Modeling recurrence of COVID-19 and its variants using recurrent neural network

Coronavirus disease 19 (COVID-19), a disease caused by severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2), began as the flu and gradually developed into a highly infectious global pandemic leading to the death of over 6 million people in about 200 countries of the world. Its pathogenic nature has qualified it as a deadly disease, causing moderate and severe respiratory difficulty in infected individuals with the ability to mutate into different variants of the first version. As a result, different government agencies and health institutions have sought solutions within and outside the clinical space. This paper models COVID-19 possible recurrence as variants and predicts that the subsequent waves will be more severe than the first wave. Long short-term memory network (LSTM) was used to predict the future occurrence of COVID-19 and forecast the virus's pattern. Machine evaluation was performed using precision, recall, F1-score, an area under the curve (AUC), and accuracy evaluation metrics. Datasets obtained were used to test the data. The collected characteristics were passed on to the system classification network, demonstrating the function's value based on the system's accuracy. The results showed that the COVID-19 variants have a higher disastrous effect within three months after the first wave. © 2023, Institute of Advanced Engineering and Science. All rights reserved.

COVID-19 Face Mask Detection Using CNN and Transfer Learning

The trauma produced by the COVID-19 sickness, which was proclaimed by the World Health Organization (WHO) in 2020, has impacted the entire world. WHO has recommended several recommendations and precautions to effectively prevent the spread of the deadly disease, including social distance, hand sanitizer, and the use of a face mask or face shield. Most particularly in crowded settings, which is what inspired this investigation into one of the WHO recommended preventive measures, namely the use of a face mask. This research used a Convolutional Neural Network and a Transfer Learning Model to determine whether or not a citizen wears a mask. This suggested model is trained and tested on the Face Masked Dataset, then image augmentation on limited available data for improved training and testing, with a 98 percent accuracy rate during training and testing. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Predicting the Spread of COVID-19 in Africa Using Facebook Prophet and Polynomial Regression

The COVID-19 pandemic is a noisy disease and a deadly one that has got the whole world’s attention. This deadly disease led to the whole world’s total lockdown for months before necessary measures were put in place for those who could not go out. Measures like regular hand washing, sanitizer, nose or face covering, social distances, and the like. This pandemic was first discovered in China and later in other parts of the world too. This study looked into the spread of COVID-19 in Africa using the US COVID-19 dataset, where data was extracted for analysis and prediction using Polynomial Regression. The results were further compared using a Facebook prophet. But at the end of the prediction, polynomial regression has the lowest Relative Mean Absolute Error (RMSE), which is now the model used for predicting the spread of COVID-19 in Africa. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Predicting the Pandemic Effect of COVID 19 on the Nigeria Economic, Crude Oil as a Measure Parameter Using Machine Learning

The spread of the Covid-19 pandemic in Nigeria has had a great impact on the economy. Prior to 2019, the crude oil price (US $/l) ranged from 62 to 79.59 starting in 2018. The year 2019 ranges between 59.1 to 73.65 per barrel. It will be between 14.28 and 66.65 per barrel in 2020. The Covid-19 pandemic will commence around December 2019 in China before being discovered outside China in the year 2020. Because of the aforementioned crude oil prices, crude oil prices increased until the year 2020, when there was a total lockdown on the export of oil products, causing crude oil prices to fall. This study investigates the impact of the pandemic on the Nigerian economy, utilizing crude oil as a good parameter measure. This was achieved by using a machine model (random tree) for prediction, and the results were further compared (Sequential Minimal Optimization (SMOreg), decision table, Random Forest, M5 tree model (M5P) and Gaussian Processes) for better accuracy. The results obtained thus far from the combination of datasets used can be used to manage the Nigerian economy, particularly the crude oil industry, in search of ways to mitigate the damage caused by the pandemic. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.