Using mobile phone data to estimate dynamic population changes and improve the understanding of a pandemic: A case study in Andorra.
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 an...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2022-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0264860 |
| _version_ | 1811296401113481216 |
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| author | Alex Berke Ronan Doorley Luis Alonso Vanesa Arroyo Marc Pons Kent Larson |
| author_facet | Alex Berke Ronan Doorley Luis Alonso Vanesa Arroyo Marc Pons Kent Larson |
| author_sort | Alex Berke |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-13T05:48:50Z |
| format | Article |
| id | doaj.art-c5c330b8ccce4e8aa3b8883e56b87822 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-04-13T05:48:50Z |
| publishDate | 2022-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-c5c330b8ccce4e8aa3b8883e56b878222022-12-22T02:59:52ZengPublic Library of Science (PLoS)PLoS ONE1932-62032022-01-01174e026486010.1371/journal.pone.0264860Using mobile phone data to estimate dynamic population changes and improve the understanding of a pandemic: A case study in Andorra.Alex BerkeRonan DoorleyLuis AlonsoVanesa ArroyoMarc PonsKent LarsonCompartmental 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.https://doi.org/10.1371/journal.pone.0264860 |
| spellingShingle | Alex Berke Ronan Doorley Luis Alonso Vanesa Arroyo Marc Pons Kent Larson Using mobile phone data to estimate dynamic population changes and improve the understanding of a pandemic: A case study in Andorra. PLoS ONE |
| title | Using mobile phone data to estimate dynamic population changes and improve the understanding of a pandemic: A case study in Andorra. |
| title_full | Using mobile phone data to estimate dynamic population changes and improve the understanding of a pandemic: A case study in Andorra. |
| title_fullStr | Using mobile phone data to estimate dynamic population changes and improve the understanding of a pandemic: A case study in Andorra. |
| title_full_unstemmed | Using mobile phone data to estimate dynamic population changes and improve the understanding of a pandemic: A case study in Andorra. |
| title_short | Using mobile phone data to estimate dynamic population changes and improve the understanding of a pandemic: A case study in Andorra. |
| title_sort | using mobile phone data to estimate dynamic population changes and improve the understanding of a pandemic a case study in andorra |
| url | https://doi.org/10.1371/journal.pone.0264860 |
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