Nationwide CoViD-19 lockdown impact on air quality in India

This paper discusses the comparative results of surface and satellite measurements made during the Phase1 (25 March to 14 April), Phase2 (15 April to 3 May) and Phase3 (3 May to 17May) of Covid-19 imposed lockdown periods of 2020 and those of the same locations and periods during 2019 over India. These comparative analyses are performed for Indian states and Tier 1 megacities where economic activities have been severely affected with the nationwide lockdown. The focus is on changes in the surface concentration of sulfur dioxide (SO2), carbon monoxide (CO), PM2.5 and PM10, Ozone (O3), Nitrogen dioxide (NO2) and retrieved columnar NO2 from TROPOMI and Aerosol Optical Depth (AOD) from MODIS satellite. Surface concentrations of PM2.5 were reduced by 30.59%, 31.64% and 37.06%, PM10 by 40.64%, 44.95% and 46.58%, SO2 by 16.73%, 12.13% and 6.71%, columnar NO2 by 46.34%, 45.82% and 39.58% and CO by 45.08%, 41.51% and 60.45% during lockdown periods of Phase1, Phase2 and Phase3 respectively as compared to those of 2019 periods over India. During 1st phase of lockdown, model simulated PM2.5 shows overestimations to those of observed PM2.5 mass concentrations. The model underestimates the PM2.5 to those of without reduction before lockdown and 1st phase of lockdown periods. The reduction in emissions of PM2.5, PM10, CO and columnar NO2 are discussed with the surface transportation mobility maps during the study periods. Reduction in the emissions based on the observed reduction in the surface mobility data, the model showed excellent skills in capturing the observed PM2.5 concentrations. Nevertheless, during the 1st & 3rd phases of lockdown periods AOD reduced by 5 to 40%. Surface O3 was increased by 1.52% and 5.91% during 1st and 3rd Phases of lockdown periods respectively, while decreased by -8.29% during 2nd Phase of lockdown period.

Assam

Assam constitutes the region of northeast India bounded by the modern nations of Bangladesh and Bhutan, as well as by the Indian states of Arunachal Pradesh, Bangla, Manipur, Meghalaya, and Nagaland. Known in ancient sources as Prāgjyotiṣpūra (the “city of eastern lights”) and as Kāmarūpa (the “form” or “place of desire”), Assam remains one of the least studied and poorly understood areas of South Asian Hinduism. The home to more than forty recognized tribal communities, Assam has tremendous religious, ethnic, and linguistic diversity, which has helped shape the unique forms of Hinduism that have flourished in the region. Moreover, Assam also has a long reputation as a realm of magic, witchcraft, and the supernatural; for example, even in the early 21st century, the town of Mayong in Morigaon district is infamous as the quintessential “land of black magic.” The historical roots of Hinduism in Assam date back to at least the Varman dynasty of the 4th to 7th centuries, when Vedic sacrifices such as the aśvamedha and other Brahmanical rites were widespread. However, most of the kings of Assam from the Varmans onward came from non-Hindu tribal backgrounds, and the form of Hinduism that developed in the region has long been a complex negotiation between Sanskritic traditions and indigenous practices from the many local communities of the region. During the Assamese Pāla dynasty of the 8th to 12th centuries, Śākta traditions became dominant, and major texts such as the Kālikā Purāṇa were composed, praising the great mother goddess Kāmākhyā (goddess of desire) and her retinue of yoginīs. A unique form of Hindu tantra probably also began to flourish at this time, and Assam has a long reputation as one of the oldest heartlands or perhaps even the original homeland of tantra in South Asia. The Ahom kings of the 13th to 19th centuries continued the patronage of powerful goddesses while also building temples to Śiva, Viṣṇu, and others. During the 16th century, Assam like much of northern India witnessed a powerful revival of Vaiṣṇava bhakti, led by the devotional reformer Śaṅkaradeva (b. 1449–d. 1568). Through Śaṅkaradeva’s influence, Vaiṣṇavism remains a dominant cultural and religious force in Assam to this day. However, even in the 21st century, Assamese Hinduism remains incredibly diverse, and one can still see a wide range of indigenous, folk, and local practices that range from magic and menstruation festivals to spirit possession and ecstatic dance performances.

An Integrated Approach for Crop Production Analysis from Geographic Information System Data using SqueezeNet

Predictions and estimations are very important for agriculture applications. The estimation results on crop production may have a huge impact in the economy of a country by changing their export and import data. The estimation of crop production was started by collecting information manually from the fields and analyzing it using a computer. However, the accuracy was not up to the mark due to the error caused by manual collection of data. The Geographic Information System (GIS) applications are developed to store the information observed from the satellite images on change detection in town planning, disaster management, business development and vegetation management. The proposed work estimates the crop production of Indian states from a GIS dataset with a SqueezeNet algorithm. The performance of the SqueezeNet algorithm is compared with the traditional Inception and ResNet algorithms.

Tuberculosis and Diabetes in India: Stakeholder Perspectives on Health System Challenges and Opportunities for Integrated Care

Background India has a dual burden of tuberculosis (TB) and diabetes mellitus (DM). Integrated care for TB/DM is still in the early phase in the country and can be considerably enhanced by understanding and addressing the challenges identified from stakeholders’ perspectives. This study explored the challenges and opportunities at individual, health system and policy level for integrated care of TB/DM comorbidities in India. Methods We used an outlier case study approach and conducted stakeholder interviews and focus group discussions with relevant program personnel including field staff and program managers of TB and DM control programs as well as officials of partners in Indian states, Kerala and Bihar. Results The integrated management requires strengthening the laboratory diagnosis and drug management components of the two individual programs for TB and DM. Focused training and sensitization of healthcare workers in public and private sector across all levels is essential. A district level management unit that coordinates the two vertical programs with a horizontal integration at the primary care level is the way forward. Substantial improvement in data infrastructure is essential to improve decision-making process. Conclusion Bi-directional screening and management of TB/DM comorbidities in India requires substantial investment in human resources, infrastructure, drug availability, and data infrastructure.

Delineation of public health risk factor associate with the mortality ratio in covid-19 pandemic situation during legislative assembly elections from Indian states

The coronavirus is an accurate disease and this virus-related pandemic is hammering human health and increased the public health emergency till now. The main objective of this study is to find out the death, mortality ratio, new cases, and recoveries case identification and correlation analysis between them using regression technique on legislative assembly elections from India. This study encompassed the present disorder of India throughout the elections time in India from 27th March 2021 to 29th April 2020. Statistical analysis was developed by the covid-19 database for monitoring and analyzing the health statutes during elections. Mortality ratio, the relation between active and death cases, active cases and recover cases in India are calculating corona affected data. The results show that death cases were high in the second wave of coronavirus in India. The correlation between daily death and new cases was strong positive (R2= 0.9306). The relationship between recoveries and death was stronger positive (R2=0.9832). The daily death and active cases collation indicated that strong positive (R2= 0.9703). The COVID-19 is dangerous to people's health. The virus is more life-threatening and if people will not follow the WHO guidelines, and it strength demonstration additional havoc very shortly.

Quantitative Framework for Establishing Low-Risk Inter-District Travel Corridors During COVID-19

Aspirations to slow down the spread of novel Coronavirus (COVID-19) resulted in unprecedented restrictions on personal and work-related travels in various nations across the globe in 2020. As a consequence, economic activities within and across the countries were almost halted. As restrictions loosen and cities start to resume public and private transport to revamp the economy, it becomes critical to assess the commuters’ travel-related risk in light of the ongoing pandemic. The paper develops a generalizable quantitative framework to evaluate the commute-related risk arising from inter-district and intra-district travel by combining nonparametric data envelopment analysis for vulnerability assessment with transportation network analysis. It demonstrates the application of the proposed model for establishing travel corridors within and across Gujarat and Maharashtra, two Indian states that have reported many COVID-19 cases since early April 2020. The findings suggest that establishing travel corridors between a pair of districts solely based on the health vulnerability indices of the origin and destination discards the en-route travel risks from the prevalent pandemic, underestimating the threat. For example, while the resultant of social and health vulnerabilities of Narmada and Vadodara districts is relatively moderate, the en-route travel risk exacerbates the overall travel risk of travel between them. The study provides a quantitative framework to identify the alternate path with the least risk and hence establish low-risk travel corridors within and across states while accounting for social and health vulnerabilities in addition to transit-time related risks.

Empirical Study on Classifiers for Earlier Prediction of COVID-19 Infection Cure and Death Rate in the Indian States

Machine Learning methods can play a key role in predicting the spread of respiratory infection with the help of predictive analytics. Machine Learning techniques help mine data to better estimate and predict the COVID-19 infection status. A Fine-tuned Ensemble Classification approach for predicting the death and cure rates of patients from infection using Machine Learning techniques has been proposed for different states of India. The proposed classification model is applied to the recent COVID-19 dataset for India, and a performance evaluation of various state-of-the-art classifiers to the proposed model is performed. The classifiers forecasted the patients’ infection status in different regions to better plan resources and response care systems. The appropriate classification of the output class based on the extracted input features is essential to achieve accurate results of classifiers. The experimental outcome exhibits that the proposed Hybrid Model reached a maximum F1-score of 94% compared to Ensembles and other classifiers like Support Vector Machine, Decision Trees, and Gaussian Naïve Bayes on a dataset of 5004 instances through 10-fold cross-validation for predicting the right class. The feasibility of automated prediction for COVID-19 infection cure and death rates in the Indian states was demonstrated.