The statistic" Midpoint" used as a magnitude of observations in each class of a frequency table leads to biased estimates of the two measures of shape of a distribution, skewness, and kurtosis. This research proposed three new statistics, the mean, median, and midrange of observations in each class instead of the midpoint. Simulation results using samples from normal, uniform, exponential distributions, and the real Istanbul weather data indicated that measures that used the mean as a representative of observations in each class outperformed the other measures of skewness and kurtosis.
Background COVID-19 pandemic has indeed plunged the global community especially African countries into an alarming difficult situation culminating into a great deal amounts of catastrophes such as economic recession, political instability and loss of jobs. The pandemic spreads exponentially and causes loss of lives. Following the outbreak of the omicron new variant of concern, forecasting and identification of the COVID-19 infection cases is very vital for government at various levels. Hence, having knowledge of the spread at a particular point in time, swift actions can be taken by government at various levels with a view to accordingly formulate new policies and modalities towards minimizing the trajectory of the consequences of COVID-19 pandemic to both public health and economic sectors. Methods Here, a potent combination of Convolutional Neural Network (CNN) learning algorithm along with Long Short Term Memory (LSTM) learning algorithm has been proposed in this work in order to produce a hybrid of a deep learning algorithm Convolutional Neural Network -Long Short Term Memory (CNN-LSTM) for forecasting COVID-19 infection cases particularly in Nigeria, South Africa and Botswana. Forecasting models for COVID-19 infection cases in Nigeria, South Africa and Botswana, were developed for 10 days using deep learning-based approaches namely CNN, LSTM and CNN-LSTM deep learning algorithm respectively. Results The models were evaluated on the basis of four standard performance evaluation metrics which include accuracy, MSE, MAE and RMSE respectively. However, the CNN-LSTM deep learning-based forecasting model achieved the best accuracy of 98.30%, 97.60%, and 97.74% for Nigeria, South Africa and Botswana respectively; and in the same manner, achieved lesser MSE, MAE and RMSE values compared to models developed with CNN and LSTM respectively. Conclusions Taken together, the CNN-LSTM deep learning-based forecasting model for COVID-19 infection cases in Nigeria, South Africa and Botswana dramatically surpasses the two other DL based forecasting models (CNN and LSTM) for COVID-19 infection cases in Nigeria, South Africa and Botswana in terms of not only the best accuracy of with 98.30%, 97.60%, and 97.74% but also in terms of lesser MSE, MAE and RMSE.
The COVID-19 pandemic has posed a serious threat to the lives of many people across the globe. In Nigeria, the first COVID-19 case was reported on 27th February, 2020, after an Italian citizen tested positive for Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2), the causative agent of COVID-19. The spread of SARS-CoV-2 remained stable until 21st April, 2020, where the number of infections in three digits emerged and as of June 20th 2020, Nigeria has a total of 19,808 confirmed cases, total discharged 6,718 and 506 deaths recorded. The severity of SARS-CoV-2 by far surpasses the coronaviruses that were so far discovered in 2003 and 2012. In this study, a Susceptible Infectious and Recovered (SIR) model for severity of COVID-19 pandemic in Nigeria has been developed to obtain the overall picture of the severity of the disease in Nigeria. Based on the simulation of the fitted model, we arrived at a basic reproductive number (R0) 1.533931 and an infected individual required 9 days to recover from the disease. The findings indicated that the fitted model satisfactorily mimicked the actual data reported by the Nigeria Center for Disease Control (NCDC). The R0 obtained showed that on the average one infected individual would spread the COVID-19 to two susceptible individuals. Though the pandemic is under control, but the government needs to take measures to totally contain the spread of the virus.
One way to make sense of data is to organize it into a more meaningful format called frequency table. The existing univariate discrete frequency table is simple to construct, easy to understand and interpret. However, when the number of elements in the data is substantial, it results in a long table that can be difficult to handle. This article presents a new discrete frequency table. The proposed frequency table is described, using simulations performed on five different discrete distributions, and real data. The new frequency table improves the existing table in various ways. This includes how the data with a large number of elements can be handled, how the mode of the data can be better estimated, and how the essential features of the data can be better revealed.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.