Non-Intrusive Assessment of COVID-19 Lockdown Follow-Up and Impact Using Credit Card Information: Case Study in Chile

Abstract

In this paper, we propose and validate with data extracted from the city of Santiago, capital of Chile, a methodology to assess the actual impact of lockdown measures based on the anonymized and geolocated data from credit card transactions. Using unsupervised Latent Dirichlet Allocation (LDA) semantic topic discovery, we identify temporal patterns in the use of credit cards that allow us to quantitatively assess the changes in the behavior of the people under the lockdown measures because of the COVID-19 pandemic. An unsupervised latent topic analysis uncovers the main patterns of credit card transaction activity that explain the behavior of the inhabitants of Santiago City. The approach is non-intrusive because it does not require the collaboration of people for providing the anonymous data. It does not interfere with the actual behavior of the people in the city; hence, it does not introduce any bias. We identify a strong downturn of the economic activity as measured by credit card transactions (down to 70%), and thus of the economic activity, in city sections (communes) that were subjected to lockdown versus communes without lockdown. This change in behavior is confirmed by independent data from mobile phone connectivity. The reduction of activity emerges before the actual lockdowns were enforced, suggesting that the population was spontaneously implementing the required measures for slowing virus propagation.

Clustering Cities over Features Extracted from Multiple Virtual Sensors Measuring Micro-Level Activity Patterns Allows One to Discriminate Large-Scale City Characteristics

“…These patterns are important for public policy decisions related to urban phenomena such as public transport [37], traffic flow [38], flood risk management [39] and urban planning [40,41]. Human behavioral patterns have also been used to measure the effectiveness of pandemic policies [42,43].…”

“…However, there are methodological challenges in translating massive data sets into valuable insights, such as aggregating raw data into mobility patterns to monitor quarantine policies [43]. Despite these challenges, the potential of using these data sources to support policy-making is tremendous.…”

Direct indicators of social distancing effectiveness in COVID-19 outbreak stages: a correlational analysis of case contacts and population mobility in Korea

“…Social distancing, a key non-pharmaceutical intervention, helps decrease contact with carriers and is an essential public health and social measure in Korea [ 8 ]. Credit card usage and population movements from mobile geolocation data have been analyzed as indirect indicators of close contact to assess the impact of social distancing on SARS-CoV-2 spread [ 9 , 10 ]. Furthermore, the assessment of mobility patterns demonstrates the effectiveness of social distancing, and the transmission risk of COVID-19 can be assessed using data on average travel distance and residence time [ 11 , 12 ].…”

Comportamiento del consumidor hacia las compras en línea durante la Covid-19 en Latinoamérica: Revisión sistemática de la literatura

“…La pandemia provocada por el SARS-CoV-2 (Covid-19) comenzó en Wuhan-China en diciembre del 2019 y se expandió rápidamente a nivel mundial (Alves et al, 2021). Ha representado un desafío global debido a que la Covid-19 ha tenido varias olas que han golpeado de diferentes maneras en todos los países (Brożek et al, 2021), provocando que se determinen confinamientos para los casos por encima del umbral de contagios determinado (Muñoz-Cancino et al, 2021). En Latinoamérica, Brasil y Perú tuvieron los niveles más altos en términos de tasa de infecciones y mortalidad (Cequea et al, 2021), seguido en las siguientes posiciones por Ecuador como uno de los países más afectados en Sudamérica (Worldometer, 2020).…”