Machine Learning and Deep Learning Based Time Series Prediction and Forecasting of Ten Nations' COVID-19 Pandemic.

In the paper, the authors investigated and predicted the future environmental circumstances of a COVID-19 to minimize its effects using artificial intelligence techniques. The experimental investigation of COVID-19 instances has been performed in ten countries, including India, the United States, Russia, Argentina, Brazil, Colombia, Italy, Turkey, Germany, and France using machine learning, deep learning, and time series models. The confirmed, deceased, and recovered datasets from January 22, 2020, to May 29, 2021, of Novel COVID-19 cases were considered from the Kaggle COVID dataset repository. The country-wise Exploratory Data Analysis visually represents the active, recovered, closed, and death cases from March 2020 to May 2021. The data are pre-processed and scaled using a MinMax scaler to extract and normalize the features to obtain an accurate prediction rate. The proposed methodology employs Random Forest Regressor, Decision Tree Regressor, K Nearest Regressor, Lasso Regression, Linear Regression, Bayesian Regression, Theilsen Regression, Kernel Ridge Regressor, RANSAC Regressor, XG Boost, Elastic Net Regressor, Facebook Prophet Model, Holt Model, Stacked Long Short-Term Memory, and Stacked Gated Recurrent Units to predict active COVID-19 confirmed, death, and recovered cases. Out of different machine learning, deep learning, and time series models, Random Forest Regressor, Facebook Prophet, and Stacked LSTM outperformed to predict the best results for COVID-19 instances with the lowest root-mean-square and highest R 2 score values.

Impact of the COVID-19 pandemic on road traffic accident forecasting in Poland and Slovakia

The COVID-19 pandemic significantly affected the performance of the transport sector and its overall intensity. Reducing mobility has a major impact on road traffic accidents. The aim of this study is to forecast the number of road traffic accidents in Poland and Slovakia and to assess how the COVID-19 pandemic affected its trend. For this purpose, data for Poland and Slovakia in the selected relevant period were analyzed. Based on actual data from the past, a forecast was made for the future considering two scenarios - one where there is no effect of pandemic, and another with effect of pandemic. Forecasting the number of accidents in Poland was carried out using selected time series models related to linear trend (Holt and Winters method) and the exponential model. In the case of Slovakia, the model without trend and the exponential model were used to forecast the number of traffic accidents. The results of the research show that the pandemic caused a decrease in the number of traffic accidents in Poland by 31% and in Slovakia by 33%. This is a significant decline, but it is linearly dependent on restrictive measures that affect the mobility of the population. A similar trend can therefore be expected on a European scale.

Forecasting the coronavirus disease 2019 pandemic in India using machine learning and statistical models

COVID-19, which is an infectious disease caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has resulted in a massive blow to India with respect to the health of its citizens and economy. The work in this paper focuses on the Prophet model, linear regression model, Holt's model and the ARIMA model for predicting the number of confirmed, recovered cases, deaths and active cases along with growth rate, recovery rate and mortality rate in India for the month of November 2020. The performance of all the above mentioned models has been evaluated using standard metrics namely R2, adjusted R2, root-mean-square error and mean absolute error. © 2022 Inderscience Enterprises Ltd.