Post-pandemic Economic Transformations in the United States of America

The COVID-19 pandemic has impacted economic activity not only in the United States, but across the globe. Lockdown and travel restrictions imposed by local authorities have led to change in customer preferences and thus transformation of economic activity from traditional areas to new regions. While most changes have been temporary and short term, some of them have been observed to be of permanent nature. Using large-scale aggregated and anonymized transaction data across various socio-economic groups, we analyse and discuss such temporary relocation of citizens' economic activities in metropolitan areas of 15 states in the US. The results of this study have extensive implications for urban planners and business owners, and can provide insights into the temporary relocation of economic activities resulting from an extreme exogenous shock like the COVID-19 pandemic. © 2022 IEEE.

Effects of stimulus checks on spending patterns of different economic groups

This paper uses daily anonymous aggregated trans-action data to analyze the changes in consumer spending caused by receipt of the stimulus payments in the United States during the COVID-19 pandemic. The stimulus checks were provided as part of the CARES Act aiming to provide emergency assistance for individuals and businesses affected by the pandemic. We analyze the impact of the receipt of those payments on the aggregated daily spending of different socio-economic groups and industries. We show that the transaction patterns of low spending consumers were most impacted by the stimulus payments among different spending groups. Our study results also indicate that the consumer responses after the first stimulus check (April 2020) were substantial and significant on industries that sell daily essential items, whereas consumer responses after the third stimulus check (March 2021) were significant in non-essential goods (e.g. luxury and entertainment sector). The results of this study are of crucial importance because they could help policy makers better shape stimulus payments that may be needed in future emergencies. © 2021 IEEE.

Pandemic spread prediction and healthcare preparedness through financial and mobility data

The pandemics like Coronavirus disease 2019 (COVID-19) require Governments and health professionals to make time-sensitive, critical decisions about travel restrictions and resource allocations. This paper identifies various factors that affect the spread of the disease using transaction data and proposes a model to predict the degree of spread of the disease and thus the number of medical resources required in upcoming weeks. We perform a region-wise analysis of these factors to identify the control measures that affect the minimal set of population. Our model also helps in estimating the surges in clinical demand and identifying when the medical resources would be saturated. Using this estimate, we suggest the preventive as well as corrective measures to avoid critical situations. © 2020 IEEE.