Analysis of Mobility Changes Caused by COVID-19 in a Context of Moderate Restrictions Using Data Collected by Mobile Devices

Article Computer Science, Information Systems

Comparative Analysis of Geolocation Information through Mobile-Devices under Different COVID-19 Mobility Restriction Patterns in Spain

Raquel Perez-Arnal et al.

Summary: The COVID-19 pandemic is reshaping the world in unprecedented ways, with human mobility playing a crucial role in virus transmission. Understanding various mobility data sources is essential for evaluating policies and predicting future crisis responses. Mobile data analysis in Spain has shown that private sources can complement public data and provide valuable insights into the new normality post-pandemic.

ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION (2021)

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Article Construction & Building Technology

Multi-source sensor based urban habitat and resident health sensing: A case study of Wuhan, China

Yan Zhang et al.

Summary: This study focuses on the relationship between urban living environment and the spread of COVID-19 at the community scale. Through analysis and modeling, it is found that residential density and living convenience are important driving factors for community infection. Additionally, the impact of sunlight exposure on virus transmission becomes a key focus of the research.

BUILDING AND ENVIRONMENT (2021)

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Article Environmental Sciences

Extracting Road Traffic Volume in the City before and during covid-19 through Video Remote Sensing

Elzbieta Macioszek et al.

Summary: The translated passage discusses the importance of continuous, automatic measurements of road traffic volume in obtaining information, as well as the impact of the Covid-19 pandemic on travel behavior on the global transport network. The article compares traffic characteristics for 2019 and 2020, analyzes changes in traffic volume, and attempts to estimate daily traffic patterns during pandemic stages.

REMOTE SENSING (2021)

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Proceedings Paper Computer Science, Hardware & Architecture

Poster: COVID-19 Case Prediction using Cellular Network Traffic

Necati Ayan et al.

Summary: This study aims to model and forecast the number of COVID-19 infections in the future using cellular network traffic data. By partnering with a major cellular network provider in Brazil, TIM Brazil, and analyzing network connections in Rio de Janeiro, a Markovian model was developed to capture individual mobility across municipalities. This model combined mobility characteristics and reported COVID-19 cases to predict future cases, outperforming a baseline linear regression model in terms of accuracy metrics such as RMSE and MAE.

2021 IFIP NETWORKING CONFERENCE AND WORKSHOPS (IFIP NETWORKING) (2021)

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Review Computer Science, Information Systems

Machine Learning on the COVID-19 Pandemic, Human Mobility and Air Quality: A Review

Md. Mokhlesur Rahman et al.

Summary: The global COVID-19 pandemic is impacting various aspects of human lives, with lockdown measures affecting virus transmission, human mobility, and air quality. Studies show that urban form, socioeconomic conditions, social cohesion, and social distancing measures significantly influence viral transmission and human mobility during the pandemic.

IEEE ACCESS (2021)

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Editorial Material Public, Environmental & Occupational Health