Impact of Later-Stages COVID-19 Response Measures on Spatiotemporal Mobile Service Usage

Zanella, Andre Felipe; Martinez-Durive, Orlando E.; Mishra, Sachit; Smoreda, Zbigniew; Fiore, Marco

doi:10.1109/infocom48880.2022.9796888

Cited by 5 publications

(2 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As a consequence, restrictions reshaped the way individuals use and access digital infrastructure and services. For instance, in France during the second phase of COVID-19, it was observed that big and metropolitan cities generate less mobile traffic compared with the pre-pandemic era and vice versa for smaller cities and towns during the lockdown and also unveiled that some applications are less prevalent in large cities and are now more likely to be used in small towns and suburbs [33]. The Internet has also become the leading platform for imparting education during COVID-19 due to strict social distancing regulations.…”

Section: Digital Divides Under Covid-19mentioning

confidence: 99%

“…To answer the question above, we fit the univariate power law model, which proved to combine high quality and immediate interpretability, to traffic and population data from October 2020 to May 2021. During this period, the French local authorities and national government enacted a number of strategies to mitigate the diffusion of the virus [33]. Results, in terms of fitting quality and observed relationship between traffic and city size, are reported in Table 3, for both communes and urban units.…”

Section: Digital Divides Under Covid-19mentioning

confidence: 99%

See 1 more Smart Citation

Investigating Second-Level Digital Divides in Nationwide Mobile Service Consumption

Mishra,

Smoreda,

Fiore

2024

Preprint

View full text Add to dashboard Cite

The widespread availability of inexpensive mobile broadband has democratized access to digital services in developed countries. While this has supposedly closed digital divides among the population, more subtle inequalities may still be present that are not driven by the accessibility to mobile services, rather by the heterogeneous capability of individuals to take advantage from those services. This paper focuses on the existence of imbalances in the consumption of mobile services across France using mobile traffic measurement data. We unveil that per-capita mobile traffic demands follow in fact a power law of the local population size, or, equivalently, there exists an emergent behavior that prompts subscribers living in larger urban areas to exhibit higher individual usage of mobile services. The result holds for global traffic, but is consistent across various mobile services, although with varying intensity. Our findings reveal the presence of potential second-level digital divides in developed countries and, surprisingly, show that such divides have steadily increased in France between 2014 and 2019. Moreover, we show how the phenomenon defies explanation by potential confounding factors like broad-band coverage, socioeconomic status, commuting, or inhabitant’s age, but has been indirectly mitigated by the recent containment measures enacted to combat COVID-19.

show abstract

Section: Digital Divides Under Covid-19mentioning

confidence: 99%

Section: Digital Divides Under Covid-19mentioning

confidence: 99%

Investigating Second-Level Digital Divides in Nationwide Mobile Service Consumption

Mishra,

Smoreda,

Fiore

2024

Preprint

View full text Add to dashboard Cite

show abstract

Modeling Publication Collection and Processing

Cao

2023

Data Analytics

View full text Add to dashboard Cite

COVID-19 Modeling: A Review

Cao

Liu

2022

Preprint

View full text Add to dashboard Cite

The unprecedented and overwhelming SARS-CoV-2 virus and COVID-19 disease significantly challenge our life and the society and economy. Many questions emerge, a critical one is to quantify the challenges, realities, intervention effect and influence of the pandemic. With the massive effort in modeling COVID-19, what COVID-19 issues have been modeled? what and how well have epidemiology, AI, data science, machine learning, deep learning, mathematics and social science characterized the COVID-19 epidemic? what are the gaps and opportunities of quantifying the pandemic? Such questions involve a wide body of knowledge and literature, unclear but important for the present and future health crisis quantification. Here, we provide a comprehensive review of the challenges, tasks, methods, progress, gaps and opportunities in relation to modeling COVID-19 processes, data, mitigation and impact. With a research landscape of COVID-19 modeling, we further categorize, summarize, compare and discuss the related methods and progress of modeling COVID-19 epidemic transmission processes and dynamics, case identification and tracing, infection diagnosis and medical treatments, nonpharmaceutical interventions and their effects, drug and vaccine development, psychological, economic and social influence and impact, and misinformation, etc. The review shows how modeling methods such as mathematical and statistical models, domain-driven modeling by epidemiological compartmental models, medical and biomedical analysis, AI and data science in particular shallow and deep machine learning, simulation modeling, social science methods and hybrid modeling have addressed the COVID-19 challenges and what gaps and directions exist for better futures.

show abstract

Impact of Later-Stages COVID-19 Response Measures on Spatiotemporal Mobile Service Usage

Cited by 5 publications

References 15 publications

Investigating Second-Level Digital Divides in Nationwide Mobile Service Consumption

Investigating Second-Level Digital Divides in Nationwide Mobile Service Consumption

Modeling Publication Collection and Processing

COVID-19 Modeling: A Review

Contact Info

Product

Resources

About