Background The coronavirus disease (COVID-19) pandemic has affected more than 200 countries and has infected more than 2,800,000 people as of April 24, 2020. It was first identified in Wuhan City in China in December 2019. Objective The aim of this study is to identify the top 15 countries with spatial mapping of the confirmed cases. A comparison was done between the identified top 15 countries for confirmed cases, deaths, and recoveries, and an advanced autoregressive integrated moving average (ARIMA) model was used for predicting the COVID-19 disease spread trajectories for the next 2 months. Methods The comparison of recent cumulative and predicted cases was done for the top 15 countries with confirmed cases, deaths, and recoveries from COVID-19. The spatial map is useful to identify the intensity of COVID-19 infections in the top 15 countries and the continents. The recent reported data for confirmed cases, deaths, and recoveries for the last 3 months was represented and compared between the top 15 infected countries. The advanced ARIMA model was used for predicting future data based on time series data. The ARIMA model provides a weight to past values and error values to correct the model prediction, so it is better than other basic regression and exponential methods. The comparison of recent cumulative and predicted cases was done for the top 15 countries with confirmed cases, deaths, and recoveries from COVID-19. Results The top 15 countries with a high number of confirmed cases were stratified to include the data in a mathematical model. The identified top 15 countries with cumulative cases, deaths, and recoveries from COVID-19 were compared. The United States, the United Kingdom, Turkey, China, and Russia saw a relatively fast spread of the disease. There was a fast recovery ratio in China, Switzerland, Germany, Iran, and Brazil, and a slow recovery ratio in the United States, the United Kingdom, the Netherlands, Russia, and Italy. There was a high death rate ratio in Italy and the United Kingdom and a lower death rate ratio in Russia, Turkey, China, and the United States. The ARIMA model was used to predict estimated confirmed cases, deaths, and recoveries for the top 15 countries from April 24 to July 7, 2020. Its value is represented with 95%, 80%, and 70% confidence interval values. The validation of the ARIMA model was done using the Akaike information criterion value; its values were about 20, 14, and 16 for cumulative confirmed cases, deaths, and recoveries of COVID-19, respectively, which represents acceptable results. Conclusions The observed predicted values showed that the confirmed cases, deaths, and recoveries will double in all the observed countries except China, Switzerland, and Germany. It was also observed that the death and recovery rates were rose faster when compared to confirmed cases over the next 2 months. The associated mortality rate will be much higher in the United States, Spain, and Italy followed by France, Germany, and the United Kingdom. The forecast analysis of the COVID-19 dynamics showed a different angle for the whole world, and it looks scarier than imagined, but recovery numbers start looking promising by July 7, 2020.

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The study has analysed the changes in structure of rural credit delivery and inclusiveness of rural credit flow across states and social groups, along with identification of factors that influence the choice of credit source. The study is based on the unit level data of Debt and Investment Survey carried out by NSSO during 1992 (48 th round), 2003 (59 th round) and 2013 (70 th round). The structure of credit system has been assessed in terms of access of rural households to different credit outlets, share of formal credit institutions, availability of credit, and interest rate. The determinants of rural households' choice for credit sources have also been studied. The study has found that the structure of credit market has changed over time and the share of institutional credit has increased. The initiatives taken by the government have paid off and the flow of institutional credit to rural areas has increased significantly even in real terms. The indicators of financial inclusion have shown a sign of improvement. However, regional disparity and presence of informal agencies in the disbursement of rural credit is still persistent. Rural households' access to institutional credit is influenced by a number of socioeconomic , institutional and policy factors. In our analysis, the education, caste affiliation, gender and assets ownership have been found to influence the rural households' access to institutional credit significantly. A concerted effort and appropriate policy reform are required to make rural households' access to institutional credit neutral to caste, class and regions.

The ongoing pandemic of the coronavirus disease 2019 started in China and devastated a vast majority of countries. In India, COVID-19 cases are steadily increasing since January 30, 2020, and the government-imposed lockdown across the country to curtail community transmission. COVID-19 forecasts have played an important role in capturing the probability of infection and the basic reproduction rate. In this study, we predicted some trajectories of trajectories associated with COVID-19 in the coming days in India using an Autoregression integrated moving average model (ARIMA) and Richard's model. By the end of April 2020, the incidence of new cases is predicted to be 5200 (95% CI: 4650 to 6002) through the ARIMA model versus be 6378 (95% CI: 4904 to 7851) Richard model. We estimated that there would be a total of 197 (95% CI: 118 to 277) deaths and drop down in the recovery rates will reach around 501 (95% CI: 245 to 758) by the end of April 2020. These estimates can help to strengthen the implementation of strategies to increase the health system capacity and enactment of social distancing measures all over India.

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The linkage between agriculture and nutrition is complex and often debated in the policy discourse in India. The enigma of fastest growing economy and yet the largest home of under-and mal-nourished population takes away the sheen from the glory of economic achievements of India. In this context, the study has examined the food consumption patterns, assessed the relationship between agricultural production and dietary diversity, and analysed the impact of dietary diversity on nutritional intake. The study is based on a household level panel data from 12 villages of Bihar, Jharkhand and Odisha in eastern India. The study has shown that agricultural production diversity is a major determinant of dietary diversity which in turn has a strong effect on calorie and protein intake. The study has suggested that efforts to promote agricultural diversification will be helpful to enhance food and nutrition security in the country. Agricultural programmes and policies oriented towards reducing under-nutrition should promote diversity in agricultural production rather than emphasizing on increasing production through focusing on selected staple crops as has been observed in several states of India. The huge fertilizer subsidies and government procurement schemes limited to a few crops provide little incentives for farmers to diversity their production portfolio.

Dairy farmers in Bihar are mostly smallholders having one or two local-bred milch animals, which are raised on crop residues and natural pastures with under-employed family labour. Feeding grains, oil cakes and green nutritious fodder are limited to crossbred cattle. Feed and fodder deficiencies are major limiting factors in raising livestock productivity. Fodder markets are important for communities, which have limited ability to produce their own fodder, but need quality fodder at reasonable prices to produce milk at competitive cost and trading is an important livelihood activity for poor who engaged in it. The study tries find ways to improve the livelihoods of resource-poor livestock producers by alleviating fodder scarcity. Livestock being an important source of livelihood in Bihar, the study has a direct poverty relevance for state. The findings indicate a huge gap between demand and supply of both dry and green fodder. South Bihar is fodder surplus area because of irrigated cultivation of paddy and wheat, while north Bihar is fodder deficit and depend on fodder surplus regions. There are no dedicated market places so, trading takes place along roadsides and without legal credentials. Fodder being a bulky item, makes its trading and handling difficult. Some traders do use compressing machines to make fodder blocks. Development of technology for cost-effective and nutritive feed requires urgent attention and here public sector R&D can play an effective role which can also be done in public-private partnership mode.