Covid-19 epidemic continues to escalate globally posing life threats to humans. Time series modeling plays a key role for the prediction of data-driven scenarios. A case for Covid-19 pandemic future numbers occurrence is one of the open forecasting scenario for application of the time series modeling. We applied the Autoregressive Integrated Moving Average (ARIMA) model to forecast the possible numbers of Covid-19 deaths in the Republic of South Africa using the previously reported data for a period of 17 months (May 2020 to September 2021). We adapted the Box-Jenkins’ methodology to step-by-step achieve the entire forecasting process. We identified the MA(1) (ARIMA(0,0,1)) as the best model based on the Akaike Information Criterion and the Bayesian Information Criterion. The forecasting done at 95% confidence interval for a period of 7 months (October 1, 2021 to April 31, 2022) indicated that the Covid-19 associated deaths in South Africa would slightly increase during the month of October 2021 but remain constant throughout the entire prediction period.
The manufacturing sector is considered a pivotal contributor to the growth of the economy around the globe. Kenya relies on the manufacturing sector to generate revenue and ultimately enhance the growth of the economy. Despite the key purpose played by these sectors in the economy, inflation rate has diversely affected their performance. The purpose of the study was to develop the Autoregressive Integrated Moving Average time series model to forecast the inflation rate in Kenya. The analysis utilized secondary data from the Kenya National Bureau of Statistics and the model was fitted to the data using R. The ARIMA with the information criterion of 576.24 was identified as the best model. Based on the forecasting, it was established that there will be a slight shift in the inflation in the coming years. Therefore, the government should use wage and price control to fight inflation but put in place policies to prevent recession and job loss in the country. The government should also employ contractionary monetary policy to fight inflation by reducing the money supply in the economy through decreases bond prices and increased interest rates. Implementation of these recommendations might assist in reducing the rate of inflation in the country.
This study aims to evaluate modelling factors affecting lung capacity using linear Regression model. The study employed multiple regression models which were used to fit the factors affecting lung capacity. The factors affect lung capacity includes the following; age, gender, smoking and height. The objectives of the study were; fitting regression model on factors affecting lung capacity, determining the relationship between age and height with lung capacity. The study aim also includes predicting the value of lung capacity using the fitted model. The data used in this study was a secondary source which was obtained from Marin [1]. The dataset is publicly available on their website. The data had 725 observations. Since multiple linear regression model was employed in this study, the model was of the form; Where; Lung capacity is the dependent variable, are the coefficients (parameters) to be estimated, Age and Height are the independent variables while is the random error component. The methods of parameter estimation discussed under this study include; maximum likelihood estimator and the least square estimator. The data for this study were analyzed using SPSS and R software which are statistical software used for data analysis. From the analysis of variance table, a p-value of 0.00 was recorded which is less than alpha (alpha= 0.05). This implies that the overall model is significant. From the model formulated, it was concluded that height and age greatly affect lung capacity. The model formulated can be used to predict the value of lung capacity provided the values of Age and Height are known. Also from the descriptive statistics, it is deduced that gender and smoking greatly affect lung capacity.
Since the outbreak of the COVID-19 pandemic, many countries have continued to suffer economically due to trade losses. COVID-19 has evolved into different forms and hence became a problem to analyze its transmission. As a result of increased COVID-19 infections, there has been a scarcity of resources like hospital facilities, quarantine centers, and personal protective equipment (PPEs) for the medics. Therefore, accurate planning has to be made by the government of Kenya to ensure that resources are made available to combat the rising COVID-19 cases. To ensure effective future planning for the COVID-19 pandemic, proper analysis of the COVID-19 pandemic among the population is key. Therefore, this study will go a long way in providing insights on how to plan for the Kenyan population through probabilistic analysis of the COVID-19 pandemic using the Markov chain. The study used Secondary Cumulative data from the Kenya ministry of health for a period between 1st June 2021 and 1st May 2022. The data was analyzed using a steady-state Markov chain in which the transition probability matrix for the COVID-19 pandemic was computed. The number of individuals infected by the COVID-19 virus and who recovered at the end of the study period was set at zero since COVID-19 disease is not curable. The results were presented in the table and reported at a 95% confidence level. Based on the findings, the study concluded that a steady-state Markov chain is beneficial in simulating the coronavirus infection in numerous stages. Also, it is noted that the use of the steady-state Markov chain model allows for capturing short and long-term memory effects that greatly improve the estimation of the number of new cases of COVID-19 and indicate whether the disease has an upward/downward trend.
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