scholarly journals Investigating the Mobile Phone Data to Estimate the Origin Destination Flow and Analysis; Case Study: Paris Region

2015 ◽  
Vol 6 ◽  
pp. 64-78 ◽  
Author(s):  
Anahid Nabavi Larijani ◽  
Ana-Maria Olteanu-Raimond ◽  
Julien Perret ◽  
Mathieu Brédif ◽  
Cezary Ziemlicki
Keyword(s):  
Mobile Phone ◽  
Mobile Phone Data ◽  
Paris Region

Assessing Refugees’ Onward Mobility with Mobile Phone Data—A Case Study of (Syrian) Refugees in Turkey

2019 ◽  
pp. 251-263
Author(s):  
Harald Sterly ◽  
Benjamin Etzold ◽  
Lars Wirkus ◽  
Patrick Sakdapolrak ◽  
Jacob Schewe ◽  
...  
Keyword(s):  
Mobile Phone ◽  
Mobile Phone Data ◽  
Syrian Refugees

scholarly journals Using mobile phone data to estimate dynamic population changes and improve the understanding of a pandemic: A case study in Andorra

2021 ◽  
Author(s):  
Alex A Berke ◽  
Ronan Doorley ◽  
Luis Alonso ◽  
Marc Pons ◽  
Vanesa Arroyo ◽  
...  
Keyword(s):  
Mobile Phone ◽  
Mobile Devices ◽  
Compartmental Models ◽  
Mobile Phone Data ◽  
Population Changes ◽  
Study Region ◽  
Testing Program ◽  
Dynamic Population ◽  
Serology Testing

Compartmental models are often used to understand and predict the progression of an infectious disease such as COVID-19. The most basic of these models consider the total population of a region to be closed. Many incorporate human mobility into their transmission dynamics, usually based on static and aggregated data. However, mobility can change dramatically during a global pandemic as seen with COVID-19, making static data unsuitable. Recently, large mobility datasets derived from mobile devices have been used, along with COVID-19 infections data, to better understand the relationship between mobility and COVID-19. However, studies to date have relied on data that represent only a fraction of their target populations, and the data from mobile devices have been used for measuring mobility within the study region, without considering changes to the population as people enter and leave the region. This work presents a unique case study in Andorra, with comprehensive datasets that include telecoms data covering 100% of mobile subscribers in the country, and results from a serology testing program that more than 90% of the population voluntarily participated in. We use the telecoms data to both measure mobility within the country and to provide a real-time census of people entering, leaving and remaining in the country. We develop multiple SEIR (compartmental) models parameterized on these metrics and show how dynamic population metrics can improve the models. We find that total daily trips did not have predictive value in the SEIR models while country entrances did. As a secondary contribution of this work, we show how Andorra's serology testing program was likely impacted by people leaving the country. Overall, this case study suggests how using mobile phone data to measure dynamic population changes could improve studies that rely on more commonly used mobility metrics and the overall understanding of a pandemic.

Urban spatiotemporal analysis using mobile phone data: Case study of medium- and large-sized Korean cities

2018 ◽  
Vol 73 ◽  
pp. 6-15 ◽  
Author(s):  
Kwang-Sub Lee ◽  
So Young You ◽  
Jin Ki Eom ◽  
Jiyoung Song ◽  
Jae Hong Min
Keyword(s):  
Mobile Phone ◽  
Spatiotemporal Analysis ◽  
Mobile Phone Data
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