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Foreign direct investment (FDI) inflows have been a trigger for accelerating economic growth in a number of countries. The pattern of FDI flows into India and its neighbourhood has been varied and so has been its impact on the economic growth in each of the countries. Although a lot of research has been carried out to establish causality between FDI and economic growth, the results are sometimes varied and conflicting. This study attempted to study the pattern of FDI into the Indian subcontinent and India’s neighbours, such as Pakistan, Nepal, Bangladesh and Sri Lanka, and explore the causality between FDI and gross domestic product (GDP). The results showed that the different economic policies of the respective countries had a role to play in explaining the difference in the quantum of the flow and there is an association between FDI and GDP, and in all the cases, FDI is instrumental in enhancing the economic growth of the countries included in the study.

Background- With the COVID-19 pandemic wreaking havoc across nations, several research projects are being carried out to study the propagation of the virus. In this study we have made an endeavour to analyse the spread of COVID-19 in the districts of India. Methods- Some districts in India have been much more a ected than the others. A cluster analysis of the worst a ected districts in India provide insight about the similarities between them. The e ects of public health interventions in flattening the curve in their respective states is studied using the individual contact SEIQHRF model. Results - The clustering of hotspot districts in India provide homogeneous clusters of districts that stand out in terms of number of positive COVID-19 cases and covariates like population density and number of COVID-19 special hospitals. The cluster analysis reveal that distribution of number of COVID-19 hospitals in the districts vary from the distribution of con rmed COVID-19 cases. The distribution of hospitals is much less skewed than the population density and COVID-19 cases. From the SEIQHRF model for Nizamuddin we observe in the second phase the number of infected individuals had seen a multitudinous increase in the states where Nizamuddin attendees returned. Thereby, increasing the risk of the disease spread in the respective states. However, the simulations reveal that the administrative interventions, if implemented strictly, flatten the curve of disease spread. In Dharavi however, as claimed by the Brihanmumbai Municipal Corporation officials, through tracing, tracking, testing and treating, massive breakout of COVID-19 was also brought under control. Conclusions - The study rounds up with two important case studies on Nizamuddin basti and Dharavi slum to illustrate the growth curve of COVID-19 in two very densely populated regions in India. In the case of Nizamuddin, the attendees of the religious events who went back to their respective states, increased the risk of infection manifold. However, Dharavi was one of the few COVID-19 success stories. Through strict testing, treating, tracking and tracing large-scale COVID-19 infection was brought under control.

Background In this study we cluster the districts of India in terms of the spread of COVID-19 and related variables such as population density and the number of specialty hospitals. Simulation using a compartment model is used to provide insight into differences in response to public health interventions. Two case studies of interest from Nizamuddin and Dharavi provide contrasting pictures of the success in curbing spread. Methods A cluster analysis of the worst affected districts in India provides insight about the similarities between them. The effects of public health interventions in flattening the curve in their respective states is studied using the individual contact SEIQHRF model, a stochastic individual compartment model which simulates disease prevalence in the susceptible, infected, recovered and fatal compartments. Results The clustering of hotspot districts provide homogeneous groups that can be discriminated in terms of number of cases and related covariates. The cluster analysis reveal that the distribution of number of COVID-19 hospitals in the districts does not correlate with the distribution of confirmed COVID-19 cases. From the SEIQHRF model for Nizamuddin we observe in the second phase the number of infected individuals had seen a multitudinous increase in the states where Nizamuddin attendees returned, increasing the risk of the disease spread. However, the simulations reveal that implementing administrative interventions, flatten the curve. In Dharavi, through tracing, tracking, testing and treating, massive breakout of COVID-19 was brought under control. Conclusions The cluster analysis performed on the districts reveal homogeneous groups of districts that can be ranked based on the burden placed on the healthcare system in terms of number of confirmed cases, population density and number of hospitals dedicated to COVID-19 treatment. The study rounds up with two important case studies on Nizamuddin basti and Dharavi to illustrate the growth curve of COVID-19 in two very densely populated regions in India. In the case of Nizamuddin, the study showed that there was a manifold increase in the risk of infection. In contrast it is seen that there was a rapid decline in the number of cases in Dharavi within a span of about one month.