Covid-19—Analysis and Prediction—A Case Study Using Machine Learning

In this research paper, machine learning-based models are used for predicting the number of countries that have been affected by the COVID-19 virus and what was the situation of them. Then, the analysis is performed whether the “Lockdown” is the savior or not, and how India faced the whole problem. After that the prediction model for India was also prepared. These are the selected ones that are comprised in this paper. The COVID virus has spread globally, causing thousands of deaths and having an enormous impact on our health systems and economies. As soon as the Covid-19 pandemic was hit hard worldwide, lots of researchers were trying to figure out lot more things, among them when and from where did the virus make its an outbreak. The whole analysis part is based on the data science and machine learning algorithms and utterly dependent on the data collected from various trusted sources. In this paper, decision tree-based linear (DTBL) model and Random Forest Regression (RFR) models are applied to ensure the accuracy of the models. The Auto-Regressive Integrated Moving Average (ARIMA) and Prophet Algorithm are used for the prediction model. Preventive measures to reduce the spread of the COVID virus in respective zones are also suggested. We hope that this research work will help in the understanding and eradication of the threatening disease. This study can be applied by other countries for predicting COVID-19 cases at the state or national level. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Airway management guidance for the endemic phase of COVID-19.

It is now apparent that severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) and coronavirus disease 2019 (COVID-19) will remain endemic for some time. Improved therapeutics and a vaccine may shorten this period, but both are far from certain. Plans must be put in place on the assumption that the virus and its disease will continue to affect the care of patients and the safety of staff. This will impact particularly on airway management due to the inherent risk to staff during such procedures. Research is needed to clarify the nature and risk of respiratory aerosol-generating procedures. Improved knowledge of the dynamics of SARS-CoV-2 infection and immunity is also required. In the meantime, we describe the current status of airway management during the endemic phase of the COVID-19 pandemic. Some controversies remain unresolved, but the safety of patients and staff remains paramount. Current evidence does not support or necessitate dramatic changes to choices for anaesthetic airway management. Theatre efficiency and training issues are a challenge that must be addressed, and new information may enable this.