Mental Health Analysis of Indians During Pandemic

The Covid-19 pandemic has severely affected many countries around the globe in terms of physically as well as mentally. During the initial months of the pandemic have reported India’s deficient cases, but eventually the cases were proliferated as the time progress. The government’s decision to impose a lockdown without warning has a wide-ranging impact, affecting everyone from low-wage workers to huge corporations. As a result, there is a negative impact on people’s mental health and emotions. The people had suffered from depressions, anxiety, fatigue and so forth. Many wide varieties of the people had expressed their thoughts, viewpoints, and their mental conditions in the form of tweets over the Twitter, a social media platform. Hence, in this paper, we have statistically analysed the data of tweeted tweets to elicit the meaningful insights. The data was analysed using the unsupervised clustering strategy–K-means and LDA–and the results were reinforced and validated using the pre-trained supervised classification approach–Text to Text transformer. The anticipated data depicted that the fear was the most common state of mind at the end of the lockdown, followed by joy, anger, and sadness. Furthermore, the deduced insights will be highly beneficial in decision-making process when such an epidemic or pandemic situation re-surges. © 2021, Springer Nature Switzerland AG.

Data Analysis and Forecasting of COVID-19 Outbreak in India Using ARIMA Model

Nowadays COVID-19 has created a pandemic for the whole world. This is also known as Novel Coronavirus-2019. In this paper, time series analysis using the ARIMA model is brought forward for COVID-19 prediction on the confirmed cases in India. ARIMA model can significantly give precise forecast results based on AIC (Akaike Information Criteria) value. ARIMA model can considerable reduce the errors of the prediction results with 24418 AIC value for predicting confirmed cases in India. The work is implemented gathering the data of confirmed cases from different states of the country. The duration from 30th January 2020 to 28th April 2020 has been taken into consideration for verifying the positive cases of corona in India. Moving average and auto regressive models are used for accurate prediction and maintaining seasonal differencing and second order differencing. The graphical representation is demonstrated applying the technique named Data Visualization in python programming. It shows the increasing amount of confirmed cases as well as the number of cured cases and death cases in India. It is examined that the p, d, q parameter in ARIMA can locate the best AIC value. According to the analysis in this context rolling mean and standard deviation test Statistic value is −1.186895. ADF Statistic value is 1.186895. Data sets are divided in training and testing module respectively for approximate judgement of positive cases. © 2021, Springer Nature Singapore Pte Ltd.