| Title:
|
Human movement can inform the spatial scale of interventions against COVID-19 transmission |
| Abstract:
|
The UK enacted an intensive nationwide lockdown on March 23 2020 to mitigate transmission of COVID-19. As restrictions began to ease resurgence in transmission has been targeted by geographically-limited interventions of various stringencies. Determining the optimal spatial scale for local interventions is critical to ensure interventions reach the most at risk areas without unnecessarily restricting areas at low risk of resurgence. Here we use detailed human mobility data from Facebook to determine the spatially-explicit network community structure of the UK before and during the lockdown period and how that has changed in response to the easing of restrictions and to locally-targeted interventions. We found that the mobility network became more sparse and the number of mobility communities decreased under the national lockdown. During this period there was no evidence of re-routing in the network. Communities in which locally-targeted interventions have happened following resurgence did not show reorganization but did show small decreases in measurable mobility effects in the Facebook dataset. We propose that geographic communities detected in Facebook or other mobility data be part of decision making for determining the spatial extent or boundaries of interventions in the UK. These data are available in near real-time and allow quantification of changes in the distribution of the population across the UK as well as people's travel patterns to give data-driven metrics for geographically-targeted interventions. |
| Published:
|
2020-10-27 |
| DOI:
|
10.1101/2020.10.26.20219550 |
| DOI_URL:
|
http://doi.org/10.1101/2020.10.26.20219550 |
| Author Name:
|
Gibbs H |
| Author link:
|
https://covid19-data.nist.gov/pid/rest/local/author/gibbs_h |
| Author Name:
|
Nightingale E |
| Author link:
|
https://covid19-data.nist.gov/pid/rest/local/author/nightingale_e |
| Author Name:
|
Liu Y |
| Author link:
|
https://covid19-data.nist.gov/pid/rest/local/author/liu_y |
| Author Name:
|
Cheshire J |
| Author link:
|
https://covid19-data.nist.gov/pid/rest/local/author/cheshire_j |
| Author Name:
|
Danon L |
| Author link:
|
https://covid19-data.nist.gov/pid/rest/local/author/danon_l |
| Author Name:
|
Smeeth L |
| Author link:
|
https://covid19-data.nist.gov/pid/rest/local/author/smeeth_l |
| Author Name:
|
Pearson C A |
| Author link:
|
https://covid19-data.nist.gov/pid/rest/local/author/pearson_c_a |
| Author Name:
|
Grundy C |
| Author link:
|
https://covid19-data.nist.gov/pid/rest/local/author/grundy_c |
| Author Name:
|
LSHTM CMMID COVID Working Group |
| Author link:
|
https://covid19-data.nist.gov/pid/rest/local/author/lshtm_cmmid_covid_working_group |
| Author Name:
|
Kucharski A J |
| Author link:
|
https://covid19-data.nist.gov/pid/rest/local/author/kucharski_a_j |
| Author Name:
|
Eggo R M |
| Author link:
|
https://covid19-data.nist.gov/pid/rest/local/author/eggo_r_m |
| sha:
|
6acfebe3d1aabd936a21e35b10ee86cdc8f5b7d0 |
| license:
|
medrxiv |
| source_x:
|
MedRxiv; WHO |
| source_x_url:
|
https://www.who.int/ |
| url:
|
https://doi.org/10.1101/2020.10.26.20219550
http://medrxiv.org/cgi/content/short/2020.10.26.20219550v1?rss=1 |
| has_full_text:
|
TRUE |
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Fig 1b
https://doi.org/10.1101/2020.
pre-lockdown
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medRxiv preprint
https://doi.org/10.1101/2020.10.26.20219550 doi
betweenness centrality.
Fig 5a & b
blue lines
network
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medRxiv preprint / 4b
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https://github.com/hamishgibbs/facebook_mobility_uk
Figure 4a ,
mid-lockdown
COVID-19 |
| Extracted Text Content in Record:
|
First 5000 Characters:The UK enacted an intensive, nationwide lockdown on March 23 2020 to mitigate transmission of COVID-19. As restrictions began to ease, resurgence in transmission has been targeted by geographically-limited interventions of various stringencies. Determining the optimal spatial scale for local interventions is critical to ensure interventions reach the most at risk areas without unnecessarily restricting areas at low risk of resurgence. Here we use detailed human mobility data from Facebook to determine the spatially-explicit network community structure of the UK before and during the lockdown period, and how that has changed in response to the easing of restrictions and to locally-targeted interventions. We found that the mobility network became more sparse and the number of mobility communities decreased under the national lockdown. During this period, there was no evidence of re-routing in the network. Communities in which locally-targeted interventions have happened following resurgence did not show reorganization but did show small decreases in measurable mobility effects in the Facebook dataset. We propose that geographic communities detected in Facebook or other mobility data be part of decision making for determining the spatial extent or boundaries of interventions in the UK. These data are available in near real-time, and allow quantification of changes in the distribution of the population across the UK, as well as people's travel patterns to give data-driven metrics for geographically-targeted interventions.
Large-scale intensive interventions in response to the COVID-19 pandemic have affected human movement patterns. Mobility data show spatially-explicit network structure, but it is not clear if that structure changed in response to national or locally-targeted interventions. We used daily Facebook for Good mobility data to quantify changes in the travel network in the UK during the national lockdown, and in response to local interventions. The network community structure inherent in these networks can help quantify which areas are at risk of resurgence, or the extent of locally-targeted interventions aiming to suppress transmission. We showed that spatial mobility data available in real-time can give information on connectivity that can be used to optimise the scale of geographically-targeted interventions.
Fine-scale geographic monitoring of large populations provides a valuable resource for increasing the accuracy and responsiveness of epidemiological modelling, outbreak response, and intervention planning in response to public health emergencies like the COVID-19
pandemic (1) (2) (3) (4) (5) . Population and mobility datasets collected from the movement of individuals' mobile phones provide empirical, near-real time metrics of population movement between different geographic regions. (6) The COVID-19 pandemic response could potentially benefit from the availability of new data sources for measuring human movement, aggregated from mobile devices by network providers and popular applications including Google Maps, Apple Maps, Citymapper, and Facebook. (7) Travel and movement behavior during epidemics may change in response to imposed interventions, perceived risk, and due to seasonal activities such as vacations (8, 9) . During the COVID-19 pandemic, mobility data has been used to assess adherence to movement restrictions (10, 11) , the impact of movement restrictions on the transmission dynamics of COVID-19 (12) (13) (14) , the socioeconomic impacts of large scale movement restrictions (15, 16) .
These are typically retrospective, describing past movement patterns to understand their impact, although the use of movement datasets to assist in developing policy responses to target key populations at risk during a disease outbreak is increasing (17) . Following the relaxation of nationwide restrictions in May 2020, the United Kingdom adopted a policy of targeted local interventions, aimed at reducing transmission in areas with resurgences, to avoid reimposing national restrictions (18) . However, the effectiveness of these measures will depend, amongst other things, on how the interconnections between areas change over time, and how 'local' areas are defined. We used mobility data from Facebook to investigate changes in travel behaviour in response to national and local policy changes. Large-scale movement datasets quantify these interconnections and could inform the appropriate geographic extent of control measures.
3 . CC-BY 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
The copyright holder for this preprint this version posted October 27, 2020. ; https://doi.org/10.1101/2020. 10.26.20219550 doi: medRxiv preprint / In this analysis, we used Facebook Data for Good UK movement and population data (March 19 to October 16 2020), which records approximately 15 million daily locations of 4.8 millio |
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