Ishrat Jahan Eliza

eDakterBari is a platform for providing health services to orphans, a community in Bangladesh with limited resources. The orphans in orphanages and the administrators of orphanages, and the respectable doctors are our primary users. We are concentrating on the service intermediation to provide healthcare through the suitable intermediaries for the orphans and developing interactive systems according to the user requirements. To do so, we have surveyed orphanages in different areas of Dhaka city and gathered information about the availability of healthcare services in the orphanages. Later, we deployed our eDakterBari to the orphanages with the help of intermediaries who are closely conected to the orphans in the orphanages.

Journal: Heliyon

Coronavirus (COVID-19) pandemic has been the defining global health crisis. New policies need to be forged by policy-makers for various sectors such as trading, banking, education, etc., to lessen losses and to heal quickly. For efficient policy-making, in turn, some prerequisites needed are historical trend analysis on the pandemic spread, future forecasting, the correlation between the spread of the disease and various socio-economic and environmental factors, etc. Besides, all of these need to be presented in an integrated manner in real-time to facilitate efficient policy-making. Therefore, in this work, we developed a web-based integrated real-time operational dashboard as a one-stop decision support system for COVID-19. In our study, we conducted a detailed data-driven analysis based on available data from multiple authenticated sources to predict the upcoming consequences of the pandemic through rigorous modeling and statistical analyses. We also explored the correlations between disease spread and diverse socio-economic as well as environmental factors. Furthermore, we presented how the outcomes of our work can facilitate both contemporary and future policy-making.

I contributed to this project by conducting statistical analysis to identify relationships between various environmental, socioeconomic, and COVID-19 variables (death, affected). In addition, the other member of the data analysis team and I performed prediction analysis using the epidemiological model SIR. Later, to make it accessible online, I placed the entire project in the AWS.

Conference: COMPASS '22: ACM SIGCAS/SIGCHI Conference on Computing and Sustainable Societies (COMPASS)

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A better disease outbreak analysis and surveillance system can harness state-of-the-art data mining and machine learning techniques to produce better forecasting. In this regard, understanding the correlation between disease outbreaks and socioeconomic factors should pave the way for such systems by providing useful indicators. Therefore, in this study, we accumulated data on 72 infectious diseases and their outbreaks all over the globe over a period of 23 years as well as corresponding different socioeconomic data. We, then, performed point-biserial and spearman correlation analysis over the collected data. Our analysis of the obtained correlations demonstrates that various disease outbreak attributes are positively and negatively correlated with different socioeconomic indicators. For example, indicators such as lifetime risk of maternal death, adolescent fertility rate, etc., are positively correlated, while indicators such as life expectancy at birth, measles immunization, etc., are negatively correlated, with disease outbreaks that affect the digestive organ system. In this paper, we find and summarize the correlations between 126 outbreak attributes derived from the characteristics of the 72 diseases in consideration and 192 socioeconomic factors.which is a novel contribution to the field of disease outbreak analysis and prediction.

I contributed to the research by performing large-scale point-biserial and correlation analysis on the accumulated data to quantify the strength and nature of relationships between two disease outbreak attributes and various socioeconomic indicators.

Conference: COMPASS '22: ACM SIGCAS/SIGCHI Conference on Computing and Sustainable Societies (COMPASS)

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Current state: Under Progress (Targeted Conference: IEEE VIZ)

It is a user study to analyze the COVID-19 viewpoints of individuals in Bangladesh, India, and the United States, three of the major affected nations. We conduct a comparative analysis of the responses with the goal of identifying any potential sociocultural variances between the perspectives and experiences of the residents of those three nations. Our study found correlations among the perspectives and experiences of the pandemic.

Current state: Under Review (Journal: World Development)

The details of individual feelings and how they change on social media before and during the epidemic are explored in our study. We thoroughly investigate the currently available libraries and their potential combinations in order to create a method that is more efficient than the frequently inadequate out-of-the-box sentiment-analyzing libraries for text posts for analyzing sentiments in Twitter text posts relevant to the pandemic. In addition to word posts, we also undertake a longitudinal bimodal exploratory data analysis utilizing image postings.

Current state: Under Review (Journal: Technological Forecasting and Social Change)