scholarly journals Modeling and Forecasting of COVID-19 New Cases in the Top 10 Infected African Countries Using Regression and Time Series Models

2020 ◽  
Author(s):  
Alemayehu Argawu
Keyword(s):  
South Africa ◽  
Time Series ◽  
Regression Models ◽  
Study Finding ◽  
Exponential Smoothing ◽  
Time Series Models ◽  
African Countries ◽  
Mass Gatherings ◽  
Preparedness Planning ◽  
Best Fit

Background: COVID-19 total cases have reached 1,083,071 (83.5%) in the top 10 infected African countries (South Africa, Egypt, Morocco, Ethiopia, Nigeria, Algeria, Ghana, Kenya, Cameroon, and Cote-dIvoire) from Feb 14 to Sep 6, 2020. Then, this study aimed to model and forecast of COVID-19 new cases in these top 10 infected African countries. Methods: In this study, the COVID 19 new cases data have been modeled and forecasted using curve estimation regression and time series models for these top 10 infected African countries from Feb 14 to Sep 6, 2020. Results: From July to August, the prevalence of COVID-19 cumulative cases was declined in South Africa, Cote dʹIvoire, Egypt, Ghana, Cameron, Nigeria, and Algeria by 31%, 26%, 22%, 20%, 14%, 12%, and 4%, respectively. But, it was highly raised in Ethiopia and Morocco by 41%, and 38% in this period, respectively. In Kenya, it was raised only by 1%. In this study, the cubic regression models for the ln(COVID-19 new cases) data were relatively the best fit for Egypt, Ethiopia, Kenya, Morocco, Nigeria and South Africa. And, the quadratic regression models for the data were the best fit for Cameroon, Cote-dIvoire and Ghana. The Algeria data was followed the logarithmic regression model. In the time series analysis, the Algeria, Egypt, and South Africa COVID-19 new cases data have fitted the ARIMA (0,1,0), ARIMA (0,1,0), and ARIMA (0,1,14) models, respectively. The Cameroon, Cote-dIvoire, Ghana, and Nigeria data have fitted the simple exponential smoothing models. The Ethiopia, Kenya, and Morocco data have followed the Damped trend, Holt, and Brown exponential smoothing models, respectively. In the analysis, the trends of COVID-19 new cases will be declined for Algeria and Ethiopia, and the trends will be constantan for Cameroon, Cote-dIvoire, Ghana and Nigeria. But, it will be raised slightly for Egypt and Kenya, and significantly for Morocco and South Africa from September 7 to October 6, 2020. Conclusion: This study was conducted with the current measures; the forecasts and trends obtained may differ from the number of cases that occur in the future. Thus, the study finding should be useful in preparedness planning against further spread of the COVID-19 epidemic in African countries. And, the researcher recommended that as many countries continue to relax restrictions on movement and mass gatherings, and more are opening their airspaces, and the countries other public and private sectors are reopening. So, strong appropriate public health and social measures must be instituted on the grounds again.

scholarly journals Modeling and Forecasting of COVID-19 New Cases in Top 10 Infected African Countries 

2020 ◽  
Author(s):  
Alemayehu Siffir Argawu
Keyword(s):  
South Africa ◽  
Time Series ◽  
Cote D'ivoire ◽  
Regression Models ◽  
Côte D’Ivoire ◽  
Exponential Smoothing ◽  
African Countries ◽  
Cote D’Ivoire ◽  
Côte D'ivoire ◽  
Best Fit

Abstract Background: COVID-19 total cases have reached 1,083,071 (83.5%) in the top 10 infected African countries (South Africa, Egypt, Morocco, Ethiopia, Nigeria, Algeria, Ghana, Kenya, Cameroon, and Côte d’Ivoire) from Feb 14 to Sep 6, 2020. Then, this study aimed to model and forecast of COVID-19 new cases in these top 10 infected African countries.Methods: In this study, the COVID 19 new cases data have been modeled and forecast using curve estimation regression and time series models for these top 10 infected African countries from Feb 14 to Sep 6, 2020. Results: From July to August, the prevalence of COVID-19 cumulative cases was declined in South Africa, Côte d’Ivoire, Egypt, Ghana, Cameron, Nigeria, and Algeria by 31%, 26%, 22%, 20%, 14%, 12%, and 4%, respectively. But, it was highly raised in Ethiopia and Morocco by 41%, and 38% in this period, respectively. In Kenya, it was raised only by 1%. In this study, the cubic regression models for the ln(COVID-19 new cases) data were relatively the best fit for Egypt, Ethiopia, Kenya, Morocco, Nigeria, and South Africa. And, the quadratic regression models for the data were the best fit for Cameroon, Cote dʹIvoire, and Ghana. The Algeria data was followed by the logarithmic regression model. In the time series analysis, the Algeria, Egypt, and South Africa COVID-19 new cases data have fitted the ARIMA (0,1,0), ARIMA (0,1,0), and ARIMA (0,1,14) models, respectively. The Cameroon, Côte d’Ivoire, Ghana, and Nigeria data have fitted the simple exponential smoothing models. Ethiopia, Kenya, and Morocco data have followed the Damped trend, Holt, and Brown exponential smoothing models, respectively. In the analysis, the trends of COVID-19 new cases will be declined for Algeria and Ethiopia, and the trends will be constant for Cameroon, Côte d’Ivoire, Ghana, and Nigeria. But, it will be raised slightly for Egypt and Kenya, and significantly for Morocco and South Africa from September 7 to October 6, 2020.Conclusion: This study was conducted with the current measures; the forecasts and trends obtained may differ from the number of cases that occur in the future. Thus, the study finding should be useful in preparedness planning against the further spread of the COVID-19 epidemic in African countries. And, the researcher recommended that as many countries continue to relax restrictions on movement and mass gatherings, and more are opening their air spaces’, and the countries’ other public and private sectors are reopening. So, strong appropriate public health and social measures must be instituted on the grounds again.

scholarly journals Modeling and Short-Term Forecasts of Indicators for COVID-19 Outbreak in 25 Countries at the end of March

2020 ◽  
pp. 6-20
Author(s):  
Handan Ankaralı ◽  
Nadire Erarslan ◽  
Özge Pasin ◽  
Abu Kholdun Al Mahmood
Keyword(s):  
Time Series ◽  
Medical Science ◽  
Exponential Smoothing ◽  
Seasonal Effects ◽  
Time Series Models ◽  
Double Exponential ◽  
Wide Range ◽  
Simple Exponential Smoothing ◽  
Double Exponential Smoothing ◽  
Prediction And Forecasting

Objective: The coronavirus, which originated in Wuhan, causing the disease called COVID-19, spread more than 200 countries and continents end of the March. In this study, it was aimed to model the outbreak with different time series models and also predict the indicators. Materials and Methods: The data was collected from 25 countries which have different process at least 20 days. ARIMA(p,d,q), Simple Exponential Smoothing, Holt’s Two Parameter, Brown’s Double Exponential Smoothing Models were used. The prediction and forecasting values were obtained for the countries. Trends and seasonal effects were also evaluated. Results and Discussion: China has almost under control according to forecasting. The cumulative death prevalence in Italy and Spain will be the highest, followed by the Netherlands, France, England, China, Denmark, Belgium, Brazil and Sweden respectively as of the first week of April. The highest daily case prevalence was observed in Belgium, America, Canada, Poland, Ireland, Netherlands, France and Israel between 10% and 12%.The lowest rate was observed in China and South Korea. Turkey was one of the leading countries in terms of ranking these criteria. The prevalence of the new case and the recovered were higher in Spain than Italy. Conclusion: More accurate predictions for the future can be obtained using time series models with a wide range of data from different countries by modelling real time and retrospective data. Bangladesh Journal of Medical Science Vol.19(0) 2020 p.06-20

Trends in Melanoma Mortality in the Population Groups of South Africa

Dermatology ◽  
2019 ◽  
Vol 235 (5) ◽  
pp. 396-399 ◽  
Author(s):  
Caradee Yael Wright ◽  
Thandi Kapwata ◽  
Elvira Singh ◽  
Adele C. Green ◽  
Peter Baade ◽  
...  
Keyword(s):  
South Africa ◽  
Regression Models ◽  
Population Group ◽  
Mortality Rates ◽  
Mortality Data ◽  
African Countries ◽  
South Africans ◽  
Awareness Campaigns ◽  
Increasing Trend ◽  
Join Point

The incidence of cutaneous melanoma (CM) is increasing in countries around the world. However, little is known about melanoma trends in African countries by population group. We studied CM mortality in South Africa from 1997 to 2014 to partly address this knowledge gap. Unit record mortality data for all South Africans who died from CM (n = 8,537) were obtained from Statistics South Africa. Join-point regression models were used to assess whether there was a statistically significant change in the direction and/or magnitude of the annual trends in CM mortality. A significant increasing trend of 11% per year was observed in age-adjusted mortality rates in men between 2000 and 2005 (p < 0.01), rising from 2 to 3 per 100,000. There was also a statistically significant increase of 180% per year among White South Africans from 1997 to 1999 (p < 0.05) and of 3% from 1999 to 2014 (p < 0.01). These results may be used to inform CM awareness campaigns and will motivate efforts to improve the collection and analysis of relevant statistics regarding the present burden of CM in South Africa.

scholarly journals Empirical Evaluation of Alternative Time-Series Models for COVID-19 Forecasting in Saudi Arabia

2021 ◽  
Vol 18 (16) ◽  
pp. 8660
Author(s):  
Isra Al-Turaiki ◽  
Fahad Almutlaq ◽  
Hend Alrasheed ◽  
Norah Alballa
Keyword(s):  
Time Series ◽  
Saudi Arabia ◽  
Time Series Data ◽  
Arima Model ◽  
Empirical Evaluation ◽  
Cubic Spline ◽  
Exponential Smoothing ◽  
Series Data ◽  
Time Series Models ◽  
Forecasting Models

COVID-19 is a disease-causing coronavirus strain that emerged in December 2019 that led to an ongoing global pandemic. The ability to anticipate the pandemic’s path is critical. This is important in order to determine how to combat and track its spread. COVID-19 data is an example of time-series data where several methods can be applied for forecasting. Although various time-series forecasting models are available, it is difficult to draw broad theoretical conclusions regarding their relative merits. This paper presents an empirical evaluation of several time-series models for forecasting COVID-19 cases, recoveries, and deaths in Saudi Arabia. In particular, seven forecasting models were trained using autoregressive integrated moving average, TBATS, exponential smoothing, cubic spline, simple exponential smoothing Holt, and HoltWinters. The models were built using publicly available daily data of COVID-19 during the period of 24 March 2020 to 5 April 2021 reported in Saudi Arabia. The experimental results indicate that the ARIMA model had a smaller prediction error in forecasting confirmed cases, which is consistent with results reported in the literature, while cubic spline showed better predictions for recoveries and deaths. As more data become available, a fluctuation in the forecasting-accuracy metrics was observed, possibly due to abrupt changes in the data.

scholarly journals Demand For Electricity In Lebanon

2011 ◽  
Vol 8 (1) ◽  
Author(s):  
Salah Abosedra ◽  
Abdallah Dah ◽  
Sajal Ghosh
Keyword(s):  
Time Series ◽  
Exponential Smoothing ◽  
Time Span ◽  
Time Series Models ◽  
Detailed Data ◽  
Economic Variables ◽  
Univariate Time Series ◽  
Modeling Techniques ◽  
Socio Economic Variables ◽  
Future Demand

This paper estimates the demand for electricity in Lebanon by employing three modeling techniques namely OLS, ARIMA and exponential smoothing for the time span January 1995 to December 2005. In- sample forecasts reveal that the forecasts made by ARIMA (0,1,3) (1,0,0)12 is superior in terms of lowest RMSE, MSE and MAPE criteria, followed by exponential smoothing and OLS. Therefore, the planners in Lebanon could utilize linear univariate time-series models for forecasting future demand of electricity until detailed data on various socio-economic variables are available, which, in the future, may result in other modeling techniques being superior to estimate the demand for electricity in the country.

scholarly journals The Comparison between ARIMA and ARFIMA Model to Forecast Kijang Emas (Gold) Prices in Malaysia using MAE, RMSE and MAPE

2021 ◽  
Vol 6 (3) ◽  
pp. 22-33
Author(s):  
Atiqa Nur Azza Mahmad Azan ◽  
Nur Faizatul Auni Mohd Zulkifly Mototo ◽  
Pauline Jin Wee Mah
Keyword(s):  
Time Series ◽  
Arima Model ◽  
Time Series Models ◽  
Monthly Average ◽  
Arfima Models ◽  
Gold Market ◽  
Univariate Time Series ◽  
The World ◽  
Arfima Model ◽  
Best Fit

Gold is known as the most valuable commodity in the world because it is a universal currency recognized by every single bank across the globe. Thus, many people were interested in investing gold since gold market was always steadier compared to other investment (Khamis and Awang, 2020). However, the credibility of gold was questionable due to the changes in gold prices caused by a variety of circumstances (Henriksen, 2018). Hence, information on the inflation of gold prices were needed to understand the trend in order to plan for the future in accordance with international gold price standards. The aim of this study was to identify the trend of Kijang Emas monthly average prices in Malaysia from the year 2010 to 2021, to determine the best fit time series model for Kijang Emas prices in Malaysia and using univariate time series models to forecast Kijang Emas prices in Malaysia. The ARIMA and ARFIMA models were used in this study to model and forecast the prices of gold (Kijang Emas) in Malaysia. Each of the actual monthly Kijang Emas prices for 2021 were found to be within the 95% predicted intervals for both the ARIMA and ARFIMA models. The performances for each model were checked by considering the values of MAE, RMSE and MAPE. From the findings, all the MAE, RMSE and MAPE values showed that the ARFIMA model emerged as the better model in forecasting the Kijang Emas prices in Malaysia compared to the ARIMA model.

scholarly journals ECONOMETRIC ANALYSIS OF THE VOLUME OF MUTUAL TRADE OF THE EAEU MEMBER STATES

2021 ◽  
Author(s):  
Mariya Tsvil ◽  
Ella Guleva ◽  
Margarita Zubkova
Keyword(s):  
Time Series ◽  
Exponential Smoothing ◽  
Multiplicative Model ◽  
Econometric Analysis ◽  
Time Series Models ◽  
Member States ◽  
Quarterly Data ◽  
Mutual Trade

The article provides econometric time series models for the volumes of mutual trade of the EAEU member states based on quarterly data from the 1st quarter of 2017 to the 3rd quarter of 2021. An exponential smoothing model and a multiplicative model are built. Also, a forecast was made for the volume of mutual trade in the IV quarter of 2021

scholarly journals A note on Covid-19 Statistics, Strange trend and Forecasting of Total Cases in the most Infected African Countries: An ARIMA and Fuzzy Time Series Approaches

2020 ◽  
Vol 7 (2) ◽  
pp. 967-982
Author(s):  
Chellai Fatih ◽  
Ahmed Hamimes ◽  
Pradeep Mishra
Keyword(s):  
South Africa ◽  
Time Series ◽  
Fuzzy Time Series ◽  
Arima Models ◽  
Current Event ◽  
African Countries ◽  
Prevention And Intervention ◽  
Corona Virus ◽  
The World ◽  
Virus Spreading

The current event in the world is corona-virus; the spread of this virus can put all countries in situation of incapacity of how manage and face. This article focused on the class of ARIMA models and Fuzzy Time Series. The techniques are applied to trajectory Corona virus on three African countries: Algeria, Egypt and South Africa over the period (2020-02-15 /2020-03-19). Although the hyper stochastic of this pandemic, it is seen that ARIMA models fits well the trajectory of Covid-19. We predict a continuous trend of virus spreading in next days, a fact that alert the governments of theses countries and the whole African countries for further strengthen prevention and intervention policies to combat this epidemic

scholarly journals Forecasting Wheat Production Using Time Series Models in Pakistan

2019 ◽  
Vol 8 (2) ◽  
pp. 172-177
Author(s):  
M Asif Masood ◽  
Irum Raza ◽  
Saleem Abid
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Moving Average ◽  
Exponential Smoothing ◽  
Series Data ◽  
Time Series Models ◽  
Linear Quadratic ◽  
Wheat Production ◽  
S Curve ◽  
Double Exponential Smoothing

The present paper was designed to forecast wheat production for 2017-18, 2018-19 and 2019-2020 respectively by using time series data from 1971-72 to 2016-17 with best selected time series models. Linear, Quadratic, Exponential, S-Curve, Double Exponential Smoothing, Single exponential smoothing, Moving average and ARIMA were estimated for wheat production. The results showed a mix trend in production of wheat for selected time period. ARIMA (2,1,2) was found best one keeping in view close forecasts with actual reported wheat production. So the preference inclined towards the ARIMA (2,1,2) than quadratic to forecasts of wheat production.

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