two states and at the same time between two regions. In the study I analyze the question of stereotypes present in the locality concerning gorals from the neighbouring Podhale region and Slovak part of Spiš region. In addition to that I deal with analysis of importance and understanding of space and border for the identity of local population. On the basis of the empirical data analysis results I observe that the identity of the researched village population is shaped by the fact that it belongs to the frontier territory where crossborder cultural and social interaction occurs.

scholarly journals Prediction of COVID-19 Cases in Afghanistan Using ARIMA model

2021 ◽  
Author(s):  
Mujtaba Haidari
Keyword(s):  
Moving Average ◽  
Arima Model ◽  
Information Criterion ◽  
Information Criteria ◽  
Third Wave ◽  
Government Organizations ◽  
Level Of Confidence ◽  
Cent Level ◽  
Uncertain Future ◽  
Novel Coronavirus

Abstract In Afghanistan, the novel coronavirus disease 2019 (COVID-19) is spreading rapidly. Currently, we are in third wave of pandemic, in Afghanistan. And recently government of Afghanistan recorded the highest confirmed cases since the start of pandemic. In order to prepare ourselves and make right decision, we need to predict the future. Past information has been used to predict future, but we need to confess that no prediction can be as real future. The data have been used to predict is from twenty first March 2021 to fifteenth July 2021. The forecasting period is from sixteenth July 2021 for 40 days till twenty fourth August 2021. To examine stationarity of data, we used Augmented Dickey-Fuller unit-root. In this study autoregressive integrated moving average ARIMA model has been used to predict future COVID-19 cases. In order to find the best ARIMA parameters, we used Akaike’s Information Criterion (AIC) and Bayesian Information Criteria (BIC). The data which we have used in this study are taken from Ministry of Public Health. We found out that ARIMA (0, 2, 1) is best fit model. By using ARIMA (0, 2, 1), we predicted COVID-19 cases in 80 and 95 per cent level of confidence. In 95 (high) and 80 (high) per cent level of confidence COVID-19 positive cases reach 216,159 and 203,979 cases respectively. On the other hand, in 95 (low) and 80 (low) per cent level of confidence after a period of 40-day positive cases will reach 145,780 and 157,961 cases respectively. This study is the first study which predict COVID-19 cases in Afghanistan, so government of Afghanistan, non-government organizations (NGOs) and scholars can use it to plan and prepare themselves to confront uncertain future and protect people against this virus.

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