An Exploration of Impact of COVID 19 on mental health -Analysis of tweets using Natural Language Processing techniques

Twitter is one of the worlds biggest social media platforms for hosting abundant number of user-generated posts. It is considered as a gold mine of data. Majority of the tweets are public and thereby pullable unlike other social media platforms. In this paper we are analyzing the topics related to mental health that are recently (June, 2020) been discussed on Twitter. Also amidst the on-going pandemic, we are going to find out if covid-19 emerges as one of the factors impacting mental health. Further we are going to do an overall sentiment analysis to better understand the emotions of users. Executive SummeryNovel Corona viruss spread and its impact on various aspects of national and individuals well-being has been at the center of lot of discussions across micro-blogging sites and various social media platforms ever since it commenced in December 2019. Users are voicing their opinions on several topics related to covid-19. Social distancing as prescribed by Government and Local Administration We all are aware that the Novel Corona virus has significantly affected our physical health; however the current social distancing norms are taking a toll on the psychological well-being of individuals. The research paper presents a two-phased analysis of most recent 2000 tweets related to mental health pulled out twice over a span of one month on 28 June 2020 and 28 July2020 respectively, thereby analyzing 4000 tweets in total. The second phase analysis was conducted exactly after a gap of one month to validate the results generated by the first analysis. The intention is to analyze to what extent people have discussed about mental health in the past few months based on the information disseminated on Twitter. Data was extracted using Twitters search application programming interface (API) and Pythons tweepy library. A predefined keyword like mental health was given to find out if Covid-19 emerges as a reason for the same. Several natural language processing (NLP) techniques like tokenization, removing URL and stop words, stemming and lemmatization were used to pre-process the text data and make it ready for analysis. These collected tweets were analyzed using word frequencies of single and double words (unigram, bigram). A very unique feature of this analysis includes a network diagram that shows interconnections between the set of most common words used in to its and the connections (if any) are represented through links. Topic modeling technique in NLP visualizes the top concerns of tweeters through a word cloud. At present we have many methods to do topic modeling. In this paper we are using the Latent Dirichlet Allocation (LDA) method which is a probabilistic approach of modeling given by Prof David H.B in 2003. This model deals with distribution of topics to tweets and allocation of those topics to documents and words to topics. Finally a sentiment analysis is done using text mining techniques to analyze the sentiment of the tweets in the form of positive, negative and neutral.

Covid-19 Pandemic Data Analysis and Forecasting using Machine Learning Algorithms

India reported its first Covid-19 case on 30th Jan 2020 and the number of cases reported heavily escalated from March, 2020. This research paper analyses COVID -19 data initially at a global level and then drills down to the scenario obtained in India. Data is gathered from multiple data sources-several authentic government websites. The need of the hour is to accurately forecast when the numbers will reach at its peak and then diminish. It will be of huge help to public welfare professionals to plan the preventive measures to be taken keeping the economic balance of the country as well. Variables such as gender, geographical location, age etc. have been represented using Python and Data Visualization techniques. Time Series Forecasting techniques including Machine Learning models like Linear Regression, Support Vector Regression, Polynomial Regression and Deep Learning Forecasting Model like LSTM(Long short-term memory) are deployed to study the probable hike in cases and in the near future. A comparative analysis is also done to understand which model fits the best for our data. Data is considered till 30th July, 2020. The results show that a statistical model named sigmoid model is outperforming other models. Also the Sigmoid model is giving an estimate of the day on which we can expect the number of active cases to reach its peak and also when the curve will start to flatten. Strength of Sigmoid model lies in providing a count of date that no other model offers and thus it is the best model to predict Covid cases counts -this is unique feature of analysis in this paper. Certain feature engineering techniques have been used to transfer data into logarithmic scale as is affords better comparison removing any data extremities or outliers. Based on the predictions of the short-term interval, our model can be tuned to forecast long time intervals.

Early markers of occult megaloblastosis for low-cost detection of hyperhomocysteinemia in patients with ischaemic stroke: preventive approach for primary health care.

Recent studies have focussed on the association between elevated homocysteine levels with megaloblastic changes and thromboembolic events, but the relationship between occult megaloblastosis (with normal haemoglobin levels) and ischaemic stroke has not been widely explored. The objective of this study is to establish a simple and economical marker for the detection of occult megaloblastosis at the community health care level in developing countries. A hundred patients who met the inclusion criteria were studied. At the 5% level of significance, the levels of cobalamin and folate were significantly lower, while the number of hypersegmented neutrophils on the peripheral smear was higher in patients from Group A (70 patients with high homocysteine) compared with the patients in Group B (30 patients with normal homocysteine). Forty-five (64.2%) of the 70 patients in Group A showed hypersegmentation of neutrophils in the peripheral smear. The high cost and difficulty in performing the vitamin assays limit their use as early markers of megaloblastosis. Hence, we conclude that in developing countries, the detection of hypersegmented neutrophils can be used at the primary healthcare level for early diagnosis of occult megaloblastosis, so that early therapeutic interventions with vitamins can prevent attacks of hyperhomocysteinemia-induced ischaemic stroke.

Reported willingness and associated factors related to utilization of voluntary counseling and testing services by female sex workers in Shandong Province, China / 生物医学与环境科学（英文）

<p><b>OBJECTIVE</b>To explore reported willingness and factors associated with utilization of voluntary counseling and testing services by female sex workers (FSWs) in China and to offer recommendations to optimize use of such services.</p><p><b>METHODS</b>A questionnaire to explore willingness to use VCT was designed based on social ecological theory and formative qualitative research. A cross-sectional survey was conducted among FSWs from entertainment venues. Single and multiple logistic regression analyses were employed to examine factors associated with reported willingness to utilize VCT.</p><p><b>RESULTS</b>A total of 970 FSWs provided valid questionnaires, with 69% (669) expressing willingness to utilize VCT. Factors at the interpersonal level associated with reported willingness included knowledge about VCT, desire to get help if diagnosed as HIV positive, ability to imagine life after an HIV positive diagnosis, and perceived support for VCT from peers, managers, and family members. Availability of free antiretroviral (ARV) treatment represented a factor at policy level. Other factors included intention to leave sex work in the near future, having had a previous HIV test, and lack of a suspected STD history.</p><p><b>CONCLUSIONS</b>The rate of reported willingness to use VCT among FSWs was substantially higher than that of actual VCT utilization (11%). The next step is to explore the connection between reported willingness and actual use. Based on these findings, peer education, VCT knowledge dissemination, and free ARV treatment should be emphasized to increase FSWs' willingness to use VCT.</p>