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The economic downturn and the air travel crisis triggered by the recent coronavirus pandemic pose a substantial threat to the new consumer class of many emerging economies. In Brazil, considerable improvements in social inclusion have…

Causally identifying the effect of digital advertising is challenging, because experimentation is expensive, and observational data lacks random variation. This paper identifies a pervasive source of naturally occurring, quasi-experimental…

The declaration of COVID-19 as a pandemic has largely amplified the spread of related information on social media, such as Twitter, Facebook, and WeChat.Unlike the previous studies which focused on how to detect the misinformation or fake…

During the 2020 pandemic caused by the COVID-19 virus, many countries implemented stay-at-home measures, which led to many businesses and schools moving from in-person to online mode of operation. We analyze sampled Netflow records at a…

The long duration of the COVID-19 pandemic allowed for multiple bursts in the infection and death rates, the so-called epidemic waves. This complex behavior is no longer tractable by simple compartmental model and requires more…

The COVID-19 pandemic has affected countries across the world, demanding drastic public health policies to mitigate the spread of infection, leading to economic crisis as a collateral damage. In this work, we investigated the impact of…

Background: The COVID-19 pandemic has highlighted the need for robust diagnostic tools capable of detecting the disease from diverse and evolving data sources. Machine learning models, especially convolutional neural networks (CNNs), have…

Predicting if passengers in a connecting flight will lose their connection is paramount for airline profitability. We present novel machine learning-based decision support models for the different stages of connection flight management,…

We explore the causal relationship between COVID-19 lockdown policies and changes in personal computer usage. In particular, we examine how lockdown policies affected average daily computer usage, as well as how it affected usage patterns…

Rapidly scaling screening, testing and quarantine has shown to be an effective strategy to combat the COVID-19 pandemic. We consider the application of deep learning techniques to distinguish individuals with COVID from non-COVID by using…

This paper analyzes the impact of COVID-19 related lockdowns in the Atlanta, Georgia metropolitan area by examining commuter patterns in three periods: prior to, during, and after the pandemic lockdown. A cellular phone location dataset is…

Global problems, such as pandemics and climate change, require rapid international coordination and diffusion of policy. These phenomena are rare however, with one notable example being the international policy response to the COVID-19…

Distribution shift occurs when the test distribution differs from the training distribution, and it can considerably degrade performance of machine learning models deployed in the real world. Temporal shifts -- distribution shifts arising…

COVID-19 has deeply affected daily life and travel behaviors. Understanding these changes is crucial, prompting an investigation into socio-demographic and socio-economic factors. This study used large-scale mobile device location data in…

In the survey we consider the case studies on sales time series forecasting, the deep learning approach for forecasting non-stationary time series using time trend correction, dynamic price and supply optimization using Q-learning, Bitcoin…

With the outbreak of the COVID-19 pandemic in 2020, most colleges and universities move to restrict campus activities, reduce indoor gatherings and move instruction online. These changes required that students adapt and alter their daily…

It has been noticed that through COVID-19 greenhouse gas emissions had a sudden reduction. Based on this significant observation, we decided to conduct a research to quantify the impact of scientific conferences' air-travelling, explore and…

This study examines the impact of the COVID-19 pandemic on cybersecurity and data breaches, with a specific focus on the shift toward remote work. The study identifies trends and offers insights into cybersecurity incidents by analyzing…

Many consumer decisions are repeated choices under uncertainty. Standard models capture these decisions using Bayesian learning and dynamic programming: consumers update beliefs from feedback and use those beliefs to guide future choices.…

While COVID-19 has impacted humans for a long time, people search the web for pandemic-related information, causing anxiety. From a theoretic perspective, previous studies have confirmed that the number of COVID-19 cases can cause negative…