Suicide Rate and Factors Analysis: Pre and Post COVID Pandemic Data Analysis

According to the 2021 Report from the World Health Organization (WHO), more than 700,000 people have taken their life. Suicide can be prevented but so far most of the efforts to do so have fallen short. However, the use of machine learning and artificial intelligence offers new opportunities to increase the accuracy level of prediction and aid the goal of suicide prevention. This paper reviews literature concerning the machine learning methods used to help identify various risk factors and help prevent suicide. This paper also presents our research and analysis findings which were used to identify various suicide risk factors and additional analysis of whether there are any correlations or variations in the risk factors from pre-and post-pandemic datasets regarding suicide rates. This is especially important during times of high stress, such as a worldwide pandemic and quarantine. The dataset(s) obtained from WHO suggest that high levels of risk factor identification are possible and This paper and the analysis serve as supporting research and guide to aid in the continued ambitious goal of suicide prevention worldwide © 2022 IEEE.

Economic Inclusion in the United States: Predictive Analysis of COVID-19 pandemic on County Rates of Unbanked Households

COVID-19 pandemic has a significant effect on the unemployment rate in the United States. However, the economic effect in different states is not the same for each household. In this work, Our goal is to capture and outline the relationships between pandemic incidence, economic inclusion, unemployment, and bank branch closures in order to understand the emerging relationship between the coronavirus pandemic, rates of economic inclusion, and economic well-being of localities. Furthermore, we machine learning algorithms to evaluate the predictive power of coronavirus incidence and fatality rates, county-level unemployment, and bank branch closure rates on rates of economic inclusion. Also, a natural language processing approach is used to analyze the unemployment COVID-19 textual data. We use BERT as a powerful transformer for sentiment classification on COVID-19 unemployment data.

Transfer Learning Model for Disrupting Misinformation During a COVID-19 Pandemic

In 2020, the COVID-19 pandemic changed the world significantly, and it is critical to have reliable online information about this virus. However, disinformation can have a negative effect on public opinion and can put the lives of millions in danger by ignoring the crucial precautions. People worldwide post their ideas about the coronavirus every second and create a rich source of information. In this work, we introduce an advanced natural language processing model to classify public opinion about the virus, which can help health organizations to take immediate actions to stop the spread of the virus by removing misinformation from online platforms. We introduce a new model with high classification accuracy to extract deep contextual information from online coronavirus comments based on main COVID-19 topics and use a robust model for sentiment classification based on more than twenty different datasets to detect the tweet's text, which contains misinformation. The new model can generate reports about tweets that contain misinformation to the states requiring emergency precautions to stop the virus's spread by removing the detected comments from their online platforms. Also, The new model can detect misinformation and prevent fake news by increasing public awareness about COVID-19.