Exploring outliers in global economic dataset having the impact of COVID-19 pandemic

Outlier is a value that lies outside most of the other values in a dataset. Outlier exploration has a huge importance in almost all the industry applications like medical diagnosis, credit card fraudulence and intrusion detection systems. Similarly, in economic domain, it can be applied to analyse many unexpected events to harvest new knowledge like sudden crash of stock market, mismatch between country's per capita incomes and overall development, abrupt change in unemployment rate and steep falling of bank interest. These situations can arise due to several reasons, out of which the present COVID-19 pandemic is a leading one. This motivates the present researchers to identify a few such vulnerable areas in the economic sphere and ferret out the most affected countries for each of them. Two well-known machine-learning techniques DBSCAN and Z-score are utilised to get these insights, which can serve as a guideline towards improving the overall scenario subsequently. Copyright © 2023 Inderscience Enterprises Ltd.

Lockdown and Reopening the Economy Spatial Dynamics of COVID-19 for India

This article explores the spatial dynamics of COVID-19 - with nationwide and partial lockdowns' in its two waves, respectively - in India by employing the location quotient and univariate Moran's I statistics with various variables representing spatial adjacency, proximity, population, population density, urbanisation, migration, and health infrastructure variables. The results suggest that though geographical proximity to the hotspot states played an important role in triggering the outbreak during both the waves, it could not influence the spatial clustering at the sluggish phase of the pandemic. © 2021 Economic and Political Weekly. All rights reserved.