Human behavior in the time of COVID-19: Learning from big data

Abstract

Since the World Health Organization (WHO) characterized COVID-19 as a pandemic in March 2020, there have been over 600 million confirmed cases of COVID-19 and more than six million deaths as of October 2022. The relationship between the COVID-19 pandemic and human behavior is complicated. On one hand, human behavior is found to shape the spread of the disease. On the other hand, the pandemic has impacted and even changed human behavior in almost every aspect. To provide a holistic understanding of the complex interplay between human behavior and the COVID-19 pandemic, researchers have been employing big data techniques such as natural language processing, computer vision, audio signal processing, frequent pattern mining, and machine learning. In this study, we present an overview of the existing studies on using big data techniques to study human behavior in the time of the COVID-19 pandemic. In particular, we categorize these studies into three groups—using big data to measure, model, and leverage human behavior, respectively. The related tasks, data, and methods are summarized accordingly. To provide more insights into how to fight the COVID-19 pandemic and future global catastrophes, we further discuss challenges and potential opportunities.

Utilization of smart devices and the evolution of customized healthcare services focusing on big data: a systematic review

“…We demonstrate the broad applicability of big data in studying human behavior during the COVID-19 pandemic. Methods, measurements, and modeling of utilizing human behavior in epidemiology are powerful yet show room for improvement, and investigated methods and applications can provide insight to cope with current epidemics and future disasters ( 29 ). The evolution of health promotion services needs to be developed for the elderly in line with an aging society, and the potential for digitalization is obviously great, however, to achieve this, additional research and analysis that is suitable for the elderly and explores the age gap is needed.…”

A dynamic ensemble model for short-term forecasting in pandemic situations

“…In the literature, such ensemble methods often use the mean (e.g., [11]) or median (e.g., [10]) of the base model outputs. However, pandemics like COVID-19 are dynamic, there are times when the number of cases barely changes, there is exponential growth and decay, and there are turning points of waves, which can all depend on external factors like interventions [13,14], people's behavior [15], seasonality [16,17], or variants of concern [18,19].…”

Why motivation for adhering to COVID-19 measures matters: Investigating time-lagged associations with epidemiological indicators.

“…The Role of Human Behavior in the Epidemiology of the SARS-CoV-2 Pandemic Over time, statisticians attempted to improve the prediction of SARS-CoV-2 evolution by including people's behavior as a preceding parameter of diagnosed infections. They did so based on simulated data (Gutiérrez-Jara et al, 2022), big data techniques (Lyu et al, 2023), and mobility data (Cot et al, 2021;Hu et al, 2021;Ilin et al, 2021;Mohammadi et al, 2022). To illustrate, mathematical models showed that compliance to self-isolation at home was negatively related to infection and hospitalization rates a few weeks later (e.g., Margraf et al, 2021;Talic et al, 2021), with individual-level interventions showing suppressed SARS-CoV-2 transmission (e.g., Doung-Ngern et al, 2020;Dwomoh et al, 2021).…”