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Regression Models for Predictions of COVID-19 New Cases and New Deaths in Ethiopia
Issue:
Volume 6, Issue 5, October 2020
Pages:
53-63
Received:
18 August 2020
Accepted:
8 September 2020
Published:
8 December 2020
Abstract: As the 15 of June 2020, we have 7,984,067 total COVID-19 cases, globally and 435,181 total deaths. Ethiopia was ranked 2nd and 15th in the table by 176 new cases and by 3,521 total new cases from African countries. Then, this study aimed to predict COVID-19 new cases and new deaths based on May/June data in Ethiopia using regression model. In this study, I used Pearson’s correlation analysis and the linear regression model to predict COVID-19 new cases and new deaths based on the available data from 12th May to 10th June 2020 in Ethiopia. There was a significant positive correlation between COVID-19 new cases and new deaths with different related variables. In the regression models, the simple linear regression model was a better fit the data of COVID-19 new cases and new deaths than as compared with quadratic and cubic regression models. In the multiple linear regression model, variables such as the number of days, the number of new laboratory tests, and the number of new cases from AA city significantly predicted the COVID-19 new cases. In this model, the number of days and new recoveries significantly predicted new deaths of COVID-19. The number of days, daily laboratory tests, and new cases from Addis Ababa city significantly predicted new COVID-19 cases, and the number of days and new recoveries significantly predicted new deaths from COVID-19. According to this analysis, if strong preventions and action are not taken in the country, the predicted values of COVID-19 new cases and new deaths will be 590 and 12 after two months (after 9th of August) from now, respectively. The researcher recommended that the Ethiopia government, Ministry of Health and Addis Ababa city administrative should give more awareness and protections for societies, and they should also open more COVID-19 laboratory testing centers. Generally, the obtained results of this study may help Ethiopian decision-makers put short-term future plans to face this epidemic.
Abstract: As the 15 of June 2020, we have 7,984,067 total COVID-19 cases, globally and 435,181 total deaths. Ethiopia was ranked 2nd and 15th in the table by 176 new cases and by 3,521 total new cases from African countries. Then, this study aimed to predict COVID-19 new cases and new deaths based on May/June data in Ethiopia using regression model. In this ...
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Determinants of Household Food Insecurity in Rural Ethiopia: Multiple Linear Regression (Classical and Bayesian Approaches)
Issue:
Volume 6, Issue 5, October 2020
Pages:
64-75
Received:
22 October 2020
Accepted:
16 November 2020
Published:
11 December 2020
Abstract: This paper examined the determinants of food insecurity among rural households in Ethiopia using data obtained from Households Consumption and Expenditure (HCE) and Welfare Monitoring (WMS) Survey conducted in 2011 by Central Statistical Agency (CSA). Bayesian multiple liner regression analysis was employed to identify determinant factors of rural household’s food insecurity, diet quality. The study revealed that the diet quality measure for rural households was obtained to be 68% who food secured and 32% who food in secured. The results of the analysis show that the variables, educational level of head of households, annual per capita expenditure of a households, farm land size of a households, number of oxen owned by the farm households, distance to input source, age of the households head, household size, gender of head of household, participating in off-farm activities, production storage and shocks such as: price rice of food items, flood, drought and illness were found to be the most important determinants of households food insecurity. Accordingly, the study suggests that a judicious combination of interventions that enhance income diversification opportunities in rural areas through promoting off-farm activities, family planning, and education, training and extension services could help enhance household food security. Provision of awareness creation on better and productive utilization of such resources as production storage should also be emphasized in rural areas. Generally improvements in fourteen predictor variables have the potential to increase the number of food secured households in rural households of Ethiopia.
Abstract: This paper examined the determinants of food insecurity among rural households in Ethiopia using data obtained from Households Consumption and Expenditure (HCE) and Welfare Monitoring (WMS) Survey conducted in 2011 by Central Statistical Agency (CSA). Bayesian multiple liner regression analysis was employed to identify determinant factors of rural ...
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Time Series Analysis of Monthly Average Temperature and Rainfall Using Seasonal ARIMA Model (in Case of Ambo Area, Ethiopia)
Teshome Hailemeskel Abebe
Issue:
Volume 6, Issue 5, October 2020
Pages:
76-87
Received:
27 October 2020
Accepted:
5 November 2020
Published:
11 December 2020
Abstract: Forecasting mean temperature and rainfall is an important for planning and formulating agricultural strategies. Thus, this paper, try to analyze and forecast monthly mean temperature and rainfall in Ambo area on the data from January 2012 to March 2019. From graphical analysis on time plot and ACF, the series seems to have a seasonal component. For that purpose, a Seasonal Autoregressive Integrated Moving Average (SARIMA) models were used to estimate and forecast the average monthly temperature and rainfall in the Ambo area, Ethiopia. Among the competitive tentative model, SARIMA (2, 0, 1) (2, 0, 1) 12 and SARIMA (1, 0, 1) (1, 0, 1) 12 model are the best time series model for fitting and forecasting mean temperature and rainfall, respectively. Moreover, the model diagnostic test on the residuals of SARIMA (2, 0, 1) (2, 0, 1) 12 and SARIMA (1, 0, 1) (1, 0, 1) 12 on mean temperature and rainfall satisfies the randomness, independency, normality and constant variance (homoscedasticity) assumptions. Finally, SARIMA (2, 0, 1) (2, 0, 1) 12 and SARIMA (1, 0, 1) (1, 0, 1) 12 were used to forecast mean of monthly temperature and rainfall from the period April 2019 to March 2023.
Abstract: Forecasting mean temperature and rainfall is an important for planning and formulating agricultural strategies. Thus, this paper, try to analyze and forecast monthly mean temperature and rainfall in Ambo area on the data from January 2012 to March 2019. From graphical analysis on time plot and ACF, the series seems to have a seasonal component. For...
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