Faculty Publications
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Item Pattern Analysis of COVID-19 Based On Geotagged Social Media Data with Sociodemographic Factors(Institute of Electrical and Electronics Engineers Inc., 2022) Sabareesha, S.S.S.; Bhattacharjee, S.; Shetty, R.D.The world has faced a catastrophic global crisis of COVID-19 caused by coronavirus and called for analyzing the affected areas in any country. The study helps to understand how the second wave affected different states in India concerning sociodemographic factors, such as population density, economy, and unemployment rate. During the lockdown, the sudden impact of staying at home has led to increased social media usage, where people expressed their opinions on multiple topics. Twitter provides timestamp and sometimes spatial information of the tweets generated. Using the geotagged Twitter dataset, a study in India is performed in this work considering the second wave of COVID-19, which occurred approximately from April to June 2021. It analyses the temporal and spatial patterns of the geotagged tweets generated from all the states during the period mentioned above. Also, topic modeling and sentiment analysis are performed to understand the concerns discussed by the people. We use different states' sociodemographic factors and machine learning algorithms to divide the population into high and low categories to understand the topic prevalence in different socioeconomic groups. This study reveals that the low socioeconomic groups have shared more concerns, urging the government to help fight the COVID-19 pandemic. © 2022 IEEE.Item Tropospheric NO2 and O3 Response to COVID-19 Lockdown Restrictions at the National and Urban Scales in Germany(John Wiley and Sons Inc, 2021) Balamurugan, V.; Chen, J.; Qu, Z.; Bi, X.; Gensheimer, J.; Shekhar, A.; Bhattacharjee, S.; Keutsch, F.N.This study estimates the influence of anthropogenic emission reductions on nitrogen dioxide ((Formula presented.)) and ozone ((Formula presented.)) concentration changes in Germany during the COVID-19 pandemic period using in-situ surface and Sentinel-5 Precursor TROPOspheric Monitoring Instrument (TROPOMI) satellite column measurements and GEOS-Chem model simulations. We show that reductions in anthropogenic emissions in eight German metropolitan areas reduced mean in-situ (& column) (Formula presented.) concentrations by 23 (Formula presented.) (& 16 (Formula presented.)) between March 21 and June 30, 2020 after accounting for meteorology, whereas the corresponding mean in-situ (Formula presented.) concentration increased by 4 (Formula presented.) between March 21 and May 31, 2020, and decreased by 3 (Formula presented.) in June 2020, compared to 2019. In the winter and spring, the degree of (Formula presented.) saturation of ozone production is stronger than in the summer. This implies that future reductions in (Formula presented.) emissions in these metropolitan areas are likely to increase ozone pollution during winter and spring if appropriate mitigation measures are not implemented. TROPOMI (Formula presented.) concentrations decreased nationwide during the stricter lockdown period after accounting for meteorology with the exception of North-West Germany which can be attributed to enhanced (Formula presented.) emissions from agricultural soils. © 2021. The Authors.Item GSI: An Influential Node Detection Approach in Heterogeneous Network Using Covid-19 as Use Case(Institute of Electrical and Electronics Engineers Inc., 2023) Shetty, R.D.; Bhattacharjee, S.; Dutta, A.; Namtirtha, A.The growth of COVID-19, caused by the SARS-CoV-2 virus, has turned into an unprecedented pandemic in the last century. It is crucial to identify superspreading nodes to prevent the pandemic's progress. Most available superspreader identification techniques consider only a single or few network metrics related to the complex network's topological structure. Furthermore, it is more challenging to determine influential spreaders from heterogeneous structures of networks. In a disease transmission network, the degree of heterogeneity is essential to locate the path of the infection spread. Therefore, it is required to have an extended degree of centrality to collect information from various neighborhood levels. This article presents an approach, namely, global structure influence (GSI), which considers network nodes' local and global influence. This method can gather information from multiple levels of the neighborhood. Evaluation of our proposed method is done by considering different types of networks, i.e., social networks, highly heterogeneous human contact networks, and epidemiological networks, and also by using the benchmark susceptible-infected-recovered (SIR) epidemic model. The GSI technique provides real-spreading dynamics across various network structures and has outperformed the baseline techniques with an average Kendall's τ improvement range from 0.017 to 0.278. This study will help to identify the superspeaders in real applications, where pathogens spread quickly because of close contact, such as the recently witnessed COVID-19 pandemic. © 2014 IEEE.