Livelihood security and its determinants among farmers during COVID-19 pandemic in Telangana, India

With COVID-19 outbreak globally several studies on livelihoods and food systems are conducted in consistent manner. India being an agrarian economy, the impact of pandemic on agricultural sector and farmers needs a great focus. The present exploratory study on Livelihood security (LS) was carried out in Telangana, India among Suryapet and Rangareddy districts purposively with 160 respondents selected through multistage random sampling during 2021. Livelihood Security Index was used with 7 sub-indicators and it depicted that only one sub-indicator i.e., economic security contributing less than 50% to LS and majority of the respondents have moderate level of livelihood security (42.5%) with overall mean value of 0.628. The determinants of LS were identifi ed through Multivariate regression analysis model and found 14 predictors were fitted in model responsible for 62.8% variance in the dependent variable (LS). The regression model revealed that Family size, educational years, Livestock holding, Social Participation, annual family income, mass media exposure, and Risk orientation were most aided predictor variables in the improvement of livelihood security during COVID-19 with educational years having greater beta-value. R2 standardized linear graph was plotted against the LS and the significant predictor value demonstrating the distribution of respondents over graph according to their livelihood security with R2 linear value 0.606. Thus, during COVID-19 more livelihood options for diversification of income can aid in increase of LS. The present study concluded by suggesting policy implications to ensure livelihood security among farming communities during pandemic.

Predicting Covid-19 Trends Using Machine Learning based on Linear and Multiple Linear Regression Model

The COVID-19 pandemic has adversely affected the health and economy of almost all the countries in the world including India. Almost thousands of people are getting affected by this daily. In this paper, analysis of the daily statistics of people who got affected and this proposed work is going to predict the future trend of the active cases in Odisha and India. Machine Learning based forecasting algorithms have proved their significance in generating predictive outcomes which are used to make decisions on actions that are going to happen in the future. ML algorithms have been using for a long time to do this kind of task. This proposed work is going to do analysis and prediction on the dataset which was created by COVID India organization. Linear and Multiple Linear Regression models are used to predict the future trend of active cases and also the number of active cases in fore coming days and to visualize the trend of future active cases. Here, the performance of Linear and Multiple Linear regression models are compared by using the R2score. Linear and Multiple Linear regression got 0.99 and 1.0 as R2scores respectively which shows that these are the strongest prediction models that are used to predict the future active cases of COVID - 19. Both these models acquired remarkable accuracy in COVID - 19 prediction. A strong correlation factor shows that there is a very strong relationship between a dependent variable (Active cases) and independent variables (positive, deceases, recovered cases). © 2022 University of Stockholm. All rights reserved.