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2022 IEEE Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation, IATMSI 2022 ; 2022.
Article in English | Scopus | ID: covidwho-20233946

ABSTRACT

Air pollution is one of the most significant concerns of the present era, which has severe and alarming effects on human health and the environment, thereby escalating the climate change issue. Hence, in-depth analysis of air pollution data and accurate air quality forecasting is crucial in controlling the growing pollution levels. It also aids in designing appropriate policies to prevent exposure to toxic pollutants and taking necessary precautionary measures. Air quality in Delhi, the capital of India, is inferior compared to other major cities in the world. In this study, daily and hourly concentrations of air pollutants in the Delhi region were collected and analyzed using various methods. A comparative analysis is performed based on months, seasons, and the topography of different stations. The effect of the Covid-19 lockdown on the reduction of pollutant levels is also studied. A correlation analysis is performed on the available data to show the relationships and dependencies among different pollutants, their relationship with weather parameters, and the correlations between the stations. Various machine learning models were used for air quality forecasting, like Linear Regression, Vector Auto Regression, Gradient Boosting Machine, Random Forest, and Decision Tree Regression. The performance of these models was compared using RMSE, MAE, and MAPE metrics. This study is focused on the dire state of air pollution in Delhi, the primary reasons behind it, and the efficacy of calculated lockdowns in bringing down pollution levels. It also highlights the potential of Linear Regression and Decision Tree Regression models in predicting the air quality for different time intervals. © 2022 IEEE.

4.
Journal of Clinical and Diagnostic Research ; 16(5):SC06-SC10, 2022.
Article in English | EMBASE | ID: covidwho-1863298

ABSTRACT

Introduction: Multisystem Inflammatory Response Syndrome in Children (MIS-C) temporally associated with Coronavirus Disease 2019 (COVID-19) is characterized by fever, raised inflammatory markers, multisystem involvement with evidence of COVID-19 infection (positive RT-PCR or serology). It occurs concurrently or after 4-6 weeks of acute COVID infection.It has wide range of clinical presentation ranging from mild asymptomatic infection to severe life-threatening illness. Clinical presentation of MIS-C has considerable overlapping features with other tropical infections. During peak wave of COVID-19, when large proportion of population has been affected by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), contracting other infections during and within four weeks of active COVID-19 is inevitable. Despite of this concern, only few researchers have studied co-infection and they explained a complex interaction between COVID-19 and other infections like tuberculosis and dengue. They demonstrated how one infection augments the severity of other. To the best of our knowledge no pediatric population-based study explained the interaction of acute COVID-19 & MIS-C with other infections so far. Aim: To determine the association of MIS-C with co-infections in SARS-CoV-2 positive children of 1 month to less than 18 years of age. Materials and Methods: A retrospective review of the medical records of pediatric patients with SARS-CoV-2 infection, treated from September 2020 to February 2021, was performed.All the patients who fulfilled World Health Organization (WHO) criteria of MIS-C were included. Detailed demographic, clinical, laboratory parameters and associated co-infections were recorded.The severe and non severe MIS-C groups were compared. Sample ‘t’ test, Wilcoxon test and Chi-squared test were used for statistical analysis. results: A total of 44 children fulfilled the diagnostic criteria of MIS-C and were included in the study. Out of 44, 20 children (45.4%) had severe disease and 24 had non severe disease. The mean age of children with severe MIS-C was 7.38±5.39 years, as compared to 4.37±4.61 years in the non severe group (p-value= 0.044). Males were predominantly affected in both the groups (Male: Female =1.22:1 in severe MIS-C and 2.4:1 in non severe MIS-C). The gastrointestinal system was most commonly affected in both groups. Associated co-infection was noted more in severe MIS-C group (11 vs 1 patient in severe vs non severe group, p-value=<0.001). Tuberculosis was found to be associated in three patients, followed by complicated enteric fever, and severe dengue in two patients each. The odds ratio for developing severe MIS-C in the presence of co-infections was 10.5(CI=2.33-47.27) while in its absence it was 0.10(0.02-0.43). conclusion: The findings of this study support that concurrent infections in COVID-19 can exacerbate the severity of COVID-19 illness and may lead to severe MIS-C.

5.
4th IEEE Pune Section International Conference, PuneCon 2021 ; 2021.
Article in English | Scopus | ID: covidwho-1741249

ABSTRACT

Effects of the COVID-19 pandemic and rise in cloud infrastructure have prompted workplaces to normalize offering remote work to individual employees in order to communicate using user-accessible software. In-person interactions used to be an effective way of communication. As in-person meetings are not always possible, the alternative is to have virtual meetings. Currently, there are many options one can use for professional meetings and for casual hangouts but none that allow for both. In this manuscript, we blueprint the process of building a highly interactive web application that bridges the gap between professional meeting environments and casual chat applications by allowing for a collaborative virtual environment between end- users. Doing so would enable individuals to connect securely over on a single platform, without the need of focusing on the context of the meeting. © 2021 IEEE.

6.
Journal of Aerosol Medicine and Pulmonary Drug Delivery ; 34(5):A8-A9, 2021.
Article in English | EMBASE | ID: covidwho-1483353

ABSTRACT

While the nasopharynx stands out as the dominant initial infection site for SARS-CoV-2, the physiological mechanism launching the lower airway infection is still not well-understood. Based on the speed of infection progress, it is thought that the nasopharynx acts as the seeding zone for subsequent contamination of the lower airway via aspiration of virus-laden boluses of nasopharyngeal fluids. We examine the plausibility of this transport process through computational fluid mechanics models of steady and forced breathing in five tomographic airway reconstructions, thereby quantifying the nasopharyngeal liquid volume transmitted to the lower airspace in each aspiration. Our model predicts 2-4 aspirations during an 8-hour sleep cycle, consistent with prior experimental data. Extending the numerical trends on aspiration volume to earlier records on aspiration frequency indicates a total aspirated nasopharyngeal liquid volume of 0.3±0.76 ml/day. Using sputum viral loads for hospitalized COVID-19 patients, we then estimate the number of virions transmitted daily to the lungs via nasopharyngeal liquid boluses. For peak sputum viral load, the number is 7.1 × 108±1.8 × 109 virions/day, well in excess of the estimated minimum infectious dose for SARS-CoV-2. These findings provide a mechanism for the progression of SARS-CoV-2 infection of the nasopharynx to the COVID-19 disease within a patient, and point to dysphagia as one of the potential underlying risk factors for adverse outcomes.

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Pharmacoepidemiology and Drug Safety ; 30:6-6, 2021.
Article in English | Web of Science | ID: covidwho-1381674
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