Mobility data shows effectiveness of control strategies for COVID-19 in remote, sparse and diffuse populations
Data that is collected at the individual-level from mobile phones is typically aggregated to the population-level for privacy reasons. If we are interested in answering questions regarding the mean, or working with groups appropriately modeled by a continuum, then this data is immediately informativ...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-07-01
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| Series: | Frontiers in Epidemiology |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fepid.2023.1201810/full |
| _version_ | 1797270766289420288 |
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| author | Yuval Berman Shannon D. Algar David M. Walker Michael Small Michael Small |
| author_facet | Yuval Berman Shannon D. Algar David M. Walker Michael Small Michael Small |
| author_sort | Yuval Berman |
| collection | DOAJ |
| description | Data that is collected at the individual-level from mobile phones is typically aggregated to the population-level for privacy reasons. If we are interested in answering questions regarding the mean, or working with groups appropriately modeled by a continuum, then this data is immediately informative. However, coupling such data regarding a population to a model that requires information at the individual-level raises a number of complexities. This is the case if we aim to characterize human mobility and simulate the spatial and geographical spread of a disease by dealing in discrete, absolute numbers. In this work, we highlight the hurdles faced and outline how they can be overcome to effectively leverage the specific dataset: Google COVID-19 Aggregated Mobility Research Dataset (GAMRD). Using a case study of Western Australia, which has many sparsely populated regions with incomplete data, we firstly demonstrate how to overcome these challenges to approximate absolute flow of people around a transport network from the aggregated data. Overlaying this evolving mobility network with a compartmental model for disease that incorporated vaccination status we run simulations and draw meaningful conclusions about the spread of COVID-19 throughout the state without de-anonymizing the data. We can see that towns in the Pilbara region are highly vulnerable to an outbreak originating in Perth. Further, we show that regional restrictions on travel are not enough to stop the spread of the virus from reaching regional Western Australia. The methods explained in this paper can be therefore used to analyze disease outbreaks in similarly sparse populations. We demonstrate that using this data appropriately can be used to inform public health policies and have an impact in pandemic responses. |
| first_indexed | 2024-04-25T02:09:29Z |
| format | Article |
| id | doaj.art-5d7c6a491c9743f796f32fcc000a5055 |
| institution | Directory Open Access Journal |
| issn | 2674-1199 |
| language | English |
| last_indexed | 2024-04-25T02:09:29Z |
| publishDate | 2023-07-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Epidemiology |
| spelling | doaj.art-5d7c6a491c9743f796f32fcc000a50552024-03-07T12:37:33ZengFrontiers Media S.A.Frontiers in Epidemiology2674-11992023-07-01310.3389/fepid.2023.12018101201810Mobility data shows effectiveness of control strategies for COVID-19 in remote, sparse and diffuse populationsYuval Berman0Shannon D. Algar1David M. Walker2Michael Small3Michael Small4Complex Systems Group, Department of Mathematics and Statistics, University of Western Australia, Perth, WA, AustraliaComplex Systems Group, Department of Mathematics and Statistics, University of Western Australia, Perth, WA, AustraliaComplex Systems Group, Department of Mathematics and Statistics, University of Western Australia, Perth, WA, AustraliaComplex Systems Group, Department of Mathematics and Statistics, University of Western Australia, Perth, WA, AustraliaCSIRO, Kensington, WA, AustraliaData that is collected at the individual-level from mobile phones is typically aggregated to the population-level for privacy reasons. If we are interested in answering questions regarding the mean, or working with groups appropriately modeled by a continuum, then this data is immediately informative. However, coupling such data regarding a population to a model that requires information at the individual-level raises a number of complexities. This is the case if we aim to characterize human mobility and simulate the spatial and geographical spread of a disease by dealing in discrete, absolute numbers. In this work, we highlight the hurdles faced and outline how they can be overcome to effectively leverage the specific dataset: Google COVID-19 Aggregated Mobility Research Dataset (GAMRD). Using a case study of Western Australia, which has many sparsely populated regions with incomplete data, we firstly demonstrate how to overcome these challenges to approximate absolute flow of people around a transport network from the aggregated data. Overlaying this evolving mobility network with a compartmental model for disease that incorporated vaccination status we run simulations and draw meaningful conclusions about the spread of COVID-19 throughout the state without de-anonymizing the data. We can see that towns in the Pilbara region are highly vulnerable to an outbreak originating in Perth. Further, we show that regional restrictions on travel are not enough to stop the spread of the virus from reaching regional Western Australia. The methods explained in this paper can be therefore used to analyze disease outbreaks in similarly sparse populations. We demonstrate that using this data appropriately can be used to inform public health policies and have an impact in pandemic responses.https://www.frontiersin.org/articles/10.3389/fepid.2023.1201810/fullCOVID-19Google COVID-19 Aggregated Mobility Research DatasetGAMRDsparse populationcompartmental modelWestern Australia |
| spellingShingle | Yuval Berman Shannon D. Algar David M. Walker Michael Small Michael Small Mobility data shows effectiveness of control strategies for COVID-19 in remote, sparse and diffuse populations Frontiers in Epidemiology COVID-19 Google COVID-19 Aggregated Mobility Research Dataset GAMRD sparse population compartmental model Western Australia |
| title | Mobility data shows effectiveness of control strategies for COVID-19 in remote, sparse and diffuse populations |
| title_full | Mobility data shows effectiveness of control strategies for COVID-19 in remote, sparse and diffuse populations |
| title_fullStr | Mobility data shows effectiveness of control strategies for COVID-19 in remote, sparse and diffuse populations |
| title_full_unstemmed | Mobility data shows effectiveness of control strategies for COVID-19 in remote, sparse and diffuse populations |
| title_short | Mobility data shows effectiveness of control strategies for COVID-19 in remote, sparse and diffuse populations |
| title_sort | mobility data shows effectiveness of control strategies for covid 19 in remote sparse and diffuse populations |
| topic | COVID-19 Google COVID-19 Aggregated Mobility Research Dataset GAMRD sparse population compartmental model Western Australia |
| url | https://www.frontiersin.org/articles/10.3389/fepid.2023.1201810/full |
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