In this study, we analyze the number of infected positive cases of COVID-19 outbreak with concern to lockdown in India in the time window of February 11th 2020 to Jun 30th 2020. The first case in India was reported in Kerala on January 30th 2020. To break the chain of spreading, Government announced a nationwide lockdown on March 24th 2020, which is increased two times. The Ongoing lockdown 3.0 is over on May 18th, 2020. We derived how the lockdown relaxation is going to impact on containment of the outbreak. Here the Exponential Growth Model has been used to derive the epidemic curve based on the data collected from February 11th 2020, to May 11th 2020, and the Machine Learning based Linear Regression model that gives the epidemic curve to predict the cases with the continuous flow of the lockdown. We estimate that if the lockdown is continuing with more relaxation, then the estimated infected cases reach up to 1.16 crores by June 30th 2020, and the lockdown would persist with current restriction, then the expected predicted infected cases are 5.69 lacs. The Exponential Growth Model and the Linear Regression Model are advantageous to predict the number of affected cases of COVID-19. These models can be used for forecasting in long term intervals. It shows from our result that lockdown with certain restriction has a vital role in preventing the spreading of this epidemic in this current situation.
The COVID-19 epidemic has brought attention to the vulnerability of new illnesses, and immunization remains a viable option for resuming normal life. This paper examines the influence of COVID-19 vaccination on the death rate and the performance of stock market in India. For this study, COVID-19 vaccination and death rate data is gathered from the Ministry of Health and Family Welfare (MoHFW) portal, and the data for the stock index is taken from the Bombay Stock Exchange (BSE), India. In order to achieve a precise representation of feature significance and distribution, EDA (Exploratory Data Analysis) is utilized in this study. The impact of COVID-19 immunization on the mortality rate and stock market index is investigated using both statistical analysis and Machine Learning Regression-based models. The models are remarkably accurate in reproducing actual result. The empirical study suggests that vaccination has a strong positive impact on the stock market and reducing the death rate. Furthermore, the policies recommended by government and monetary authorities coupled with COVID-19 vaccine supported the stock market recovery in pandemic.
Manufacturing productivity is inextricably linked to air freight handling for the global delivery of finished and semi-finished goods. In this article, our focus is to capture the transport risk associated with air freight which is the difference between the actual and the planned time of arrival of a shipment. To mitigate the time-related uncertainties, it is essential to predict the delays with adequate precision. Initially data from a case study in the transportation and logistics sector were pre-processed and divided into categories based on the duration of the delays in various legs. Existing datasets are transformed into a series of features, followed by extracting important features using a decision tree-based algorithm. To predict the delay with maximum accuracy, we used an improved hybrid ensemble learning-based prediction model with bagging and stacking enabled by characteristics like time, flight schedule, and transport legs. We also calculated the dependency of accuracy on the point in time during business process execution is examined while predicting. Our results show all predictive methods consistently have a precision of at least 70 percent, provided a lead-time of half the duration of the process. Consistently, the proposed model provides strategic and sustainable insights to decision-makers for cargo handling.
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