COVID-19 Pandemic has brought a threatening challenge to the world and as well as for Indian society and economy. In India, it has become a public health disaster and its' intensity increasing continuously. For the disaster risk reduction, and capacity building against the COVID-19 pandemic understanding of the relationship between socio-environmental conditions with the pandemic is very necessary. The objective of the present work is to construct a socio-environmental vulnerability index of the potential risk of community spread of COVID-19 using socio-economic and environmental variables. In this, cross-sectional study principal component analyses have been used to drive SoEVI. 4 uncorrelated sub-index has been extracted from 16 sub-indicators which reflects 59% of the variance. Aggregation of 4 Sub-Index has been done to obtain the final vulnerability Index. Results show that there is spatial variability in vulnerability based on environmental and socio-economic conditions. Districts of north and central India found more vulnerable then south India. Statistical significance has been tested using regression analysis, positive relation has been found between vulnerability index and confirmed and active cases. The vulnerability index has highlighted environmentaly and socioeconomicallybackward districts. These areas will suffer more critical problems against COVID-19 pandemic for their socio-environmental problem.

Rapid population growth in North-East India has led to urban sprawl, agricultural expansion, and industrialization, resulting in significant deforestation. Numerous studies have utilized Forest Canopy Density (FCD) coupled with remote sensing and GIS to assess forest cover changes. This study evaluates how FCD changed from 2000 to 2020, highlighting deforestation patterns in North-East India. Machine learning models, including Binary Logistic Regression (BLR), Random Forest (RF), REP-Tree, and XGBoost Regression (XGBR), were used to identify areas at risk of deforestation. Influencing factors considered were forest density, barren land, agricultural land, urban areas, distance to roads, and topographical characteristics. Results indicated that proximity to roads notably increases deforestation risk. Higher densities of agricultural and urban land also contribute to greater deforestation rates, whereas increased forest and barren land densities reduce this risk. Among the tested models, Random Forest showed superior performance with a high True Positive Rate (TPR) and low False Positive Rate (FPR), effectively identifying high-risk deforestation zones. Analysis also revealed a concerning shift from high-density to low-density forests, signifying substantial forest cover loss and potential threats to biodiversity and ecosystem services. The findings emphasize the need for integrated land-use planning and targeted conservation, specifically addressing road proximity, to effectively combat deforestation in North-East India and similar regions.