ABSTRACT
This research explored the precision of diverse time-series models for COVID-19 epidemic detection in all the 36 different states and the Federal Capital Territory (FCT) in Nigeria with the maximum count of daily cumulative of confirmed, recovered, and death cases as of 4 November 2020. A 14step forecast system for active coronavirus cases was built, analyzed, and compared for six different deep learning-stimulated and statistical time-series models using two openly accessible datasets. The results obtained showed that based on RMSE metric, ARIMA model obtained the best values for four of the states (0.002537, 0.001969.12E-058, 5.36E-05 values for Lagos, FCT, Edo and Delta states, respectively). While no method is all-encompassing for predicting daily active coronavirus cases for different states in Nigeria, ARIMA model obtains the highest-ranking prediction performance and attained a good position results in other states.
ABSTRACT
This study assessed the knowledge and perception of Nigerians about COVID-19 vaccination. A cross-sectional survey was conducted comprising Health and Non-health workers in Nigeria. The knowledge, attitude, and perception of respondents on COVID-19 vaccination in Nigeria was obtained through an online. Logistic regression was employed to determine which factor imparted on COVID-19 vaccination decision. The study showed a significant relationship between COVID-19 vaccination and immigration requirements. The survey showed that 74.07% of the health workers had been vaccinated, while 47.06% of non-Health Workers had been vaccinated. This study recommends that Governments at all levels should create more awareness of the importance of COVID-19 vaccination to increase the number of vaccinated individuals.
ABSTRACT
A significant worldwide pandemic disease that has shut the whole world's economy and put the health care services personnel into anxiety is COronaVIrus Disease 2019 (COVID-19). It is difficult to model as it shared closely related characteristics/symptoms with other pneumonia diseases like SARS, MERS, ARDS, and Pulmonary Tuberculosis (PTB). Health practitioners use images (CT scan, Chest X-Ray (CXR)), timely occurrences (daily), audio (Cough), text (clinical and laboratory data) to detect, predict and treat patients with this disease. But machine learning has been proven by researchers when it can effectively and precisely detect, predict, classify, recommend treatment. This chapter discusses and implements a data classification task for early diagnosis and prognosis of the COVID-19 pandemic using CXR image. Classification is a supervised learning task that uses labeled data to assign items to different classes. The indicators that define a good classification task and assess classification models' performance are Receiver Operating Characteristic (ROC), Precision-Recall Curve (PRC), Recall, F1-Score Precision.